The DOI matches its Crossref record; 137 of the 140 references it cites were found in a registry or an index; 4 of the 221 sources its claims cite were checked for support.
Automated Visual Defect Classification for Flat Steel Surface: A Survey
Abstract
For a typical surface automated visual inspection (AVI) instrument of planar materials, defect classification is an indispensable part after defect detection, which acts as a crucial precondition for achieving the online quality inspection of end products. In the industrial environment of manufacturing flat steels, this task is awfully difficult due to diverse defect appearances, ambiguous intraclass, and interclass distances. This article attempts to present a focused but systematic review of the traditional and emerging automated computer-vision-based defect classification methods by investigating approximately 140 studies on three specific flat steel products of con-casting slabs, hot-rolled steel strips, and cold-rolled steel strips. According to the natural image processing procedure of defect recognition, the diverse approaches are grouped into five successive parts: image acquisition, image preprocessing, feature extraction, feature selection, and defect classifier. Recent literature has been reviewed from an industrial goal-oriented perspective to provide some guidelines for future studies and recommend suitable methods for boosting the surface quality inspection level of AVI instruments.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This survey reviews approximately 140 studies on automated visual defect classification for flat steel surfaces, covering continuous casting slabs, hot-rolled steel strips, and cold-rolled steel strips. It synthesizes traditional and deep-learning methods across five processing stages and identifies future directions such as multimode fusion, small-sample learning, and better time-cost evaluation.
Key findings
- The survey investigates approximately 140 studies and states that literature over the last ten years accounts for approximately 75% of the reviewed advancements.
- Methods are grouped into five successive components: image acquisition, image preprocessing, feature extraction, feature selection, and defect classifier.
- In feature selection, PCA is reported as the most frequently used dimension-reduction method, and GA is preferred for feature optimization; deep-learning methods such as CNN, CAE, and GAN are widely applied but often depend on labeled data.
- Table IV reports typical classification accuracies, including 99.50% for a deep CNN with majority voting on hot-rolled strips, 99.11% for generalized completed LBP + NNC on hot-rolled strips (266.74 ms per image), 97.5% for a SqueezeNet-based model (8.0 ms per image), and 98.12% for multiple support vector hypersphere with feature and sample weights (1.17 ms per image).
Methods
- Design: a focused systematic review of traditional and emerging computer-vision-based defect classification methods for flat steel, investigating approximately 140 studies on continuous casting slabs, hot-rolled steel strips, and cold-rolled steel strips.
- Organization: the review follows the natural image-processing procedure and groups approaches into image acquisition, image preprocessing, feature extraction, feature selection, and defect classifier.
- Analysis: it compares strengths and weaknesses of method families and includes a statistical list of typical methods with application, difficulty, performance, and classification time where available; no new experimental dataset is introduced.
Limitations
- Stated: most current classification methods emphasize accuracy and noise robustness while rarely involving time-cost evaluation, which matters for in-situ AVI on high-speed production lines.
- Stated: feature selection algorithms are generally identical and somewhat unitary in steel surface defect recognition, and the feature selection process is discounted; unsupervised learning results still show some disparity from the ultimate goal.
- Evident: as a survey, the paper reports no primary experiments, and many entries in Table IV have “T: NA,” limiting cross-study comparison of real-time suitability; its scope is also limited to three flat steel product types rather than all planar materials.
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Claims the paper makes
96 Not checked yet · 79 No basis given · 32 Own interpretation · 7 Sources not found · 1 Own result · accuracy not yet measured
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In the paper
“Throughout the literature we consulted, we found that the PCA is the most frequently used method to reduce the dimensions in the problem of strip steel surface defect classification because of its reliable performance.”
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In the paper
“Of these methods, the most popular algorithms applied to flat steel surface defect classification are PCA and LPP [112, 113].”
What the sources say
- Not checked yetcites [112] [112] A. Cord, F. Bach, and D. Jeulin, 'Texture classification by statistical learning from morphological image processing: application to metallic surfaces, ' J Microsc, vol. 239, no. 2, pp. 159-166, Aug. 2010. source
- Not checked yetcites [113] [113] M. Chu, A. Wang, R. Gong, and M. Sha, 'Multi-class classification methods of enhanced LS-TWSVM for strip steel surface defects,' J. Iron Steel Res. Int., vol. 21, no. 2, pp. 174-180, Feb. 2014. source
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In the paper
“In general, the algorithms used in the feature selection are generally identical and a little bit unitary in the topic of steel surface defect recognition.”
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“The statistical table reflects that most of the current classification methods place emphasis on classification accuracy and noise robustness while rarely involving the time cost evaluation of the designed classifiers.”
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In the paper
“Essentially, feature extraction and feature selection are dedicated to learning the temporal, spatial [13-15] and spectral features [16, 17] in images and even the intrinsic priors in the production line [18] to narrow the intraclass variation and expand the interclass distance.”
What the sources say
- Not checked yetcites [13] [13] L. Fang, S. Li, X. Kang, and J. A. Benediktsson, 'Spectral-spatial hyperspectral image classification via multiscale adaptive sparse representation,' IEEE Trans. Geosci. Remote Sensing, vol. 52, no. 12, pp. 7738-7749, Dec. 2014. - source
- Not checked yetcites [14] [14] L. Fang, C. Wang, S. Li, and J. A. Benediktsson, 'Hyperspectral image classification via multiple-feature-based adaptive sparse representation,' IEEE Trans. Instrum. Meas., vol. 66, no. 7, pp. 1646-1657, Jul. 2017. - source
- Not checked yetcites [15] [15] X. Kang, S. Li, and J. A. Benediktsson, 'Spectral-spatial hyperspectral image classification wth edge-preserving filtering,' IEEE Trans. Geosci. Remote Sensing, vol. 52, no. 5, pp. 2666-2677, May. 2014. - source
- Not checked yetcites [16] [16] L. He, J. Li, C. Liu, and S. Li, 'Recent advances on spectral-spatial hyperspectral image classification: An overview and new guidelines,' IEEE Trans. Geosci. Remote Sensing, vol. 56, no. 3, pp. 1579-1597, Mar. 2018. - source
- Not checked yetcites [17] [17] X. Kang, C. Li, S. Li, and H. Lin, 'Classification of hyperspectral images by gabor filtering based deep network,' IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., vol. 11, no. 4, pp. 1166-1178, Apr. 2018. - source
- Not checked yetcites [18] [18] J. Wang, Q. Li, J. Gan, H. Yu, and X. Yang, 'Surface defect detection via entity sparsity pursuit with intrinsic priors,' IEEE Trans. Ind. Inform., vol. 16, no. 1, pp. 141-150, Jan. 2020. - source
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In the paper
“Beyond that, it is important to note that the intensity imaging techniques still rely on the reflection property of the entire surface, so it is necessary to explore imaging methods that are less sensitive to the changes in reflection factors.”
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In the paper
“Combining these three illumination methods can greatly benefit defect detection and classification.”
What the sources say
- Not checked yetcites [5] [5] Q. Luo, and Y. He, 'A cost-effective and automatic surface defect inspection system for hot-rolled flat steel,' Robot. Comput.-Integr. Manuf., vol. 38, pp. 16-30, Apr. 2016. - source
- Not matchedcites [39] [39] R. Gong, C. Wu, M. Chu, and X. Liu, 'The strip steel surface defect recognition based on multiple support vector hyper-sphere with feature and sample weights,' Steel Res. Int., vol. 87, no. 12, pp. 1678-1685, Dec. 2016. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [40] [40] M. Chu, X. Liu, R. Gong, and L. Liu, 'Multi-class classification method using twin support vector machines with multi-information for steel surface defects,' Chemometrics Intell. Lab. Syst., vol. 176, pp. 108-118, May. 2018. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [41] [41] Y. Jeon, D. Choi, J. P. Yun, and S. W. Kim, 'Detection of periodic defects using dual-light switching lighting method on the surface of thick plates,' ISIJ Int., vol. 55, no. 9, pp. 1942-1949, 2015. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [42] [42] Y. J. Jeon, D. C. Choi, S. J. Lee, J. P. Yun, and S. W. Kim, 'Steel-surface defect detection using a switching-lighting scheme,' Appl. Optics, vol. 55, no. 1, pp. 47-57, Jan. 2016. - source
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In the paper
“the GF has optimal joint localization in both the spatial and spatial-frequency domains [62].”
What the sources say
- Not checked yetcites [62] [62] R. Medina, F. Gayubo, L. M. Gonzá lez-Rodrigo, D. Olmedo, J. Gó mez-Garcí a-Bermejo, E. Zalama, and J. R. Perán, 'Automated visual classification of frequent defects in flat steel coils,' Int. J. Adv. Manuf. Technol., vol. 57, no. 9-12, pp. 1087-1097, Dec. 2011. - source
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In the paper
“Operators such as Sobel and Roberts have difficulty detecting unclear and tiny defects due to their relatively small weighting factor.”
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In the paper
“Both GMGOCM and GLGOCM features fully take into account the scale and rotation invariance when extracting the steel defect feature [34].”
What the sources say
- Not matchedcites [34] [34] M. Chu, A. Wang, R. Gong, and M. Sha, 'Strip steel surface defect recognition based on novel feature extraction and enhanced least squares twin support vector machine,' ISIJ Int., vol. 54, no. 7, pp. 1638-1645, 2014. -
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In the paper
“To overcome the above challenge, the discrete Shearlet transform gray-level co-occurrence matrix (DST-GLCM) was designed in [92], which achieves outstanding classification rates on defects with high interclass similarity and high intraclass appearance variations.”
What the sources say
- Not checked yetcites [92] [92] M. W. Ashour, F. Khalid, A. Abdul Halin, L. N. Abdullah, and S. H. Darwish, 'Surface defects classification of hot-rolled steel strips using multi-directional Shearlet features,' Arab. J. Sci. Eng., vol. 44, no. 4, pp. 2925-2932, Apr. 2018. - source
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In the paper
“Has good characteristics in extracting the local space and frequency domain information of the target.”
What the sources say
- Not matchedcites [62] [62] R. Medina, F. Gayubo, L. M. Gonzá lez-Rodrigo, D. Olmedo, J. Gó mez-Garcí a-Bermejo, E. Zalama, and J. R. Perán, 'Automated visual classification of frequent defects in flat steel coils,' Int. J. Adv. Manuf. Technol., vol. 57, no. 9-12, pp. 1087-1097, Dec. 2011. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [63] [63] D. C. Choi, Y. J. Jeon, S. J. Lee, J. P. Yun, and S. W. Kim, 'Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method,' Appl. Optics, vol. 53, no. 22, pp. 4865-4872, Aug. 2014. - source
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In the paper
“Lack of localized signal analysis function.”
What the sources say
- Not checked yetcites [68] [68] K. Wiltschi, A. Pinz, and T. Lindeberg, 'An automatic assessment scheme for steel quality inspection,' Mach. Vis. Appl., vol. 12, no. 3, pp. 113-128, Oct. 2000. - source
- Not checked yetcites [96] [96] C. Dongyan, X. Kewen, N. Aslam, and H. Jingzhong, 'Defect classification recognition on strip steel surface using second-order cone programming-relevance vector machine algorithm,' J. Comput. Theor. Nanosci., vol. 13, no. 9, pp. 6141-6148, Step. 2016. - source
- Not checked yetcites [97] [97] Y. h. Ai, and K. Xu, 'Surface detection of continuous casting slabs based on curvelet transform and kernel locality preserving projections,' J. Iron Steel Res. Int., vol. 20, no. 5, pp. 80-86, May. 2013. - source
- Not checked yetcites [98] [98] G. Wu, H. Zhang, X. Sun, J. Xu, and K. Xu, 'A Bran-new feature extraction method and its application to surface defect recognition of hot rolled strips,' in IEEE Int. Conf. Autom. Logis., Jinan, China, Aug. 2007, pp. 2069-2074. - source
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In the paper
“Gabor and wavelet-based methods cannot tolerate local deformation well.”
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In the paper
“Deep-learning-based methods are also widely applied in the feature extraction of flat steel surface images, such as convolutional neural networks (CNNs), convolutional autoencoders (CAE) and generative adversarial networks (GANs).”
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Difficulty in choosing wavelet basis.”
What the sources say
- Not checked yetcites [4] [4] S. Ghorai, A. Mukherjee, M. Gangadaran, and P. K. Dutta, 'Automatic defect detection on hot-rolled flat steel products,' IEEE Trans. Instrum. Meas., vol. 62, no. 3, pp. 612-621, Mar. 2013. - source
- Not checked yetcites [61] [61] X. Wu, K. Xu, and J. Xu, 'Application of undecimated wavelet transform to surface defect detection of hot rolled steel plates,' in Int. Congress Image Signal Process., Sanya, China, May. 2008, pp. 528-532. - source
- Not checked yetcites [96] [96] C. Dongyan, X. Kewen, N. Aslam, and H. Jingzhong, 'Defect classification recognition on strip steel surface using second-order cone programming-relevance vector machine algorithm,' J. Comput. Theor. Nanosci., vol. 13, no. 9, pp. 6141-6148, Step. 2016. - source
- Not checked yetcites [99] [99] F. Pernkopf, 'Detection of surface defects on raw steel blocks using Bayesian network classifiers,' Pattern Anal. Appl., vol. 7, no. 3, pp. 333-342, Dec. 2004. - source
- Not checked yetcites [100] [100] K. Xu, Y.-h. Ai, and X.-y. Wu, 'Application of multi-scale feature extraction to surface defect classification of hot-rolled steels,' Int. J. Minerals, Metallurgy, Mater., vol. 20, no. 1, pp. 37-41, Jan. 2013. - source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“In addition, deep learning can quickly discover the deep-layer and discriminative feature representations from the training data [103].”
What the sources say
- Not checked yetcites [103] [103] S. Li, W. Song, L. Fang, Y. Chen, P. Ghamisi, and J. A. Benediktsson, 'Deep learning for hyperspectral image classification: An overview,' IEEE Trans. Geosci. Remote Sensing, vol. 57, no. 9, pp. 6690-6709, Sep. 2019. - source
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In the paper
“Cannot effectively retain the detail information of the original images.”
What the sources say
- Not checked yetcites [101] [101] K. Xu, S. Liu, and Y. Ai, 'Application of Shearlet transform to classification of surface defects for metals,' Image Vis. Comput., vol. 35, pp. 23-30, Mar 2015. - source
- Not checked yetcites [102] [102] X. Liu, K. Xu, P. Zhou, D. Zhou, and Y. Zhou, 'Surface defect identification of aluminium strips with non-subsampled shearlet transform,' Opt. Lasers Eng., vol. 127, pp. 1-9, Apr. 2020. - source
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In the paper
“Because all local parts share weights, it does not take into account the difference of contribution to the whole between each local part.”
What the sources say
- Not checked yetcites [104] [104] S. Zhou, Y. Chen, D. Zhang, J. Xie, and Y. Zhou, 'Classification of surface defects on steel sheet using convolutional neural networks,' Mater. Tehnol., vol. 51, no. 1, pp. 123-131, 2017. - source
- Not matchedcites [105] [105] J. Masci, U. Meier, G. Fricout, and J. Schmidhuber, 'Multi-scale pyramidal pooling network for generic steel defect classification,' in Int. Joint Conf. Neural Networks, Dallas, TX, USA 2013, pp. 1-8. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [106] [106] D. He, K. Xu, and P. Zhou, 'Defect detection of hot rolled steels with a new object detection framework called classification priority network,' Comput. Ind. Eng., vol. 128, pp. 290-297, Feb. 2019. - source
- Not matchedcites [107] [107] V. Natarajan, T.-Y. Hung, S. Vaikundam, L.-T. Chia, and Ieee, 'Convolutional networks for voting-based anomaly classification in metal surface inspection,' in IEEE Int. Conf. Ind. Technol., Toronto, CANADA, 2017, pp. 986-991. -
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plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Other effective algorithms are also applied, such as suboptimal feature selection algorithms, which have better results than simple sequential methods [68]”
What the sources say
- Not checked yetcites [68] [68] K. Wiltschi, A. Pinz, and T. Lindeberg, 'An automatic assessment scheme for steel quality inspection,' Mach. Vis. Appl., vol. 12, no. 3, pp. 113-128, Oct. 2000. - source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“For this reason, Chu et al . developed the Relief-F to solve the multiclass classification problem in feature optimization for strip steel surface defect recognition [39].”
What the sources say
- Not checked yetcites [39] [39] R. Gong, C. Wu, M. Chu, and X. Liu, 'The strip steel surface defect recognition based on multiple support vector hyper-sphere with feature and sample weights,' Steel Res. Int., vol. 87, no. 12, pp. 1678-1685, Dec. 2016. -
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In the paper
“Testing results prove that this scheme has improved the classification performance efficiently due to the discriminative information obtained from the reconstructive error or the sparse vector.”
What the sources say
- Not checked yetcites [114] [114] S. Zhou, Y. Chen, D. Zhang, J. Xie, and Y. Zhou, 'Learning a class-specific and shared dictionary for classifying surface defects of steel sheet,' ISIJ Int., vol. 57, no. 1, pp. 123-130, Nov. 2017. source
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In the paper
“Notably, Chu et al. made great contributions to surface defect classification based on a multiclass SVM, by improving the historic problems of imbalanced training samples [120] and noise robustness [45].”
What the sources say
- Not matchedcites [120] [120] M. Chu, X. Liu, R. Gong, and J. Zhao, 'Multi-class classification method for strip steel surface defects based on support vector machine with adjustable hyper-sphere,' J. Iron Steel Res. Int., vol. 25, no. 7, pp. 706-716, Jul. 2018.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [45] [45] R. Gong, M. Chu, Y. Yang, and Y. Feng, 'A multi-class classifier based on support vector hyper-spheres for steel plate surface defects,' Chemometrics Intell. Lab. Syst., vol. 188, pp. 70-78, May. 2019. - source
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In the paper
“In [104] and [105], a CNN was adopted as a classifier for the defect classification of flat steel surfaces, and the experimental results indicate that the CNN is powerful and robust for the classification task.”
What the sources say
- Not checked yetcites [104] [104] S. Zhou, Y. Chen, D. Zhang, J. Xie, and Y. Zhou, 'Classification of surface defects on steel sheet using convolutional neural networks,' Mater. Tehnol., vol. 51, no. 1, pp. 123-131, 2017. - source
- Not matchedcites [105] [105] J. Masci, U. Meier, G. Fricout, and J. Schmidhuber, 'Multi-scale pyramidal pooling network for generic steel defect classification,' in Int. Joint Conf. Neural Networks, Dallas, TX, USA 2013, pp. 1-8. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“For example, Masci et al. [105] emulated a standard dictionary-based encoding strategy as an encoding layer to improve the recognition rate for generic steel defects.”
What the sources say
- Not matchedcites [105] [105] J. Masci, U. Meier, G. Fricout, and J. Schmidhuber, 'Multi-scale pyramidal pooling network for generic steel defect classification,' in Int. Joint Conf. Neural Networks, Dallas, TX, USA 2013, pp. 1-8. -
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plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“These application cases show that the SOM and its variants are promising in surface defect classification in the steel manufacturing industry.”
What the sources say
- Not checked yetcites [127] [127] G. W. Kang, and H. B. Liu, 'Surface defects inspection of cold rolled strips based on neural network,' in 4th Int. Conf. Machine Learning Cybernetics, Canton, PEOPLES R CHINA, 2005, pp. 5034-5037.
- Not checked yetcites [50] [50] L. A. O. Martins, F. L. C. Padua, and P. E. M. Almeida, 'Automatic detection of surface defects on rolled steel using computer vision and artificial neural networks,' in 36th Annual Conf. IEEE Ind. Electron., Glendale, AZ, USA, 2010, pp. 1081-1086. -
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In the paper
“This method has achieved satisfactory classification accuracy in the surface defect classification of flat steel [62, 115].”
What the sources say
- Not matchedcites [62] [62] R. Medina, F. Gayubo, L. M. Gonzá lez-Rodrigo, D. Olmedo, J. Gó mez-Garcí a-Bermejo, E. Zalama, and J. R. Perán, 'Automated visual classification of frequent defects in flat steel coils,' Int. J. Adv. Manuf. Technol., vol. 57, no. 9-12, pp. 1087-1097, Dec. 2011. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [115] [115] X.-y. Zhao, K.-s. La, and D.-m. Dai, 'An improved BP algorithm and its application in classification of surface defects of steel plate,' J. Iron Steel Res. Int., vol. 14, no. 2, pp. 52-55, 2007.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Not matched
In the paper
“For the multiclass classification problem, Zhang et al. [59] succeeded in identifying seven classes of steel surface defects effectively based on a multiclass SVM by simultaneously optimizing the kernel function selection and parameter settings of the traditional SVM method.”
What the sources say
- Not checked yetcites [59] [59] X. Zhang, Y. Ding, Y. Lv, A. Shi, and R. Liang, 'A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM,' Expert Syst. Appl., vol. 38, no. 5, pp. 5930-5939, May. 2011. - source
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“In addition to the above classifiers, some other classifiers have also achieved good effects under supervised learning for the task of flat steel surface defect classification, such as decision trees, random forests, and relevance vector machines (RVMs).”
What the sources say
- Not checked yetcites [110] [110] S. Lechwar, Ł. Rauch, and M. Pietrzyk, 'Use of Artificial Intelligence in Classification of Mill Scale Defects,' Steel Res. Int., vol. 86, no. 3, pp. 266-277, Mar. 2015. - source
- Not checked yetcites [125] [125] T. Zhang, D. Xia, H. Tang, X. Yang, and H. Li, 'Classification of steel samples by laser-induced breakdown spectroscopy and random forest,' Chemometrics Intell. Lab. Syst., vol. 157, pp. 196-201, Oct. 2016. source
- Not matchedcites [126] [126] H. Jingzhong, X. Kewen, Y. Fan, and Z. Baokai, 'Strip steel surface defects recognition based on SOCP optimized multiple kernel RVM,' Math. Probl. Eng., vol. 2018, pp. 1-8, 2018.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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None of the sources cited for this claim have been checked yet.
In the paper
“AVI systems relying on multi-classifiers can achieve better classification performance and robustness than those based on individual classifiers.”
What the sources say
- Not checked yetcites [62] [62] R. Medina, F. Gayubo, L. M. Gonzá lez-Rodrigo, D. Olmedo, J. Gó mez-Garcí a-Bermejo, E. Zalama, and J. R. Perán, 'Automated visual classification of frequent defects in flat steel coils,' Int. J. Adv. Manuf. Technol., vol. 57, no. 9-12, pp. 1087-1097, Dec. 2011. - source
- Not matchedcites [122] [122] Y. Zhang, W. Liu, Z. Xing, and Y. Yan, 'Surface defect recognition for steel strips by combining multiple classifiers,' J. Northeastern University (Natural Sci.), vol. 33, no. 2, pp. 267-270, Feb. 2012.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [123] [123] Y. Yunhui, S. Kechen, X. Zhitao, and F. Xuehui, 'The strip steel surface defects classification method based on weak classifier adaptive enhancement,' in Third IEEE Int.Conf. Meas Tech Mech Auto, Shangshai, China, 2011, pp. 958-961. source
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Deep learning methods such as CNN, GAN, and AutoEncoder have nurtured many novel ideas that have achieved outstanding performance in both feature learning and defect recognition for flat steel surfaces.”
What the sources say
- Not checked yetcites [104] [104] S. Zhou, Y. Chen, D. Zhang, J. Xie, and Y. Zhou, 'Classification of surface defects on steel sheet using convolutional neural networks,' Mater. Tehnol., vol. 51, no. 1, pp. 123-131, 2017. - source
- Not matchedcites [107] [107] V. Natarajan, T.-Y. Hung, S. Vaikundam, L.-T. Chia, and Ieee, 'Convolutional networks for voting-based anomaly classification in metal surface inspection,' in IEEE Int. Conf. Ind. Technol., Toronto, CANADA, 2017, pp. 986-991. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [108] [108] D. He, K. Xu, P. Zhou, and D. Zhou, 'Surface defect classification of steels with a new semi-supervised learning method,' Opt. Lasers Eng., vol. 117, pp. 40-48, Feb. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosteddirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“GANs are often utilized to generate labelled defective images based on the existing limited labelled samples and sufficient unlabelled samples from industrial sites.”
What the sources say
- Not checked yetcites [132] [132] J. Lian, W. Jia, M. Zareapoor, Y. Zheng, R. Luo, D. K. Jain, and N. Kumar, 'Deep-learning-based small surface defect detection via an exaggerated local variation-based generative adversarial network,' IEEE Trans. Ind. Inform., vol. 16, no. 2, pp. 1343-1351, Feb. 2020. source
- Not checked yetcites [133] [133] S. Niu, B. Li, X. Wang, and H. Lin, 'Defect image sample generation with GAN for improving defect recognition,' IEEE Trans. Autom. Sci. Eng., vol. 17, no. 3, pp. 1611-1622, Jul. 2020. source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- It may state two separate claims as one. 109
- Probably not a substantial claim. 146
- The quote only partly states this claim. 1
None of the sources cited for this claim have been checked yet.
In the paper
“Sensitive to scale variation and noise interference.”
What the sources say
- Not checked yetcites [1] [1] K. Song, and Y. Yan, 'A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects,' Appl. Surf. Sci., vol. 285, pp. 858-864, Sep. 2013. - source
- Not matchedcites [10] [10] Q. Luo, X. Fang, Y. Sun, L. Liu, J. Ai, C. Yang, and O. Simpson, 'Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns,' IEEE Access, vol. 7, pp. 23488-23499, Feb. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [11] [11] M. Chu, R. Gong, S. Gao, and J. Zhao, 'Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine,' Chemometrics Intell. Lab. Syst., vol. 171, pp. 140-150, Dec. 2017. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [18] [18] J. Wang, Q. Li, J. Gan, H. Yu, and X. Yang, 'Surface defect detection via entity sparsity pursuit with intrinsic priors,' IEEE Trans. Ind. Inform., vol. 16, no. 1, pp. 141-150, Jan. 2020. - source
- Could not verifycites [81] [81] M. Xiao, M. Jiang, G. Li, L. Xie, and L. Yi, 'An evolutionary classifier for steel surface defects with small sample set,' EURASIP J. Image Video Process., vol. 2017, no. 1, pp. 1-13, Jul. 2017. - sourceExperimental — accuracy not yet measured
Section 3.3 describes LBP and its gray-scale and rotation invariance, but does not discuss sensitivity to scale variation or noise. Sections 6.1–6.2 examine noise effects on classifiers, not on LBP itself. Searches for LBP scale and noise sensitivity found no passage addressing the claim. (LLM, conf=0.74)
- Not checked yetcites [82] [82] S. R. Aghdam, E. Amid, and M. F. Imani, 'A fast method of steel surface defect detection using decision trees applied to LBP based features, ' in 7th IEEE Conf. Ind. Electron. Appl., Singapore, Singapore, 2012, pp. 1447-1452. - source
- Could not verifycites [83] [83] S. Tian, and K. Xu, 'An algorithm for surface defect identification of steel plates based on genetic algorithm and extreme learning machine,' Metals, vol. 7, no. 8, pp. 1-11, Aug. 2017. - sourceExperimental — accuracy not yet measured
Section 5 (Experiments; page unavailable) says the study uses the original Local Binary Pattern operator for feature extraction, but does not assess its sensitivity to scale variation or noise. Searches for those limitations found no passage in our copy that addresses them. (LLM, conf=0.73)
- Not checked yetcites [84] [84] R. Ren, T. Hung, and K. C. Tan, 'A generic deep-learning-based approach for automated surface inspection,' IEEE Trans. Cybern., vol. 48, no. 3, pp. 929-940, Mar. 2018. - source
- Could not verifycites [85] [85] X. Tao, D. Zhang, W. Ma, X. Liu, and D. Xu, 'Automatic metallic surface defect detection and recognition with convolutional neural networks,' Appl. Sci.-Basel, vol. 8, no. 9, pp. 1-15, Sep. 2018. - sourceExperimental — accuracy not yet measured
The Introduction (p. 2) lists local binary patterns among handcrafted features and discusses limitations of traditional methods, but does not say LBP is sensitive to scale variation or noise. Searches for LBP with scale variation and noise found no passage making that specific claim; the noise discussion in §5.2 (p. 9) concerns thresholding methods instead. (LLM, conf=0.76)
- Could not verifycites [86] [86] Y. Liu, K. Xu, and D. Wang, 'Online surface defect identification of cold rolled strips based on local binary pattern and extreme learning machine,' Metals, vol. 8, no. 3, pp. 1-18, Mar. 2018. - sourceExperimental — accuracy not yet measured
Section 2.1.1 describes how LBP features are computed, and the Introduction compares LBP’s computational complexity with SIFT and SURF. Neither passage addresses sensitivity to scale variation or noise interference; searches for those terms and paraphrases found no supporting passage in our copy. (LLM, conf=0.72)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In particular, the literature over the last ten years accounts for approximately 75% of the abovementioned advancements.”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“According to the review, we can find that studies have a preference for GA when considering steel feature optimization, which is mainly because there are few superior methods for query.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The adequacy of evaluation approaches existing in the literature can be categorized into three groups, namely, filter method , wrapper method and embedding method .”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Deep learning can make full use of the advantages of big data to automatically learn the multilayer depiction of features, which can involve thousands of features, and the expression ability of these learned features is stronger than that of manually designed features.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“After evaluation, the PCA is mostly used to reduce dimensions, and almost all studies choose the GA for feature optimization; both are traditional and reliable methods.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The most commonly used classifiers in supervised learning are neural networks (NN), support vector machines (SVM), distance functions, dictionary learning, sparse representation and multi-classifier fusion.”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Any surface defects not treated in time will threaten the steel product quality, which might cause substantial economic and reputation cost to both the steel manufacturers and end customers [1-5].”
What the sources say
- Not checked yetcites [1] [1] K. Song, and Y. Yan, 'A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects,' Appl. Surf. Sci., vol. 285, pp. 858-864, Sep. 2013. -
- Not checked yetcites [2] [2] K. Song, and Y. Yan, 'Micro surface defect detection method for silicon steel strip based on saliency convex active Contour model,' Math. Probl. Eng., vol. 2013, pp. 1-13, Nov. 2013. -
- Not checked yetcites [3] [3] K. Song, S. Hu, Y. Yan, and J. Li, 'Surface defect detection method using saliency linear scanning morphology for silicon steel strip under oil pollution interference,' ISIJ Int., vol. 54, no. 11, pp. 2598-2607, Jul. 2014. -
- Not checked yetcites [4] [4] S. Ghorai, A. Mukherjee, M. Gangadaran, and P. K. Dutta, 'Automatic defect detection on hot-rolled flat steel products,' IEEE Trans. Instrum. Meas., vol. 62, no. 3, pp. 612-621, Mar. 2013. -
- Not checked yetcites [5] [5] Q. Luo, and Y. He, 'A cost-effective and automatic surface defect inspection system for hot-rolled flat steel,' Robot. Comput.-Integr. Manuf., vol. 38, pp. 16-30, Apr. 2016. -
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In the industrial environment of manufacturing flat steels, this task is awfully difficult due to diverse defect appearances, ambiguous intraclass and interclass distances.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“For a typical surface automated visual inspection (AVI) instrument of planar materials, defect classification is an indispensable part after defect detection, which acts as a crucial precondition for achieving the on-line quality inspection of end products.”
What the sources say
- Not checked yetcites [5] [5] Q. Luo, and Y. He, 'A cost-effective and automatic surface defect inspection system for hot-rolled flat steel,' Robot. Comput.-Integr. Manuf., vol. 38, pp. 16-30, Apr. 2016. -
- Not checked yetcites [10] [10] Q. Luo, X. Fang, Y. Sun, L. Liu, J. Ai, C. Yang, and O. Simpson, 'Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns,' IEEE Access, vol. 7, pp. 23488-23499, Feb. 2019. -
- Not checked yetcites [11] [11] M. Chu, R. Gong, S. Gao, and J. Zhao, 'Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine,' Chemometrics Intell. Lab. Syst., vol. 171, pp. 140-150, Dec. 2017. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“A typical AVI instrument mainly realizes the two fundamental functions of defect detection and classification, whose results are used to adjust the relevant configurations of the production line to guarantee the quality of final steel product [5, 10, 11].”
What the sources say
- Not checked yetcites [5] [5] Q. Luo, and Y. He, 'A cost-effective and automatic surface defect inspection system for hot-rolled flat steel,' Robot. Comput.-Integr. Manuf., vol. 38, pp. 16-30, Apr. 2016. -
- Not checked yetcites [10] [10] Q. Luo, X. Fang, Y. Sun, L. Liu, J. Ai, C. Yang, and O. Simpson, 'Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns,' IEEE Access, vol. 7, pp. 23488-23499, Feb. 2019. -
- Not checked yetcites [11] [11] M. Chu, R. Gong, S. Gao, and J. Zhao, 'Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine,' Chemometrics Intell. Lab. Syst., vol. 171, pp. 140-150, Dec. 2017. -
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Fig. 2 (b) shows eighteen types of image samples of hot-rolled steel strips, because the surfaces usually covered with many scales, which increase the possibility of misclassifying other types of defects.”
plaindirectn = 18- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The most critical goal of defect classification for continuous casting slabs is to distinguish cracks from the other three similar pseudo-defects.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared with defect detection, which mainly affects the time-efficiency and missed detection rate of the AVI instrument, defect classification directly determines its final user experience, as the cognitive performance of defect patterns represents the troubleshooting ability of the AVI instrument.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Among them, it is proved that the median filters have the best performance on the suppression of salt and pepper noise, and the Gaussian filter and Wiener filter have better effects on Gaussian noise.”
What the sources say
- Not checked yetcites [46] [46] A. Landstrom, and M. J. Thurley, 'Morphology-based crack detection for steel slabs,' IEEE J. Sel. Top. Signal Process., vol. 6, no. 7, pp. 866-875, Nov. 2012. - source
- Not checked yetcites [47] [47] S. G. Ryu, D. c. Choi, Y. J. Jeon, S. J. Lee, J. P. Yun, and S. W. Kim, 'Detection of scarfing faults on the edges of slabs,' ISIJ Int., vol. 54, no. 1, pp. 112-118, 2014. - source
- Not checked yetcites [48] [48] F. G. Bulnes, D. F. Garcia, F. Javier De la Calle, R. Usamentiaga, and J. Molleda, 'A Non-invasive technique for online defect detection on steel strip surfaces,' J. Nondestruct. Eval., vol. 35, no. 4, pp. 1-18, Dec. 2016. - source
- Not checked yetcites [49] [49] Y. Liu, Y. Hsu, Y. Sun, S. Tsai, C. Ho, and C. Chen, 'A computer vision system for automatic steel surface inspection,' in 2010 5th IEEE Conf. Ind. Electron. Appl., Taichung, Taiwan, 2010, pp. 1667-1670. - source
- Not checked yetcites [50] [50] L. A. O. Martins, F. L. C. Padua, and P. E. M. Almeida, 'Automatic detection of surface defects on rolled steel using computer vision and artificial neural networks,' in 36th Annual Conf. IEEE Ind. Electron., Glendale, AZ, USA, 2010, pp. 1081-1086. - source
- Not checked yetcites [51] [51] Y. Wang, H. Xia, X. Yuan, L. Ling, and S. Bei, 'Distributed defect recognition on steel surfaces using an improved random forest algorithm with optimal multi-feature-set fusion,' Multimed. Tools Appl., vol. 77, no. 13, pp. 16741-16770, Jul. 2017. - source
- Not matchedcites [52] [52] T. Bo, J. Kong, X. Wang, and C. Li, 'Surface inspection system of steel strip based on machine vision,' in 1st Int. Workshop. Database Technol. Appl., Wuhan, Hubei, China, Apr. 2009, pp. 359-362. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [53] [53] Y. Chen, L. Chen, X. Liu, S. Ding, and H. Zhang, 'Real-time steel inspection system based on support vector machine and multiple kernel learning,' in 6th Int. Conf. Intell. Syst. Knowl. Eng., Shanghai, PEOPLES R CHINA, 2011, pp. 185-190. - source
- Not matchedcites [54] [54] R. Gong, C. Wu, and M. Chu, 'Steel surface defect classification using multiple hyper-spheres support vector machine with additional information,' Chemometrics Intell. Lab. Syst., vol. 172, pp. 109-117, Jan. 2018. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [55] [55] M. Ricci, A. Ficola, M. L. Fravolini, L. Battaglini, A. Palazzi, P. Burrascano, P. Valigi, L. Appolloni, S. Cervo, and C. Rocchi, 'Magnetic imaging and machine vision NDT for the on-line inspection of stainless steel strips,' Meas. Sci. Technol., vol. 24, no. 2, pp. 1-17, Feb, 2013. - source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Because the global gray value of steel surface images is ordinarily low but the dynamic range is large, the effect of histogram equalization is not as good as that of homomorphic filtering.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Weak correlation information and strong interference information influence the reliability of feature extraction and defect classification.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Generally, the challenges that image acquisition faces are the greatest in all parts of flat steel surface defect classification.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, cracks are challenging to detect because of the interference of scales, slag marks, and uneven illumination, which are similar to the appearance of cracks.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Intensity imaging technology is largely dependent on the illumination condition, which is the most vulnerable to interference in production.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Because the former is limited to surface defects with three-dimensional properties, e.g. cavities, scratches, and nicks, and it is not competitive with the latter in terms of spatial resolution and acquisition speed, intensity imaging is most commonly used in real-world flat steel production lines.”
What the sources say
- Not checked yetcites [32] [32] F. Pernkopf, and P. O'Leary, 'Image acquisition techniques for automatic visual inspection of metallic surfaces,' NDT E Int., vol. 36, no. 8, pp. 609-617, Dec. 2003. -
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“However, these surveys are insufficiently updated, and the methods involved do not reflect the latest level of algorithms in contemporary development.”
What the sources say
- Not checked yetcites [19] [19] R. T. Chin, and C. A. Harlow, 'Automated visual inspection: a survey,' IEEE Trans. Pattern Anal. Mach. Intell., vol. 4, no. 6, pp. 557-573, Dec. 1982. - source
- Not checked yetcites [20] [20] T. S. Newman, and A. K. Jain, 'A survey of automated visual inspection,' Comput. Vis. Image Underst., vol. 61, no. 2, pp. 231-262, Mar. 1995. - source
- Not checked yetcites [21] [21] X. Xie, 'A review of recent advances in surface defect detection using texture analysis techniques,' Electron. Lett. Comput. Vis. Image Anal., vol. 7, no. 3, pp. 1-22, 2008. - source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The resource-saving algorithm of dynamic homogenizing compensation (DHC) [5] explores the intrinsic prior that the image intensity is varying actively but horizontal intensity distribution is extremely stable, which helped the DHC algorithm win a competition against other image enhancement methods.”
What the sources say
- Not checked yetcites [5] [5] Q. Luo, and Y. He, 'A cost-effective and automatic surface defect inspection system for hot-rolled flat steel,' Robot. Comput.-Integr. Manuf., vol. 38, pp. 16-30, Apr. 2016. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Fortunately, advanced imaging techniques and emerging machine learning methods jointly resist the challenges of unsatisfactory imaging environments and quasi real-time requirements [5], forcefully driving the progress of defect recognition, especially in learning classification [8].”
What the sources say
- Not checked yetcites [5] [5] Q. Luo, and Y. He, 'A cost-effective and automatic surface defect inspection system for hot-rolled flat steel,' Robot. Comput.-Integr. Manuf., vol. 38, pp. 16-30, Apr. 2016. - source
- Not checked yetcites [8] [8] L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu, and M. Pietikainen, 'Deep learning for generic object detection: A survey,' Int. J. Comput. Vis., vol. 128, no. 2, pp. 261-318, Feb. 2020. - source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“However, these surveys paid more attention to the detection methods, and they simply summarized the research progress of the classification methods through supervised and unsupervised classifiers.”
What the sources say
- Not checked yetcites [29] [29] N. Neogi, D. K. Mohanta, and P. K. Dutta, 'Review of vision-based steel surface inspection systems,' EURASIP J. Image Video Process., vol. 2014, no. 1, pp. 1-19, Nov. 2014. - source
- Not checked yetcites [30] [30] X. Sun, J. Gu, S. Tang, and J. Li, 'Research progress of visual inspection technology of steel products-A review,' Appl. Sci.-Basel, vol. 8, no. 11, pp. 1-25, Nov, 2018. - source
- Not checked yetcites [31] [31] T. Czimmermann, G. Ciuti, M. Milazzo, M. Chiurazzi, S. Roccella, C. M. Oddo, and P. Dario, 'Visual-based defect detection and classification approaches for industrial applications-a survey,' Sensors, vol. 20, no. 5, pp. 1-25, Mar, 2020. - source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Image acquisition occurs during the first step of defect classification, and the quality of the acquired images directly affects the performance of the subsequent processing.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The surface quality of the cold-rolled strip is usually better than the other two flat steel. Therefore, the number, size and degree of defects are also the most demanding.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Wavelet smoothing can sharpen an object's edges and restrain noise; moreover, it can enhance the positioning precision of the edges and the depiction of the images.”
What the sources say
- Not checked yetcites [58] [58] Y. Xu, J. B. Weaver, D. M. Healy, and J. Lu, 'Wavelet transform domain filters: a spatially selective noise filtration technique,' IEEE Trans. Image Process., vol. 3, no. 6, pp. 747-758, Nov. 1994. - source
- Not checked yetcites [59] [59] X. Zhang, Y. Ding, Y. Lv, A. Shi, and R. Liang, 'A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM,' Expert Syst. Appl., vol. 38, no. 5, pp. 5930-5939, May. 2011. - source
- Not matchedcites [60] [60] C. S. Lee, C. H. Choi, J. Y. Choi, Y. K. Kim, and S. H. Choi, 'Feature extraction algorithm based on adaptive wavelet packet for surface defect classification,' in Int. Conf. Image Processing LAUSANNE, SWITZERLAND 1996, pp. 673-676. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [61] [61] X. Wu, K. Xu, and J. Xu, 'Application of undecimated wavelet transform to surface defect detection of hot rolled steel plates,' in Int. Congress Image Signal Process., Sanya, China, May. 2008, pp. 528-532. - source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In addition to the performance of the classifiers, the characteristic of the extracted features is another important factor influencing the precision of classification methods.”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, a single filter may not only smooth the image but also miss the details of the image.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Among them, LED is the most widely used luminaire in machine vision applications due to its advantages of longevity and low heat production [35, 37, 38].”
What the sources say
- Not matchedcites [35] [35] K. Liu, H. Wang, H. Chen, E. Qu, Y. Tian, and H. Sun, 'Steel surface defect detection using a new Haar-Weibull-variance model in unsupervised manner,' IEEE Trans. Instrum. Meas., vol. 66, no. 10, pp. 2585-2596, Jul. 2017. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [37] [37] H. Zheng, L. X. Kong, and S. Nahavandi, 'Automatic inspection of metallic surface defects using genetic algorithms,' in 9th Internet. Conf. Metal Forming, UNIV BRIMINGHAM, BIRMINGHAM, ENGLAND, Sep. 2002, pp. 427-433. - source
- Not checked yetcites [38] [38] Q. Li, and S. Ren, 'A Real-time visual inspection system for discrete surface defects of rail heads,' IEEE Trans. Instrum. Meas., vol. 61, no. 8, pp. 2189-2199, Aug. 2012. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“The optimized filters have been very effective not only in filtering mixed noise, but also in maintaining edge details.”
What the sources say
- Not checked yetcites [39] [39] R. Gong, C. Wu, M. Chu, and X. Liu, 'The strip steel surface defect recognition based on multiple support vector hyper-sphere with feature and sample weights,' Steel Res. Int., vol. 87, no. 12, pp. 1678-1685, Dec. 2016. - source
- Not checked yetcites [57] [57] M. Chu, A. Wang, and R. Gong, 'An image filtering algorithm for removing mixed noise,' in 2nd Int. Conf. Mechatronics Control Eng., Dalian, PEOPLES R CHINA 2013, pp. 383-388. - source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“the Fourier amplitude spectrum is translational invariant and is often used to assess the directional information of carbide distribution images [68].”
What the sources say
- Not checked yetcites [68] [68] K. Wiltschi, A. Pinz, and T. Lindeberg, 'An automatic assessment scheme for steel quality inspection,' Mach. Vis. Appl., vol. 12, no. 3, pp. 113-128, Oct. 2000. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, it is sensitive to rotation, which could bring about information redundancy.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Edge-based operators can locate edges accurately but cannot guarantee the continuity and closure of edges.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“the PHOG has an excellent feature description ability and was applied to describe steel texture features.”
What the sources say
- Not checked yetcites [94] [94] J. Masci, U. Meier, D. Ciresan, J. Schmidhuber, and G. Fricout, 'Steel defect classification with max-pooling convolutional neural networks,' in 2012 Int. Joint Conf. Neural Network, Brisbane, AUSTRALIA, 2012, pp. 1-6. -
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Luo et al. innovatively extracted the forgotten useful information hidden in nonuniform patterns in [10, 90], which not only improves the accuracy and calculation time simultaneously but also has better robustness to noise.”
What the sources say
- Not checked yetcites [10] [10] Q. Luo, X. Fang, Y. Sun, L. Liu, J. Ai, C. Yang, and O. Simpson, 'Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns,' IEEE Access, vol. 7, pp. 23488-23499, Feb. 2019. -
- Not checked yetcites [90] [90] Q. Luo, Y. Sun, P. Li, O. Simpson, L. Tian, and Y. He, 'Generalized completed local binary patterns for time-efficient steel surface defect classification,' IEEE Trans. Instrum. Meas., vol. 68, no. 3, pp. 667-679, Mar. 2019. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The watershed algorithm is able to detect weak edges but is still sensitive to noise.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The Laplacian operator is of poor robust to noise and has a large amount of calculation.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The FT only depicts the spatial-frequency distribution without regard to the spatial domain information.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, LBP, GLCM and HOG are the three reliable methods most widely used in steel due to their adaptability and robustness.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Otsu is a classical adaptive threshold method for separating defects from the background in flat steel images [59, 69, 70], which obtains a threshold value based on the characteristic of the large variance between the background and foreground.”
What the sources say
- Not checked yetcites [59] [59] X. Zhang, Y. Ding, Y. Lv, A. Shi, and R. Liang, 'A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM,' Expert Syst. Appl., vol. 38, no. 5, pp. 5930-5939, May. 2011. - source
- Not checked yetcites [69] [69] M. R. Yazdchi, A. G. Mahyari, and A. Nazeri, 'Detection and classification of surface defects of cold rolling mill steel using morphology and neural network,' in Int. Conf. Comput. Intell. for Modelling, Control and Automation, Vienna, AUSTRIA, 2008, pp. 1071-1076. - source
- Not checked yetcites [70] [70] M. Win, A. R. Bushroa, M. A. Hassan, N. M. Hilman, and A. Ide-Ektessabi, 'A contrast adjustment thresholding method for surface defect detection based on mesoscopy,' IEEE Trans. Ind. Informat., vol. 11, no. 3, pp. 642-649, Jun. 2015. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The grayscale of steel strip images is ordinarily nonuniform, the gray value variation cross the background and the defect is sometimes gradual, and the size of defect area is very small, not easy to be recognized by the computer.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The watershed algorithm can well detect weak edges, even though the noise in an image may cause over-segmentation, it can still ensure a closed continuous edge.”
What the sources say
- Not checked yetcites [45] [45] R. Gong, M. Chu, Y. Yang, and Y. Feng, 'A multi-class classifier based on support vector hyper-spheres for steel plate surface defects,' Chemometrics Intell. Lab. Syst., vol. 188, pp. 70-78, May. 2019. - source
- Not matchedcites [54] [54] R. Gong, C. Wu, and M. Chu, 'Steel surface defect classification using multiple hyper-spheres support vector machine with additional information,' Chemometrics Intell. Lab. Syst., vol. 172, pp. 109-117, Jan. 2018. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Kirsch operator has a good effect in maintaining details and anti-noise, but it depends on the edge direction and cannot guarantee the continuity and closure of the edge, which make it difficult to form a large region.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although this method is simple and efficient, in many cases, the contrast between the defect and the background is not the same everywhere in the image, so it is challenging to separate the abnormal area and the background with a unified threshold.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Choi et al. [63] combined two Gabor filters to detect seam cracks on steel plates, which offers high detection performance and the effective reduction of noise components.”
What the sources say
- Not checked yetcites [63] [63] D. C. Choi, Y. J. Jeon, S. J. Lee, J. P. Yun, and S. W. Kim, 'Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method,' Appl. Optics, vol. 53, no. 22, pp. 4865-4872, Aug. 2014. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Many works suggest that the features in the spatial-frequency domain show stronger robust to noise and intensity variation than in the spatial domain, and transform features have been reported to be useful in improving the representation of spectral data and increasing the classification accuracy [16].”
What the sources say
- Not checked yetcites [16] [16] L. He, J. Li, C. Liu, and S. Li, 'Recent advances on spectral-spatial hyperspectral image classification: An overview and new guidelines,' IEEE Trans. Geosci. Remote Sensing, vol. 56, no. 3, pp. 1579-1597, Mar. 2018. - source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“These two methods are both robust to noise to a large extent.”
What the sources say
- Not checked yetcites [1] [1] K. Song, and Y. Yan, 'A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects,' Appl. Surf. Sci., vol. 285, pp. 858-864, Sep. 2013. - source
- Not matchedcites [88] [88] M. Chu, and R. Gong, 'Invariant feature extraction method based on smoothed local binary pattern for strip steel surface defect,' ISIJ Int., vol. 55, no. 9, pp. 1956-1962, May 2015. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“FT can reflect the local outstanding feature information, which is widely used in the extraction of features of steel surface images [96, 97].”
What the sources say
- Not checked yetcites [96] [96] C. Dongyan, X. Kewen, N. Aslam, and H. Jingzhong, 'Defect classification recognition on strip steel surface using second-order cone programming-relevance vector machine algorithm,' J. Comput. Theor. Nanosci., vol. 13, no. 9, pp. 6141-6148, Step. 2016. - source
- Not checked yetcites [97] [97] Y. h. Ai, and K. Xu, 'Surface detection of continuous casting slabs based on curvelet transform and kernel locality preserving projections,' J. Iron Steel Res. Int., vol. 20, no. 5, pp. 80-86, May. 2013. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Wu et al. [98] first obtained an original feature set by the FFT, and then , two extended features (Sum of Valid Pixels (SVP) and Repletion Ration of Center Region (RRCR)) were introduced to reflect the global and local statistic feature information, respectively, which excavated more deep information from the spectrum images.”
What the sources say
- Not checked yetcites [98] [98] G. Wu, H. Zhang, X. Sun, J. Xu, and K. Xu, 'A Bran-new feature extraction method and its application to surface defect recognition of hot rolled strips,' in IEEE Int. Conf. Autom. Logis., Jinan, China, Aug. 2007, pp. 2069-2074. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, since GLCM and its variants are merely describe spatial features, they cannot reflect the highor low-frequency components of the multidirectional defects on a surface, while steel strip surface defects vary widely (various types, shapes and orientations).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In contrast, threshold-based methods are simple and efficient, but the selection of the optimal threshold value is usually labor-intensive.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The 2D Gabor function contains real and imaginary parts. The former can be utilized to detect blob, and the latter can be used to detect edge.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The GF can be obtained by superimposing a trigonometric function and a Gaussian function; which is an effective texture detection tool to extract the features of a specific of specific scale and orientation.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Be of rotation and gray invariance, fast calculation speed.”
What the sources say
- Not checked yetcites [1] [1] K. Song, and Y. Yan, 'A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects,' Appl. Surf. Sci., vol. 285, pp. 858-864, Sep. 2013. - source
- Not matchedcites [10] [10] Q. Luo, X. Fang, Y. Sun, L. Liu, J. Ai, C. Yang, and O. Simpson, 'Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns,' IEEE Access, vol. 7, pp. 23488-23499, Feb. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [11] [11] M. Chu, R. Gong, S. Gao, and J. Zhao, 'Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine,' Chemometrics Intell. Lab. Syst., vol. 171, pp. 140-150, Dec. 2017. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [18] [18] J. Wang, Q. Li, J. Gan, H. Yu, and X. Yang, 'Surface defect detection via entity sparsity pursuit with intrinsic priors,' IEEE Trans. Ind. Inform., vol. 16, no. 1, pp. 141-150, Jan. 2020. - source
- Not checked yetcites [81] [81] M. Xiao, M. Jiang, G. Li, L. Xie, and L. Yi, 'An evolutionary classifier for steel surface defects with small sample set,' EURASIP J. Image Video Process., vol. 2017, no. 1, pp. 1-13, Jul. 2017. - source
- Not checked yetcites [82] [82] S. R. Aghdam, E. Amid, and M. F. Imani, 'A fast method of steel surface defect detection using decision trees applied to LBP based features, ' in 7th IEEE Conf. Ind. Electron. Appl., Singapore, Singapore, 2012, pp. 1447-1452. - source
- Not checked yetcites [83] [83] S. Tian, and K. Xu, 'An algorithm for surface defect identification of steel plates based on genetic algorithm and extreme learning machine,' Metals, vol. 7, no. 8, pp. 1-11, Aug. 2017. - source
- Not checked yetcites [84] [84] R. Ren, T. Hung, and K. C. Tan, 'A generic deep-learning-based approach for automated surface inspection,' IEEE Trans. Cybern., vol. 48, no. 3, pp. 929-940, Mar. 2018. - source
- Not checked yetcites [85] [85] X. Tao, D. Zhang, W. Ma, X. Liu, and D. Xu, 'Automatic metallic surface defect detection and recognition with convolutional neural networks,' Appl. Sci.-Basel, vol. 8, no. 9, pp. 1-15, Sep. 2018. - source
- Not checked yetcites [86] [86] Y. Liu, K. Xu, and D. Wang, 'Online surface defect identification of cold rolled strips based on local binary pattern and extreme learning machine,' Metals, vol. 8, no. 3, pp. 1-18, Mar. 2018. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The Fast Fourier Transform (FFT) not only represents the images' gray features and geometrical features but also realizes fast convolution and object recognition simultaneously.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The BWT can be constructed using the lifting-scheme, not only has the properties of compactly support, time-frequency localization, high vanishing moments and anisotropy but also provides the specific characteristics of strict sampling and adaptability.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A WT may be an ideal candidate because it presents powerful judgment on the spatial-frequency characteristics both horizontally and vertically.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The discrete wavelet transform (DWT) results in different wavelet coefficients when transforming the original signal, the undecimated wavelet transform (UWT) was put forward to overcome this issue in [61]. Which can produce more accurate information for frequency localization and is robust to scales, water marks and uneven illumination.”
What the sources say
- Not checked yetcites [61] [61] X. Wu, K. Xu, and J. Xu, 'Application of undecimated wavelet transform to surface defect detection of hot rolled steel plates,' in Int. Congress Image Signal Process., Sanya, China, May. 2008, pp. 528-532. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Despite the FFT can faster the operation speed, it is not very competitive in image coding, especially in real-time processing.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The multi-scale and multidirection localization method (MGA) was suggested to represent the high-dimensional data of hot-rolled steels in [100], which has been proved to be less redundancy.”
What the sources say
- Not checked yetcites [100] [100] K. Xu, Y.-h. Ai, and X.-y. Wu, 'Application of multi-scale feature extraction to surface defect classification of hot-rolled steels,' Int. J. Minerals, Metallurgy, Mater., vol. 20, no. 1, pp. 37-41, Jan. 2013. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Invariant to geometric and optical deformations.”
What the sources say
- Not checked yetcites [93] [93] N. Dalal, and B. Triggs, 'Histograms of oriented gradients for human detection,' in Conf. Comput. Vis. Pattern Recognition, San Diego, CA, 2005, pp. 886-893. - source
- Not matchedcites [94] [94] J. Masci, U. Meier, D. Ciresan, J. Schmidhuber, and G. Fricout, 'Steel defect classification with max-pooling convolutional neural networks,' in 2012 Int. Joint Conf. Neural Network, Brisbane, AUSTRALIA, 2012, pp. 1-6. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Based on the above advantages, it behaves remarkably for hot-rolled steel [92].”
What the sources say
- Not checked yetcites [92] [92] M. W. Ashour, F. Khalid, A. Abdul Halin, L. N. Abdullah, and S. H. Darwish, 'Surface defects classification of hot-rolled steel strips using multi-directional Shearlet features,' Arab. J. Sci. Eng., vol. 44, no. 4, pp. 2925-2932, Apr. 2018. - source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Compared to other methods, it sets up disparate direction amounts at diverse decomposition scales and is preeminent when approximating 2D smooth functions with discontinuities along the C2-curves; the ST is fit to analyze images with complicated backgrounds and has been successfully applied to defecting steel defects in [101, 102].”
What the sources say
- Not checked yetcites [101] [101] K. Xu, S. Liu, and Y. Ai, 'Application of Shearlet transform to classification of surface defects for metals,' Image Vis. Comput., vol. 35, pp. 23-30, Mar 2015. - source
- Not checked yetcites [102] [102] X. Liu, K. Xu, P. Zhou, D. Zhou, and Y. Zhou, 'Surface defect identification of aluminium strips with non-subsampled shearlet transform,' Opt. Lasers Eng., vol. 127, pp. 1-9, Apr. 2020. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Gabor wavelets cannot effectively depict the directional properties because of their isotropic support and limited directivity.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The selection range of parameters is wide and the calculation is large.”
What the sources say
- Not matchedcites [34] [34] M. Chu, A. Wang, R. Gong, and M. Sha, 'Strip steel surface defect recognition based on novel feature extraction and enhanced least squares twin support vector machine,' ISIJ Int., vol. 54, no. 7, pp. 1638-1645, 2014. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [53] [53] Y. Chen, L. Chen, X. Liu, S. Ding, and H. Zhang, 'Real-time steel inspection system based on support vector machine and multiple kernel learning,' in 6th Int. Conf. Intell. Syst. Knowl. Eng., Shanghai, PEOPLES R CHINA, 2011, pp. 185-190. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [91] [91] X. Zhang, W. Li, J. Xi, Z. Zhang, and X. Fan, 'Surface defect target identification on copper strip based on adaptive genetic algorithm and feature saliency,' Math. Probl. Eng., vol. 2013, pp. 1-10, 2013. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [92] [92] M. W. Ashour, F. Khalid, A. Abdul Halin, L. N. Abdullah, and S. H. Darwish, 'Surface defects classification of hot-rolled steel strips using multi-directional Shearlet features,' Arab. J. Sci. Eng., vol. 44, no. 4, pp. 2925-2932, Apr. 2018. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“Based on the discrete Fourier transform, the DCT is constructed as a real domain transform, which has the characteristic that most of the discriminative information about the steel surface is concentrated in a few coefficients of the DCT [99].”
What the sources say
- Not checked yetcites [99] [99] F. Pernkopf, 'Detection of surface defects on raw steel blocks using Bayesian network classifiers,' Pattern Anal. Appl., vol. 7, no. 3, pp. 333-342, Dec. 2004. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The ST provides efficient multiscale directional representation, which is a relatively new MGA method.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The CT is a higher-dimensional extension of the WT and was created to describe images of different scales and angles.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“CT is a nonadaptive image representation method with two essential features: anisotropy scaling law and directionality.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since the wavelet basis function can only represent 1D directions, it is challenging for wavelet based methods to sparsely express high-dimensional data due to their line singularity and surface singularity.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“High feature dimension and large amount of calculation.”
What the sources say
- Not checked yetcites [93] [93] N. Dalal, and B. Triggs, 'Histograms of oriented gradients for human detection,' in Conf. Comput. Vis. Pattern Recognition, San Diego, CA, 2005, pp. 886-893. - source
- Not matchedcites [94] [94] J. Masci, U. Meier, D. Ciresan, J. Schmidhuber, and G. Fricout, 'Steel defect classification with max-pooling convolutional neural networks,' in 2012 Int. Joint Conf. Neural Network, Brisbane, AUSTRALIA, 2012, pp. 1-6. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“It is applicable to characterize and analyze edge features with curved or linear shapes that perform well in continuous casting slabs [97].”
What the sources say
- Not checked yetcites [97] [97] Y. h. Ai, and K. Xu, 'Surface detection of continuous casting slabs based on curvelet transform and kernel locality preserving projections,' J. Iron Steel Res. Int., vol. 20, no. 5, pp. 80-86, May. 2013. - source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Classical WTs can realize time-frequency localization and 1D data sparse representation.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Can reflect the comprehensive distribution characteristic of the image gray level, such as direction, adjacent interval, change range, etc.”
What the sources say
- Not matchedcites [34] [34] M. Chu, A. Wang, R. Gong, and M. Sha, 'Strip steel surface defect recognition based on novel feature extraction and enhanced least squares twin support vector machine,' ISIJ Int., vol. 54, no. 7, pp. 1638-1645, 2014. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [53] [53] Y. Chen, L. Chen, X. Liu, S. Ding, and H. Zhang, 'Real-time steel inspection system based on support vector machine and multiple kernel learning,' in 6th Int. Conf. Intell. Syst. Knowl. Eng., Shanghai, PEOPLES R CHINA, 2011, pp. 185-190. - source
- Not checked yetcites [91] [91] X. Zhang, W. Li, J. Xi, Z. Zhang, and X. Fan, 'Surface defect target identification on copper strip based on adaptive genetic algorithm and feature saliency,' Math. Probl. Eng., vol. 2013, pp. 1-10, 2013. - source
- Not checked yetcites [92] [92] M. W. Ashour, F. Khalid, A. Abdul Halin, L. N. Abdullah, and S. H. Darwish, 'Surface defects classification of hot-rolled steel strips using multi-directional Shearlet features,' Arab. J. Sci. Eng., vol. 44, no. 4, pp. 2925-2932, Apr. 2018. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“BWT is symmetric, preventing image content from shifting between subbands while allowing for extensions at the boundaries.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Gabor wavelets have many useful characteristics and have been performed well for defect classification in textured materials, which is naturally a single layer architecture, yet deep multilayer architecture is in a position to extract more influential features [96].”
What the sources say
- Not checked yetcites [96] [96] C. Dongyan, X. Kewen, N. Aslam, and H. Jingzhong, 'Defect classification recognition on strip steel surface using second-order cone programming-relevance vector machine algorithm,' J. Comput. Theor. Nanosci., vol. 13, no. 9, pp. 6141-6148, Step. 2016. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Curvelets have very interesting properties, especially, only a few coefficients are needed to approximate the curved singularities, which makes the curvelet coefficients for pixels pertaining to a particular object particular.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“By contrast, the SCT improves the tolerance ability of local deformations for current feature extraction and builds nonlinear invariant representations for the defects on hot-rolled steel strips by cascading wavelet transforms and modulus pooling operators [6], providing a new idea for defect classification.”
What the sources say
- Not checked yetcites [6] [6] K. Song, S. Hu, and Y. Yan, 'Automatic recognition of surface defects on hot-rolled steel strip using scattering convolution network,' J. Comput. Inf. Syst., vol. 10, no. 7, pp. 3049-3055, Apr. 2014. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Invariance to translation, expansion and rotation.”
What the sources say
- Not checked yetcites [68] [68] K. Wiltschi, A. Pinz, and T. Lindeberg, 'An automatic assessment scheme for steel quality inspection,' Mach. Vis. Appl., vol. 12, no. 3, pp. 113-128, Oct. 2000. - source
- Not checked yetcites [96] [96] C. Dongyan, X. Kewen, N. Aslam, and H. Jingzhong, 'Defect classification recognition on strip steel surface using second-order cone programming-relevance vector machine algorithm,' J. Comput. Theor. Nanosci., vol. 13, no. 9, pp. 6141-6148, Step. 2016. - source
- Not checked yetcites [97] [97] Y. h. Ai, and K. Xu, 'Surface detection of continuous casting slabs based on curvelet transform and kernel locality preserving projections,' J. Iron Steel Res. Int., vol. 20, no. 5, pp. 80-86, May. 2013. - source
- Not checked yetcites [98] [98] G. Wu, H. Zhang, X. Sun, J. Xu, and K. Xu, 'A Bran-new feature extraction method and its application to surface defect recognition of hot rolled strips,' in IEEE Int. Conf. Autom. Logis., Jinan, China, Aug. 2007, pp. 2069-2074. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The DCT requires a complicated number operation.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As an unsupervised learning method, Autoencoder (AE) can automatically learn from a large amount of unlabelled data to obtain the effective features.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A novel method such as transfer learning is often been considered for classification tasks due to the most samples of steel are unlabelled.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Great description and anti-noise ability.”
What the sources say
- Not checked yetcites [99] [99] F. Pernkopf, 'Detection of surface defects on raw steel blocks using Bayesian network classifiers,' Pattern Anal. Appl., vol. 7, no. 3, pp. 333-342, Dec. 2004. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“At present, transfer learning makes full use of the previously labelled data and guarantees the precision of the model on new tasks with limited training samples [107], which broadens the application prospect of the CNN.”
What the sources say
- Not checked yetcites [107] [107] V. Natarajan, T.-Y. Hung, S. Vaikundam, L.-T. Chia, and Ieee, 'Convolutional networks for voting-based anomaly classification in metal surface inspection,' in IEEE Int. Conf. Ind. Technol., Toronto, CANADA, 2017, pp. 986-991. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“A group of AutoEncoders were also trained to reduce the dimension of the extracted multiscale features in [109], which improved the performance under inadequate training samples.”
What the sources say
- Not matchedcites [109] [109] D. He, K. Xu, and D. Wang, 'Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels,' Image Vis. Comput., vol. 89, pp. 12-20, Sep. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Anisotropic scaling law and directionality.”
What the sources say
- Not checked yetcites [92] [92] M. W. Ashour, F. Khalid, A. Abdul Halin, L. N. Abdullah, and S. H. Darwish, 'Surface defects classification of hot-rolled steel strips using multi-directional Shearlet features,' Arab. J. Sci. Eng., vol. 44, no. 4, pp. 2925-2932, Apr. 2018. - source
- Not checked yetcites [97] [97] Y. h. Ai, and K. Xu, 'Surface detection of continuous casting slabs based on curvelet transform and kernel locality preserving projections,' J. Iron Steel Res. Int., vol. 20, no. 5, pp. 80-86, May. 2013. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Lack of good direction selectivity and multi-resolution analytical ability.”
What the sources say
- Not checked yetcites [99] [99] F. Pernkopf, 'Detection of surface defects on raw steel blocks using Bayesian network classifiers,' Pattern Anal. Appl., vol. 7, no. 3, pp. 333-342, Dec. 2004. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Due to the reliance on manual design, these features tend to be simpler and mainly depend on some prior information, and it is difficult to use the advantage of big data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“They are the most basic network framework for deep learning, they can learn deep level features that cannot be extracted by traditional manual feature extraction methods under supervised learning manner, which show stronger discrimination[104].”
What the sources say
- Not checked yetcites [104] [104] S. Zhou, Y. Chen, D. Zhang, J. Xie, and Y. Zhou, 'Classification of surface defects on steel sheet using convolutional neural networks,' Mater. Tehnol., vol. 51, no. 1, pp. 123-131, 2017. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“High computational complexity”
What the sources say
- Not checked yetcites [6] [6] K. Song, S. Hu, and Y. Yan, 'Automatic recognition of surface defects on hot-rolled steel strip using scattering convolution network,' J. Comput. Inf. Syst., vol. 10, no. 7, pp. 3049-3055, Apr. 2014. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Difficulty in determining the optimal filter parameters and not robust to rotation invariance.”
What the sources say
- Not checked yetcites [62] [62] R. Medina, F. Gayubo, L. M. Gonzá lez-Rodrigo, D. Olmedo, J. Gó mez-Garcí a-Bermejo, E. Zalama, and J. R. Perán, 'Automated visual classification of frequent defects in flat steel coils,' Int. J. Adv. Manuf. Technol., vol. 57, no. 9-12, pp. 1087-1097, Dec. 2011. - source
- Not checked yetcites [63] [63] D. C. Choi, Y. J. Jeon, S. J. Lee, J. P. Yun, and S. W. Kim, 'Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method,' Appl. Optics, vol. 53, no. 22, pp. 4865-4872, Aug. 2014. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“By contrast, the deep-learning-based feature extraction method can learn automatically from the massive data characteristics of multilayers.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Xu et al. [108] trained CAE to depict fine-grained features and fed them into a softmax classification layer to form a classification network.”
What the sources say
- Not matchedcites [108] [108] D. He, K. Xu, P. Zhou, and D. Zhou, 'Surface defect classification of steels with a new semi-supervised learning method,' Opt. Lasers Eng., vol. 117, pp. 40-48, Feb. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Good sparsity.”
What the sources say
- Not checked yetcites [101] [101] K. Xu, S. Liu, and Y. Ai, 'Application of Shearlet transform to classification of surface defects for metals,' Image Vis. Comput., vol. 35, pp. 23-30, Mar 2015. - source
- Not checked yetcites [102] [102] X. Liu, K. Xu, P. Zhou, D. Zhou, and Y. Zhou, 'Surface defect identification of aluminium strips with non-subsampled shearlet transform,' Opt. Lasers Eng., vol. 127, pp. 1-9, Apr. 2020. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“CAE combines the convolution and pooling operations of the convolutional neural network to extract more robust and discriminative representations.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Capable of multi-resolution analysis and represent local signal features.”
What the sources say
- Not checked yetcites [4] [4] S. Ghorai, A. Mukherjee, M. Gangadaran, and P. K. Dutta, 'Automatic defect detection on hot-rolled flat steel products,' IEEE Trans. Instrum. Meas., vol. 62, no. 3, pp. 612-621, Mar. 2013. -
- Not checked yetcites [61] [61] X. Wu, K. Xu, and J. Xu, 'Application of undecimated wavelet transform to surface defect detection of hot rolled steel plates,' in Int. Congress Image Signal Process., Sanya, China, May. 2008, pp. 528-532. -
- Not checked yetcites [96] [96] C. Dongyan, X. Kewen, N. Aslam, and H. Jingzhong, 'Defect classification recognition on strip steel surface using second-order cone programming-relevance vector machine algorithm,' J. Comput. Theor. Nanosci., vol. 13, no. 9, pp. 6141-6148, Step. 2016. -
- Not checked yetcites [99] [99] F. Pernkopf, 'Detection of surface defects on raw steel blocks using Bayesian network classifiers,' Pattern Anal. Appl., vol. 7, no. 3, pp. 333-342, Dec. 2004. -
- Not checked yetcites [100] [100] K. Xu, Y.-h. Ai, and X.-y. Wu, 'Application of multi-scale feature extraction to surface defect classification of hot-rolled steels,' Int. J. Minerals, Metallurgy, Mater., vol. 20, no. 1, pp. 37-41, Jan. 2013. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the performance of CNN-based methods mainly depend on plentiful training samples, which stunts the utilization of CNNs in industrial scenes with small datasets.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The process is complicated and not easy realize.”
What the sources say
- Not checked yetcites [92] [92] M. W. Ashour, F. Khalid, A. Abdul Halin, L. N. Abdullah, and S. H. Darwish, 'Surface defects classification of hot-rolled steel strips using multi-directional Shearlet features,' Arab. J. Sci. Eng., vol. 44, no. 4, pp. 2925-2932, Apr. 2018. - source
- Not checked yetcites [97] [97] Y. h. Ai, and K. Xu, 'Surface detection of continuous casting slabs based on curvelet transform and kernel locality preserving projections,' J. Iron Steel Res. Int., vol. 20, no. 5, pp. 80-86, May. 2013. - source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the image information of the steel surface is actually distinctive from most pretrained models, which breach the utilization conditions of transfer learning and makes the applications of transfer learning to steel defect detection not as good as the applications in other fields.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Taking the errors of localization result and background into consideration, multigroup CNN (MG-CNN) was created, which can build more effective and explainable feature map groups and can be used for feature extraction of hot-rolled steel images [106].”
What the sources say
- Not checked yetcites [106] [106] D. He, K. Xu, and P. Zhou, 'Defect detection of hot rolled steels with a new object detection framework called classification priority network,' Comput. Ind. Eng., vol. 128, pp. 290-297, Feb. 2019. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Standard CNNs lack multiple resolution pooling and are restricted to a constant size of input images, A multiscale pyramidal pooling network (MSPPN) was presented to solve the above problems [105].”
What the sources say
- Not checked yetcites [105] [105] J. Masci, U. Meier, G. Fricout, and J. Schmidhuber, 'Multi-scale pyramidal pooling network for generic steel defect classification,' in Int. Joint Conf. Neural Networks, Dallas, TX, USA 2013, pp. 1-8. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Local translation invariance and elastic deformation stability.”
What the sources say
- Not checked yetcites [6] [6] K. Song, S. Hu, and Y. Yan, 'Automatic recognition of surface defects on hot-rolled steel strip using scattering convolution network,' J. Comput. Inf. Syst., vol. 10, no. 7, pp. 3049-3055, Apr. 2014. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Variants of CNNs have been proven to have record-breaking performance.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“recursive feature elimination (RFE), which ranks all features in descending order, and an appropriate number of top features are selected [114].”
What the sources say
- Not checked yetcites [114] [114] S. Zhou, Y. Chen, D. Zhang, J. Xie, and Y. Zhou, 'Learning a class-specific and shared dictionary for classifying surface defects of steel sheet,' ISIJ Int., vol. 57, no. 1, pp. 123-130, Nov. 2017.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Deep-learning-based methods can quickly learn modern and powerful feature representations from training data for strange applications.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“In contrast, Zhao et al. [87] proposed the discriminant manifold regularized local descriptor (DMRLD) based on the new viewpoint learning mechanism, which applies the manifold structure to regularize the local descriptor for describing the features of the image.”
What the sources say
- Not checked yetcites [87] [87] J. Zhao, Y. Peng, and Y. Yan, 'Steel surface defect classification based on discriminant manifold regularized local descriptor,' IEEE Access, vol. 6, pp. 71719-71731, Nov. 2018. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“DMRLD is purposefully designed for depicting the useful and completed local feature for steel surface defect classification without a high defect image quality requirement.”
What the sources say
- Not checked yetcites [87] [87] J. Zhao, Y. Peng, and Y. Yan, 'Steel surface defect classification based on discriminant manifold regularized local descriptor,' IEEE Access, vol. 6, pp. 71719-71731, Nov. 2018. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“To enhance the visual feature selection, the adaptive genetic algorithm (AGA) [91] and the hybrid chromosome genetic algorithm (HCGA) [67] were proposed.”
What the sources say
- Not checked yetcites [91] [91] X. Zhang, W. Li, J. Xi, Z. Zhang, and X. Fan, 'Surface defect target identification on copper strip based on adaptive genetic algorithm and feature saliency,' Math. Probl. Eng., vol. 2013, pp. 1-10, 2013. - source
- Not checked yetcites [67] [67] H. Hu, Y. Liu, M. Liu, and L. Nie, 'Surface defect classification in large-scale strip steel image collection via hybrid chromosome genetic algorithm,' Neurocomputing, vol. 181, pp. 86-95, Mar. 2016. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Compare with fully connected network, it reduces many parameters and simplifies the calculation by adopting local connection, weight sharing and down sampling operations.”
What the sources say
- Not checked yetcites [104] [104] S. Zhou, Y. Chen, D. Zhang, J. Xie, and Y. Zhou, 'Classification of surface defects on steel sheet using convolutional neural networks,' Mater. Tehnol., vol. 51, no. 1, pp. 123-131, 2017. - source
- Not matchedcites [105] [105] J. Masci, U. Meier, G. Fricout, and J. Schmidhuber, 'Multi-scale pyramidal pooling network for generic steel defect classification,' in Int. Joint Conf. Neural Networks, Dallas, TX, USA 2013, pp. 1-8. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [106] [106] D. He, K. Xu, and P. Zhou, 'Defect detection of hot rolled steels with a new object detection framework called classification priority network,' Comput. Ind. Eng., vol. 128, pp. 290-297, Feb. 2019. - source
- Not checked yetcites [107] [107] V. Natarajan, T.-Y. Hung, S. Vaikundam, L.-T. Chia, and Ieee, 'Convolutional networks for voting-based anomaly classification in metal surface inspection,' in IEEE Int. Conf. Ind. Technol., Toronto, CANADA, 2017, pp. 986-991. - source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Because all local parts share weights, it does not take into account the difference of contribution to the whole between each local part.”
What the sources say
- Not matchedcites [108] [108] D. He, K. Xu, P. Zhou, and D. Zhou, 'Surface defect classification of steels with a new semi-supervised learning method,' Opt. Lasers Eng., vol. 117, pp. 40-48, Feb. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [109] [109] D. He, K. Xu, and D. Wang, 'Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels,' Image Vis. Comput., vol. 89, pp. 12-20, Sep. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
- Not matched
In the paper
“Xu et al. designed a kernel LPP (KLPP) by implementing the LPP in a kernel space, which was reported to solve nonlinear problems well when applied on the surface inspection for con-casting slabs [97].”
What the sources say
- Not checked yetcites [97] [97] Y. h. Ai, and K. Xu, 'Surface detection of continuous casting slabs based on curvelet transform and kernel locality preserving projections,' J. Iron Steel Res. Int., vol. 20, no. 5, pp. 80-86, May. 2013. - source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The genetic algorithm (GA) is a typical random search algorithm [98].”
What the sources say
- Not checked yetcites [98] [98] G. Wu, H. Zhang, X. Sun, J. Xu, and K. Xu, 'A Bran-new feature extraction method and its application to surface defect recognition of hot rolled strips,' in IEEE Int. Conf. Autom. Logis., Jinan, China, Aug. 2007, pp. 2069-2074. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“A variety of improved methods based on concrete scenes have emerged, such as the incorporation of the PCA with bootstrap aggregating, which reduces not only the dimensions but also the variance in the decision trees [82].”
What the sources say
- Not checked yetcites [82] [82] S. R. Aghdam, E. Amid, and M. F. Imani, 'A fast method of steel surface defect detection using decision trees applied to LBP based features, ' in 7th IEEE Conf. Ind. Electron. Appl., Singapore, Singapore, 2012, pp. 1447-1452. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A representative heuristic algorithm, Relief, is mainly based on sample learning. This method is straightforward and efficient but is restricted to binary classification.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A large number of features may increase the complexity of the classifier, slow down the training process and seriously affect the final classification accuracy and efficiency.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The research methods used to design the network automatically is helpful to the design space exploration of deep learning networks and plays an essential role in accelerating the process of network design and promoting the application of deep learning in engineering.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Correlatively, manifold regularization adds items related to the manifolds to the regularization items to play the role of semisupervision by using the geometric structures in the data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“High computational burden and poor classification ability.”
What the sources say
- Not checked yetcites [87] [87] J. Zhao, Y. Peng, and Y. Yan, 'Steel surface defect classification based on discriminant manifold regularized local descriptor,' IEEE Access, vol. 6, pp. 71719-71731, Nov. 2018. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“After feature evaluation, the dimension of the feature subsets may be too large, and there exists much redundancy, which will reduce the robustness and generalization speed of the classifier, so the process of dimension reduction is necessary.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“After the processing of the two steps mentioned above, the subsets acquired may not be optimal for the classifiers; thus, feature optimization is essential in improving the overall performance.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There are two traditional optimization algorithms based on searching strategies: random search algorithms and heuristic algorithms .”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The calculation of weight matrices and the selection of constraint matrices are the main differences in these methods [100].”
What the sources say
- Not checked yetcites [100] [100] K. Xu, Y.-h. Ai, and X.-y. Wu, 'Application of multi-scale feature extraction to surface defect classification of hot-rolled steels,' Int. J. Minerals, Metallurgy, Mater., vol. 20, no. 1, pp. 37-41, Jan. 2013. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One reason feature selection is needed is to avoid the so-called curse of dimensionality, and another is to enhance the generalization ability of the designed classifier.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Compared with the traditional autoencoder, it can well retain the spatial information of 2D signals and can be used in unsupervised learning.”
What the sources say
- Not matchedcites [108] [108] D. He, K. Xu, P. Zhou, and D. Zhou, 'Surface defect classification of steels with a new semi-supervised learning method,' Opt. Lasers Eng., vol. 117, pp. 40-48, Feb. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [109] [109] D. He, K. Xu, and D. Wang, 'Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels,' Image Vis. Comput., vol. 89, pp. 12-20, Sep. 2019. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“Two main approaches were used in [110], where the filter approach assigned the weights before induction, the wrapper approach ran an induction algorithm on the training set and used the accuracy of the resulting description to evaluate the mill scale defect feature set.”
What the sources say
- Not checked yetcites [110] [110] S. Lechwar, Ł. Rauch, and M. Pietrzyk, 'Use of Artificial Intelligence in Classification of Mill Scale Defects,' Steel Res. Int., vol. 86, no. 3, pp. 266-277, Mar. 2015. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“To speed up the promotion and application of deep learning features, academia and industry have carried out much research on structure optimization.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Be able to find the essence of from the observed phenomena, and find the internal law of data generation.”
What the sources say
- Not checked yetcites [87] [87] J. Zhao, Y. Peng, and Y. Yan, 'Steel surface defect classification based on discriminant manifold regularized local descriptor,' IEEE Access, vol. 6, pp. 71719-71731, Nov. 2018. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The earlier local descriptors, such as the LBP and HOG, subject to the hand-crafted definition and the limitations of the applications.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This kind of mind-set has strong adaptability to the data, especially in the case of sufficient labelled data to obtain better results.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Neuhauser et al. [117] altered the GoogLeNet architecture and enabled transform learning to expedite the training process and to improve performance of the network by compensating for the limited training set.”
What the sources say
- Not checked yetcites [117] [117] F. M. Neuhauser, G. Bachmann, and P. Hora, 'Surface defect classification and detection on extruded aluminum profiles using convolutional neural networks,' Int. J. Mater. Form., vol. 13, no. 4, pp. 591-603, Jul. 2020.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Chu's team kept improving the algorithm and came up with a series of SVM variants, such as the enhanced least squares twin SVM [34], multiple support vector hypersphere with feature and sample weights [39], machine learning with quantile hyperspheres [121] and multiple hyperspheres vector machine with additional information [54].”
What the sources say
- Not matchedcites [34] [34] M. Chu, A. Wang, R. Gong, and M. Sha, 'Strip steel surface defect recognition based on novel feature extraction and enhanced least squares twin support vector machine,' ISIJ Int., vol. 54, no. 7, pp. 1638-1645, 2014. -
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked yetcites [39] [39] R. Gong, C. Wu, M. Chu, and X. Liu, 'The strip steel surface defect recognition based on multiple support vector hyper-sphere with feature and sample weights,' Steel Res. Int., vol. 87, no. 12, pp. 1678-1685, Dec. 2016. - source
- Not matchedcites [121] [121] M. Chu, J. Zhao, X. Liu, and R. Gong, 'Multi-class classification for steel surface defects based on machine learning with quantile hyper-spheres,' Chemometrics Intell. Lab. Syst., vol. 168, pp. 15-27, Sep. 2017.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“Moreover, He et al. [109] designed a hierarchical learning scheme by combining the CNN and the AutoEncoder, which has greatly improved the performance of the model with insufficient training samples.”
What the sources say
- Not checked yetcites [109] [109] D. He, K. Xu, and D. Wang, 'Design of multi-scale receptive field convolutional neural network for surface inspection of hot rolled steels,' Image Vis. Comput., vol. 89, pp. 12-20, Sep. 2019. - source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Therefore, compressing and accelerating the model under the premise of ensuring the accuracy of the network has become a hot spot of discussion in the field of network structure optimization.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, studying unsupervised classification methods greatly benefits surface defect recognition for flat steel.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: In real-world flat steel industrial production lines, collecting and manually labeling a large number of flat steel surface defect data is extremely labour intensive and time consuming, compounded by harsh manufacturing environments.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In general, networks with multiple convolutional layers can do better in regression and classification tasks than shallow networks.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The distance function is one of the simplest tools for defect classification.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Luo et al. [10, 90] selected the NNC as the dissimilarity metric between two multi-region histograms to identify LBP-like histograms.”
What the sources say
- Not checked yetcites [10] [10] Q. Luo, X. Fang, Y. Sun, L. Liu, J. Ai, C. Yang, and O. Simpson, 'Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns,' IEEE Access, vol. 7, pp. 23488-23499, Feb. 2019. -
- Not checked yetcites [90] [90] Q. Luo, Y. Sun, P. Li, O. Simpson, L. Tian, and Y. He, 'Generalized completed local binary patterns for time-efficient steel surface defect classification,' IEEE Trans. Instrum. Meas., vol. 68, no. 3, pp. 667-679, Mar. 2019. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Distance is an important measure to describe the relationship between pixels.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“To achieve high classification accuracy with a small training sample set, Fu et al. [116] presented a compact yet powerful CNN model by training the low-level features and incorporating multiple receptive fields. This method was reported to realize high classification accuracy of steel surface defects based on limited defect-specific training samples.”
What the sources say
- Not checked yetcites [116] [116] G. Fu, P. Sun, W. Zhu, J. Yang, Y. Cao, M. Y. Yang, and Y. Cao, 'A deep-learning-based approach for fast and robust steel surface defects classification,' Opt. Lasers Eng., vol. 121, pp. 397-405, Oct. 2019.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The iteration scale expands with the increase of the number in weak classifiers, and correspondingly, more training time will be spent.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Moreover, Zhao et al. [87] simultaneously considered the variation and exemplar distances of the NNC to measure the similarity between the local models.”
What the sources say
- Not checked yetcites [87] [87] J. Zhao, Y. Peng, and Y. Yan, 'Steel surface defect classification based on discriminant manifold regularized local descriptor,' IEEE Access, vol. 6, pp. 71719-71731, Nov. 2018. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Feature selection plays a critical role in enhancing the performance of the classifier and indirectly influences the process of steel defect recognition.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“According to whether the training sample has a label or not, the current classifiers can be classified into supervised, unsupervised and semi-supervised learning-based classifiers.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Inspired by [59], Agarwal et al. [119] proposed a classification scheme of the process-knowledge-based multiclass SVM (called PK-MSVM) by combining the feature extraction task of the automated defect inspection with the process knowledge; the PK-MSVM performed better than the traditional multiclass SVM.”
What the sources say
- Not checked yetcites [119] [119] K. Agarwal, R. Shivpuri, Y. Zhu, T.-S. Chang, and H. Huang, 'Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling,' Expert Syst. Appl., vol. 38, no. 6, pp. 7251-7262, Jun. 2011. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Further increasing the classification accuracy requires exploring high-dimensional features, in return, sacrificing the classification speed.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The nearest neighbour classifier (NNC) is the commonly used distance classifier for surface defect classification, where the chi-square distance is a simple but effective criterion for measuring the similarity between the training images and the test images.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, the expensive training step limits the generalization of nascent neural networks.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The classification methods based on supervised learning can excute sufficient training and learning on the images to obtain the most effective representation.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The accuracy of this kind of conventional classifier primarily relies on the precision of the extracted features.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Different from the conventional classification process that treats feature extraction and classifier training as two separate steps, classification methods based on dictionary learning and sparse representation do not have an explicit stage of feature extraction, which might be more effective in surface defect classification for flat steel.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, a large number of application scenarios cannot provide the necessary resources.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the performance of multi-classifier fusion mainly depends on the selection of weak classifiers.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“At present, there exists network architectures containing more than 100 convolutional layers.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The performance of supervised classification methods mainly relies on two factors: the quality of the aforementioned features and the capability of the classifier.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Furthermore, Zhou et al. [114] utilized the high-quality dictionary to extract the compact, reconstructive and discriminative features of the test images.”
What the sources say
- Not checked yetcites [114] [114] S. Zhou, Y. Chen, D. Zhang, J. Xie, and Y. Zhou, 'Learning a class-specific and shared dictionary for classifying surface defects of steel sheet,' ISIJ Int., vol. 57, no. 1, pp. 123-130, Nov. 2017.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In [74], the authors recommended the K nearest neighbour (KNN) as a classifier to realize defect classification for cold-rolled steel with a co-occurrence matrix.”
What the sources say
- Not checked yetcites [74] [74] F. Dupont, C. Odet, and M. Cartont, 'Optimization of the recognition of defects in flat steel products with the cost matrices theory,' Ndt E Int., vol. 30, no. 1, pp. 3-10, Feb. 1997. -
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Xiao et al. [81] constructed a multiple classifier system for classifying steel surface defects, which contains multiple SVM classifiers and a Bayes kernel classifier. The Bayes kernel fuses the results from the multiple classifiers and adjusts the hybrid parameters with only a small sample set.”
What the sources say
- Not checked yetcites [81] [81] M. Xiao, M. Jiang, G. Li, L. Xie, and L. Yi, 'An evolutionary classifier for steel surface defects with small sample set,' EURASIP J. Image Video Process., vol. 2017, no. 1, pp. 1-13, Jul. 2017. -
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“SVM is a generalized linear classifier for binary classification, which has been widely used for the defect classification of flat steel surfaces [67, 100-102, 118].”
What the sources say
- Not checked yetcites [67] [67] H. Hu, Y. Liu, M. Liu, and L. Nie, 'Surface defect classification in large-scale strip steel image collection via hybrid chromosome genetic algorithm,' Neurocomputing, vol. 181, pp. 86-95, Mar. 2016. - source
- Not checked yetcites [100] [100] K. Xu, Y.-h. Ai, and X.-y. Wu, 'Application of multi-scale feature extraction to surface defect classification of hot-rolled steels,' Int. J. Minerals, Metallurgy, Mater., vol. 20, no. 1, pp. 37-41, Jan. 2013. - source
- Not checked yetcites [101] [101] K. Xu, S. Liu, and Y. Ai, 'Application of Shearlet transform to classification of surface defects for metals,' Image Vis. Comput., vol. 35, pp. 23-30, Mar 2015. - source
- Not checked yetcites [102] [102] X. Liu, K. Xu, P. Zhou, D. Zhou, and Y. Zhou, 'Surface defect identification of aluminium strips with non-subsampled shearlet transform,' Opt. Lasers Eng., vol. 127, pp. 1-9, Apr. 2020. - source
- Not matchedcites [118] [118] X. Liu, K. Xu, D. Zhou, and P. Zhou, 'Improved contourlet transform construction and its application to surface defect recognition of metals,' Multidimens. Syst. Signal Process., vol. 31, no. 3, pp. 951-964, Jul. 2020.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Unsupervised learning methods mainly separate samples of the same type through the different characteristics of the data, and therefore, they do not need the label information from the data.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results reached with limited sample set can also match the results obtained with massive sample set.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Generally, supervised learning methods need labelled samples for model training, which makes full use of the prior information in the data categories.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The latest image processing or machine learning methods provide novel way to enhance the image quality of flat steel.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The accuracy of a single-mode algorithm for classification is always limited, and internal prior utilization, multimode fusion and interdisciplinary crossing can give full play to their respective advantages, which is the trend of future development.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: The accuracy of a single-mode algorithm for classification is always limited.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In specific industrial practice, the quantity of the original samples might be limited (lack of representativeness and with contingencies), and the distribution can be uneven. If these situations happen, the semisupervised learning methods may become unreliable, creating difficulties for its development.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Meanwhile, achieving high-precision classification in the case of only a small number of labelled samples is still a hot issue”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, excessive deletion of redundant features leads to the loss of a large amount of information.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The current unsupervised learning results show that there is still some disparity from our ultimate goal.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Designing a feature selection method for high-dimensional small sample data without decreasing the prediction accuracy of the feature subset is challenging.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“For instance, the GAN has realized extreme achievement in image enhancement, providing a better solution to the problems of insufficient samples, feature extraction difficulties and poor image quality.”
What the sources say
- Not checked yetcites [131] [131] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, 'Generative adversarial nets,' ArXiv , pp. 1-9, 06/01, 2014.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the same time, obtaining a real-time, reliable and stable fusion feature extraction scheme will always be a research hotspot.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For instance, some techniques have shown prominent results on the image enhancement (i.e., noise removal) of many texture images with rich datasets in multimedia areas, although they are not necessarily suitable for the enhancement of images gathered from industrial production lines where labelled image sets are extremely limited.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to the strong randomness of the features of the flat steel surface image, it is very difficult to use a single mathematical model to extract features.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, making full use of the prior knowledge of human visual perception characteristics and combining it with feature extraction methods could be helpful to describe the image more effectively.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After feature selection, the prediction accuracy of the high-dimensional small sample dataset may decline.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Image preprocessing usually makes the contrast, visual effect and entropy of the image reach a balance on the premise of improving the image contrast.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is challenging for unsupervised learning methods to realize the high accuracy of supervised classification.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Semisupervised learning methods joint the previous two manners, which utilizes the labelled samples and massive unlabelled samples to train classifiers.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The feature selection algorithm is subject to the data distribution. If the training set changes, the feature subset is also very different.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Compared with machine learning-based methods for defect detection, defect classification methods focus on extracting the features of different kinds of defects and classifying the defects with similar features into one category.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We noticed that, the number of labels to get comparable results to fully supervised learning is decreasing.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We identified that some common ideas are not often combined and that the combination of broad range and unusual methods is beneficial.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper relies on this without support.
The natural image processing procedure of defect recognition consists of five successive parts: image acquisition, image preprocessing, feature extraction, feature selection, and defect classifier.
“According to the natural image processing procedure of defect recognition, the diverse approaches are grouped into five successive parts: image acquisition, image preprocessing, feature extraction, feature selection and defect classifier.”
The paper relies on this without support.
The traditional spatial domain feature set is very complex, very laborious to extract, and difficult to guarantee real-time recognition of flat steel surface defects.
“The traditional spatial domain feature set is very complex, very laborious to extract and difficult to guarantee the real-time recognition of flat steel surface defects.”
The paper relies on this without support.
To support near-real-time operation, feature extraction needs to be competitive in feature dimensions and algorithm properties.
“To support near-real-time operation, the feature extraction need to be competitive in feature dimensions and algorithm properties.”
The authors state this as an assumption.
The data are a low-dimensional manifold uniformly sampled in a high-dimensional Euclidean space.
“Supposing the data are a low-dimensional manifold uniformly sampled in a high-dimensional Euclidean space”
The authors state this as an assumption.
Graph embedding dimensionality reduction methods assume the data set is on a low-dimensional manifold embedded in the raw high-dimensional feature space.
“which are all based on the assumption that the data set is on a low-dimensional manifold embedded in the raw high-dimensional feature space.”
The paper relies on this without support.
Most existing steel samples are not marked because of the randomness and limitations of the production line, so only a small amount of data with safety labels can be used for learning, resulting in instability of the final classification results.
“However, most of the existing steel samples are not marked because of the randomness and limitations of the production line, so only a small amount of data with safety labels can be used for learning, resulting in the instability of the final classification results.”
The paper relies on this without support.
In real-world flat steel industrial production lines, collecting and manually labeling a large number of flat steel surface defect data is extremely labour intensive and time consuming, compounded by harsh manufacturing environments.
“In real-world flat steel industrial production lines, it is extremely labour intensive and time consuming to collect a large number of flat steel surface defect data, and label them manually; this challenge is compounded by the harsh manufacturing environment (e.g., high temperature, poor lighting conditions, mechanical vibrations, and dense dust).”
The paper relies on this without support.
The imbalance of defect samples in actual flat steel production lines is a real problem that prompts researchers to use limited samples to achieve similar results.
“However, the imbalance of defect samples in actual flat steel production lines has prompted researchers to use limited samples to achieve similar results.”
The paper relies on this without support.
The quality of the input image and the original parameters of the classification model are the keys to determining the classification performance of the unsupervised learning model.
“However, the quality of the input image and the original parameters of the classification model are the keys to determining the classification performance of the unsupervised learning model.”
The paper relies on this without support.
The accuracy of a single-mode algorithm for classification is always limited.
“The accuracy of a single-mode algorithm for classification is always limited”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the actual production environment, it is very difficult to obtain high quality datasets for training machine learning related algorithms because defects in steel production are originally small probability events, and it is very difficult to obtain many samples with various types of defects”
From Steel Surface Defect Recognition: A Survey (2022) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“the labeling of the data is also costly and labor intensive [4][5][6][7].”
From Steel Surface Defect Recognition: A Survey (2022) · cites this paper as [4]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Therefore, the steel surface defect detection method based on deep learning is widely used in steel production, and more scholars begin to improve and perfect it [13, 14].”
From A New Steel Defect Detection Algorithm Based on Deep Learning (2021) · cites this paper as [14]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 5) Classifiers based on Multi-Classifier Fusion · p. 13
“Yan et al. [122] put forward a classification method”
§ REFERENCES · p. 20
“[122] Y. Zhang, W. Liu, Z. Xing, and Y. Yan”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ C. Semisupervised Learning · p. 14
“Song et al. [135] utilized the multi-training of the deep convolutional GAN (DCGAN)”
§ REFERENCES · p. 20
“[135] Y. He, K. Song, H. Dong, and Y. Yan”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ VIII. SUMMARY AND DISCUSSION · p. 16
“The three key parts of image acquisition, image preprocessing, feature extraction, feature selection and defect classification are reviewed successively.”
§ Automated Visual Defect Classification for Flat Steel Surface: A Survey · p. 1
“According to the natural image processing procedure of defect recognition, the diverse approaches are grouped into five successive parts: image acquisition, image preprocessing, feature extraction, feature selection and defect classifier.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ D. Quick Glance of Defect Types · p. 3
“Fig. 3 (c) shows twelve types of image defects of cold-rolled steel strips”
§ A. Image Denoising · p. 4
“Fig. 2. Typical defect image samples. (a) Continuous casting slabs, (b) hot-rolled steel strips and (c) cold-rolled steel strips.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 2) Convolutional Autoencoder (CAE) · p. 9
“Xu et al. [108] trained CAE to depict fine-grained features”
§ C. Semisupervised Learning · p. 14
“Di et al. [108] designed an improved GAN, namely, SGAN”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 3) Histogram of Oriented Gradients (HOG) · p. 7
“the pyramid of HOG (PHOG) descriptor takes into account the spatial locality of the descriptor's constituents in [94]”
§ VIII. SUMMARY AND DISCUSSION · p. 16
“| [94] | 2012 | Max-Pooling convolutional neural networks | Max-Pooling convolutional neural networks”
Citations
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Visual pattern recognition by moment invariants
Hu, M.-K. (1962) · IRE Transactions on Information Theory · doi:10.1109/tit.1962.1057692 · Cited by 7,594
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965
Self-citationMixMatch: A Holistic Approach to Semi-Supervised LearningPreprint
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., & Raffel, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02249 · Cited by 2,523
Deep Learning for Hyperspectral Image Classification: An Overview
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., & Benediktsson, J. A. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2907932 · Cited by 2,007
Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015) · Advanced Engineering Informatics · doi:10.1016/j.aei.2015.01.008 · Cited by 1,065 · cited 1×
“Gradually, researchers began to focus on specific planar materials, such as asphalt pavement [22], [23], fabric [24]–[26], timber [27], and semiconductors [28].”
BackgroundSong, K., & Yan, Y. (2013) · Applied Surface Science · doi:10.1016/j.apsusc.2013.09.002 · Cited by 1,026
Semi-Supervised Learning with Generative Adversarial NetworksPreprint
Odena, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.01583 · Cited by 789
Research works printed but not linked: 2
- song, 2014, Automatic recognition of surface defects on hot-rolled steel strip using scattering convolution network, J Comput Inf Syst, 10, 3049
- xu, 2008, Application of fractal dimensions based on the optimized scale to classification of surface defects on hot-rolled steel strips, Metallic Equipment, 168, 1
Cited by
Sorted by citations
Triplet-Graph Reasoning Network for Few-Shot Metal Generic Surface Defect Segmentation
Bao, Y., Song, K., Liu, J., Wang, Y., Yan, Y., Yu, H., & Li, X. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3083561 · Cited by 322
Review of surface defect detection of steel products based on machine vision
Tang, B., Chen, L., Sun, W. M., & Lin, Z. (2022) · IET Image Processing · doi:10.1049/ipr2.12647 · Cited by 311
A New Steel Defect Detection Algorithm Based on Deep Learning
Zhao, W., Chen, F., Huang, H., Li, D., & Cheng, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5592878 · Cited by 259
“+erefore, the steel surface defect detection method based on deep learning is widely used in steel production, and more scholars begin to improve and perfect it [13, 14].”
MethodSteel Surface Defect Recognition: A Survey
Wen, X., Shan, J., He, Y., & Song, K. (2022) · Coatings · doi:10.3390/coatings13010017 · Cited by 210
Wang, S., Xia, X., Ye, L., & Yang, B. (2021) · Metals · doi:10.3390/met11030388 · Cited by 184
Metadata
- Authors
- Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L.
- Affiliations
- Central South University (CN)
- Hefei University of Technology (CN)
- National University of Defense Technology (CN)
- ORCID
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2020-12
- Volume
- 69
- Issue
- 12
- Pages
- 9329-9349
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W3102571413
- Semantic Scholar
- c1898f9b88e92c05fe42b48c960bdb95898963fd
- Cited by
- 148
- References
- 140
- Citations by year
- 2026: 21 · 2025: 34 · 2024: 28 · 2023: 28 · 2022: 24 · 2021: 13
- FWCI
- 10.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Funders
- National Natural Science Foundation of China (61988101, 61973323, 51704089)
- Hunan Provincial National Science Foundation of China (2020JJ4747)
- Innovation and Development Project of Ministry of Industry and Information Technology of the People’s Republic of China (TC19084DY)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- automated visual inspection, surface defect classification, flat steel, steel slabs, hot-rolled strip, cold-rolled steel strip, image preprocessing, feature extraction, feature selection, defect detection, online quality inspection, computer vision
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-10, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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