Steel Surface Defect Detection Using a New Haar–Weibull-Variance Model in Unsupervised Manner
Abstract
Automatic defect detection on the steel surface is a challenging task in computer vision, owing to miscellaneous patterns of the defects, low contrast between the defect and background, the existence of pseudo defects, and so on. In this paper, a new Haar-Weibull-variance (HWV) model is proposed for steel surface defect detection in an unsupervised manner. First, an anisotropic diffusion model is utilized to eliminate the influence of pseudodefects. Second, a new HWV model is established to characterize the texture distribution of each local patch in the image. The proposed model can project the texture distribution of each patch into the low-dimensional space with only two parameters. The parameter distribution of the whole image can also be unified into the form of linear radiation in an Euclidean space. The reliable background can be extracted via the formation of parameter distribution, by which the model parameter can be optimized further. Finally, the adaptive threshold can be determined to distinguish the defect from the background. Experimental results show that the proposed method can detect an arbitrary type of defect on the homogeneously textured surface and achieve an average detection rate of 96.2% on the data set, which outperforms the previous methods.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Liu et al. developed a Haar-Weibull-variance (HWV) model by replacing local gradient magnitude features with Haar features from local patches.
“Hence, Liu et al. [91] developed a Haar-Weibull-variance (HWV) model by replacing the features of local gradient magnitude by Haar features from local patches.”
From Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020) · cites this paper as [91]
The HWV model achieved an average correct detection rate of 96.2% on a homogeneously textured defect dataset from an actual hot-rolling mill.
“This method is reported to have achieved an average correct detection rate of 96.2% on a homogeneously textured defect dataset gathered from an actual hot-rolling mill.”
From Automated Visual Defect Detection for Flat Steel Surface: A Survey (2020) · cites this paper as [91]
Cites
Scale-space and edge detection using anisotropic diffusion
Perona, P., & Malik, J. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.56205 · Cited by 12,143 · see citing works
Song, K., & Yan, Y. (2013) · Applied Surface Science · doi:10.1016/j.apsusc.2013.09.002 · Cited by 1,026 · see citing works
Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation
Turaga, S. C., Murray, J. F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., Denk, W., & Seung, H. S. (2010) · Neural Computation · doi:10.1162/neco.2009.10-08-881 · Cited by 376 · see citing works
Automatic Defect Detection on Hot-Rolled Flat Steel Products
Ghorai, S., Mukherjee, A., Gangadaran, M., & Dutta, P. K. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2218677 · Cited by 317 · see citing works
A Contrast Adjustment Thresholding Method for Surface Defect Detection Based on Mesoscopy
Win, M., Bushroa, A. R., Hassan, M. A., Hilman, N. M., & Ide-Ektessabi, A. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2417676 · Cited by 142 · see citing works
A Six-Stimulus Theory for Stochastic Texture
Geusebroek, J., & Smeulders, A. W. M. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-4632-7 · Cited by 131 · see citing works
Wavelet-based defect detection in solar wafer images with inhomogeneous texture
Li, W.-C., & Tsai, D. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.07.025 · Cited by 127 · see citing works
Karimi, M., & Asemani, D. (2014) · ISA Transactions · doi:10.1016/j.isatra.2013.11.015 · Cited by 115 · see citing works
The phase only transform for unsupervised surface defect detection
Aiger, D., & Talbot, H. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5540198 · Cited by 104 · see citing works
Active Microwave Thermography for Defect Detection of CFRP-Strengthened Cement-Based Materials
Foudazi, A., Edwards, C. A., Ghasr, M. T., & Donnell, K. M. (2016) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2016.2596080 · Cited by 81 · see citing works
THE PHASE ONLY TRANSFORM FOR UNSUPERVISED SURFACE DEFECT DETECTION
Aiger, D., & Talbot, H. (2011) · Series in computer vision · doi:10.1142/9789814343008_0011 · Cited by 76 · see citing works
Automated Visual Inspection System for Bogie Block Key Under Complex Freight Train Environment
Liu, L., Zhou, F., & He, Y. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2015.2479101 · Cited by 73 · see citing works
A fast regularity measure for surface defect detection
Tsai, D.-M., Chen, M.-C., Li, W.-C., & Chiu, W.-Y. (2012) · Machine Vision and Applications · doi:10.1007/s00138-011-0403-3 · Cited by 70 · see citing works
Unsupervised Variational Image Segmentation/Classification Using a Weibull Observation Model
Ben Ayed, I., Hennane, N., & Mitiche, A. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881961 · Cited by 70 · see citing works
Non-parametric texture defect detection using Weibull features
Timm, F., & Barth, E. (2011) · SPIE Proceedings · doi:10.1117/12.872463 · Cited by 68 · see citing works
Steel plates fault diagnosis on the basis of support vector machines
Tian, Y., Fu, M., & Wu, F. J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.09.036 · Cited by 63 · see citing works
Self-citationAn Effective Defect Inspection Method for LCD Using Active Contour Model
Gan, Y., & Zhao, Q. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2013.2258242 · Cited by 59 · see citing works
A fast method of steel surface defect detection using decision trees applied to LBP based features
Aghdam, S. R., Amid, E., & Imani, M. F. (2012) · 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2012.6360951 · Cited by 46 · see citing works
Background and Clutter Mixture Distributions for Active Sonar Statistics
Abraham, D. A., Gelb, J. M., & Oldag, A. W. (2011) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2010.2102150 · Cited by 46 · see citing works
Automatic saw-mark detection in multicrystalline solar wafer images
Li, W.-C., & Tsai, D. (2011) · Solar Energy Materials and Solar Cells · doi:10.1016/j.solmat.2011.03.025 · Cited by 45 · see citing works
Characterization of Corroded Reinforced Steel Bars by Active Microwave Thermography
Foudazi, A., Ghasr, M. T., & Donnell, K. M. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2015.2450353 · Cited by 43 · see citing works
Xu, L., & Huang, Q. (2012) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2012.2209417 · Cited by 39 · see citing works
Unsupervised detection of surface defects: A two-step approach
Choi, J., & Kim, C. (2012) · 2012 19th IEEE International Conference on Image Processing · doi:10.1109/icip.2012.6467040 · Cited by 36 · see citing works
Li, T.-Y., Tsai, J., Chang, R., Ho, L.-W., & Yang, C.-F. (2012) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2207658 · Cited by 35 · see citing works
Qaddoumi, N. N., El‐Hag, A., Hosani, M. A., Mansouri, I. A., & Ghufli, H. A. (2010) · IEEE Transactions on Dielectrics and Electrical Insulation · doi:10.1109/tdei.2010.5448094 · Cited by 32 · see citing works
A Wavelet-Based Approach in Detecting Visual Defects on Semiconductor Wafer Dies
Yeh, C.-H., Wu, F.-C., Ji, W.-L., & Huang, C. (2010) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2010.2046108 · Cited by 29 · see citing works
Fabric defect detection based on texture enhancement
Zuo, H., Wang, Y., Yang, X., & Wang, X. (2012) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2012.6469799 · Cited by 25 · see citing works
An automatic detection method of mura defects for liquid crystal display using real Gabor filters
Bi, X., Xu, X., & Shen, J. (2015) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2015.7408000 · Cited by 19 · see citing works
Inspection of metallic surfaces using Local Binary Patterns
Mansano, M. D. R., Pavesi, L., Oliveira, L. S., Britto, A., & Koerich, A. L. (2011) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2011.6119655 · Cited by 10 · see citing works
Modelling of inhomogeneity in radar clutter using Weibull mixture densities
Cao, T. (2013) · IET Radar Sonar & Navigation · doi:10.1049/iet-rsn.2012.0367 · Cited by 6 · see citing works
A new Mumford-Shah type model involvinga smoothing operator for multiphase image segmentation
Gu, Y., Xiong, W., Wang, L.-L., Cheng, J., Du, J., Chen, W., Wang, Y., & Chia, S.-C. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351149 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features
He, Y., Song, K., Meng, Q., & Yan, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2915404 · Cited by 1,341 · see citing works
“For example, some methods perform binary classification to find the regions of defects [9], [10] or only provide a coarse region of a defect [11], [12].”
MethodAutomated Visual Defect Detection for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Liu, L., Yang, C., & Sun, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2963555 · Cited by 559 · see citing works
Surface Defect Detection Methods for Industrial Products: A Review
Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021) · Applied Sciences · doi:10.3390/app11167657 · Cited by 457 · see citing works
Recent advances in surface defect inspection of industrial products using deep learning techniques
Zheng, X., Zheng, S., Kong, Y., & Chen, J. (2021) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-021-06592-8 · Cited by 287 · see citing works
Steel Surface Defect Recognition: A Survey
Wen, X., Shan, J., He, Y., & Song, K. (2022) · Coatings · doi:10.3390/coatings13010017 · Cited by 191 · see citing works
Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification
Luo, Q., Sun, Y., Li, P., Simpson, O., Tian, L., & He, Y. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2852918 · Cited by 178 · see citing works
A steel surface defect inspection approach towards smart industrial monitoring
Hao, R., Lu, B., Cheng, Y., Li, X., & Huang, B. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01670-2 · Cited by 182 · see citing works
Ma, Y., Yin, J., Huang, F. C., & Li, Q. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10956-3 · Cited by 169 · see citing works
Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials
Fang, X., Luo, Q., Zhou, B., Li, C., & Tian, L. (2020) · Sensors · doi:10.3390/s20185136 · Cited by 166 · see citing works
A Simple Guidance Template-Based Defect Detection Method for Strip Steel Surfaces
Wang, H., Zhang, J., Tian, Y., Chen, H., Sun, H., & Liu, K. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2887145 · Cited by 153 · see citing works
Self-citationFPCB Surface Defect Detection: A Decoupled Two-Stage Object Detection Framework
Luo, J., Yang, Z., Li, S., & Wu, Y. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3092510 · Cited by 152 · see citing works
Automated Visual Defect Classification for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148 · see citing works
Surface Defect Detection via Entity Sparsity Pursuit With Intrinsic Priors
Wang, J., Li, Q., Gan, J., Yu, H., & Yang, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2917522 · Cited by 141 · see citing works
A survey of real-time surface defect inspection methods based on deep learning
Liu, Y., Zhang, C., & Dong, X. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10475-7 · Cited by 131 · see citing works
WSS-YOLO: An improved industrial defect detection network for steel surface defects
Lu, M. L., Sheng, W., Zou, Y. Y., Chen, Y., & Chen, Z. (2024) · Measurement · doi:10.1016/j.measurement.2024.115060 · Cited by 120 · see citing works
Su, B., Chen, H., Zhu, Y., Liu, W., & Liu, K. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2900961 · Cited by 119 · see citing works
Self-citationA nondestructive automatic defect detection method with pixelwise segmentation
Yang, L., Fan, J., Huo, B., Li, E., & Liu, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108338 · Cited by 119 · see citing works
Dual attention deep learning network for automatic steel surface defect segmentation
Pan, Y., & Zhang, L. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12792 · Cited by 115 · see citing works
Cohn, R., & Holm, E. A. (2021) · Integrating materials and manufacturing innovation · doi:10.1007/s40192-021-00205-8 · Cited by 104 · see citing works
Efficient surface defect detection using self-supervised learning strategy and segmentation network
Xu, R., Hao, R., & Huang, B. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101566 · Cited by 95 · see citing works
Cross Position Aggregation Network for Few-Shot Strip Steel Surface Defect Segmentation
Feng, H., Song, K., Cui, W., Zhang, Y., & Yan, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3246519 · Cited by 84 · see citing works
Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks
Tao, X., Wang, Z., Zhang, Z., Zhang, D., Xu, D., Gong, X., & Zhang, L. (2018) · IEEE Transactions on Components Packaging and Manufacturing Technology · doi:10.1109/tcpmt.2018.2794540 · Cited by 79 · see citing works
Luo, Q., Fang, X., Sun, Y., Liu, L., Ai, J., Yang, C., & Simpson, O. (2019) · IEEE Access · doi:10.1109/access.2019.2898215 · Cited by 75 · see citing works
Steel Surface Defect Diagnostics Using Deep Convolutional Neural Network and Class Activation Map
Lee, S. Y., Tama, B. A., Moon, S. J., & Lee, S. (2019) · Applied Sciences · doi:10.3390/app9245449 · Cited by 75 · see citing works
An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection
Yang, L., Song, S., Fan, J., Huo, B., Li, E., & Liu, Y. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3127645 · Cited by 71 · see citing works
A Real-Time Steel Surface Defect Detection Approach With High Accuracy
Wang, W., Mi, C., Wu, Z., Lu, K., Long, H., Pan, B., Li, D., Zhang, J., Chen, P., & Wang, B. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3127648 · Cited by 67 · see citing works
A Robust and Rapid Camera Calibration Method by One Captured Image
Zhang, J., Yu, H., Deng, H., Chai, Z., Ma, M., & Zhong, X. Y. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2884583 · Cited by 65 · see citing works
A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification
Liu, Y., Yuan, Y., Balta, C., & Liu, J. (2020) · Materials · doi:10.3390/ma13204629 · Cited by 62 · see citing works
A Review of Metal Surface Defect Detection Technologies in Industrial Applications
Qiao, Q., Hu, H., Ahmad, A. A., & Wang, K. (2025) · IEEE Access · doi:10.1109/access.2025.3544578 · Cited by 66 · see citing works
Research on Detecting Bearing-Cover Defects Based on Improved YOLOv3
Zheng, Z., Zhao, J., & Li, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3050484 · Cited by 60 · see citing works
Wu, Q., Qin, X., Dong, K., Shi, A., & Hu, Z. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118966 · Cited by 63 · see citing works
Liu, Y., Xiao, H., Xu, J., & Zhao, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3165287 · Cited by 59 · see citing works
An Unsupervised Fault-Detection Method for Railway Turnouts
Guo, Z., Wan, Y., & Ye, H. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.2998863 · Cited by 58 · see citing works
Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
Wang, L., Zhang, D., Guo, J., & Han, Y. (2020) · Applied Sciences · doi:10.3390/app10238660 · Cited by 58 · see citing works
Steel Surface Defect Detection Using GAN and One-Class Classifier
Liu, K., Li, A., Wen, X., Chen, H., & Yang, P. (2019) · International Conference on Automation and Computing (ICAC) · doi:10.23919/iconac.2019.8895110 · Cited by 56 · see citing works
Self-citationMetal surface defect detection based on improved YOLOv5
Zhou, C., Lu, Z., Lv, Z., Meng, M., Tan, Y., Xia, K., Liu, K., & Zuo, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-47716-2 · Cited by 55 · see citing works
Self-citationCao, G., Ruan, S., Peng, Y., Huang, S., & Kwok, N. M. (2018) · IEEE Access · doi:10.1109/access.2018.2842028 · Cited by 54 · see citing works
Zou, Y., & Lan, R. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2942533 · Cited by 55 · see citing works
Liu, Z., Yang, B., Duan, G., & Tan, J. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3001695 · Cited by 52 · see citing works
A New Self-Reference Image Decomposition Algorithm for Strip Steel Surface Defect Detection
Liu, K., Luo, N., Li, A., Tian, Y. Z., Sajid, H., & Chen, H. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2952706 · Cited by 48 · see citing works
Self-citationA Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products
Ibrahim, A. A. M. S., & Tapamo, J. (2024) · Informatics · doi:10.3390/informatics11020025 · Cited by 49 · see citing works
Zhang, J., Wang, H., Tian, Y. Z., & Liu, K. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103231 · Cited by 45 · see citing works
Self-citationUnveiling Patterns: A Study on Semi-Supervised Classification of Strip Surface Defects
Liu, Y., Yang, H., & Wu, C. (2023) · IEEE Access · doi:10.1109/access.2023.3326843 · Cited by 43 · see citing works
Fast and Precise Positioning in PCBs Using Deep Neural Network Regression
Tsai, D., & Chou, Y.-H. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2957866 · Cited by 42 · see citing works
Fast Defect Inspection Based on Data-Driven Photometric Stereo
Ren, M., Wang, X., Xiao, G., Chen, M., & Fu, L. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2858062 · Cited by 41 · see citing works
Detection and classification of surface defects on hot-rolled steel using vision transformers
Vasan, V., Sridharan, N. V., Sugumaran, V., & Aghaei, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38498 · Cited by 44 · see citing works
Liang, Y., Xu, K., & Zhou, P. (2020) · Sensors · doi:10.3390/s20164519 · Cited by 36 · see citing works
Automatic Wear Measurement of Pantograph Slider Based on Multiview Analysis
Lu, S., Liu, Z., Li, D., & Shen, Y. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2997724 · Cited by 34 · see citing works
A deep learning-based approach for the automated surface inspection of copper clad laminate images
Zheng, X., Chen, J., Wang, H., Zheng, S., & Kong, Y. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01877-z · Cited by 32 · see citing works
Self-citationCASPPNet: a chained atrous spatial pyramid pooling network for steel defect detection
Zheng, Z., Hu, Y., Zhang, Y., Yang, H., Qiao, Y., Qu, Z., & Huang, Y. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac68d2 · Cited by 31 · see citing works
Texture surface defect detection of plastic relays with an enhanced feature pyramid network
Huang, F. C., Wang, B., Li, Q., & Zou, J. T. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01864-2 · Cited by 29 · see citing works
Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet
Zhou, S., Wu, S., Liu, H., Lu, Y., & Hu, N. (2018) · Applied Sciences · doi:10.3390/app8091628 · Cited by 28 · see citing works
Surface Defect Detection of Strip-Steel Based on an Improved PP-YOLOE-m Detection Network
Zhang, Y., Liu, X., Guo, J., & Zhou, P. (2022) · Electronics · doi:10.3390/electronics11162603 · Cited by 28 · see citing works
A Review and Benchmark on State-of-the-Art Steel Defects Detection
Chazhoor, A. A. P., Ho, E. S. L., Gao, B., & Woo, W. L. (2023) · SN Computer Science · doi:10.1007/s42979-023-02436-2 · Cited by 36 · see citing works
Iterating Tensor Voting: A Perceptual Grouping Approach for Crack Detection on EL Images
Liu, K., Yan, H., Meng, K., Chen, H., & Sajid, H. (2020) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.2988314 · Cited by 27 · see citing works
Self-citationLSA-Net: Location and shape attention network for automatic surface defect segmentation
Li, W., Li, B., Niu, S., Wang, Z., Wang, M., & Niu, T. (2023) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2023.05.001 · Cited by 26 · see citing works
Wang, D. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11808-0 · Cited by 26 · see citing works
FMV-YOLO: A Steel Surface Defect Detection Algorithm for Real-World Scenarios
He, L., Zheng, L., & Xiong, J. (2025) · Electronics · doi:10.3390/electronics14061143 · Cited by 25 · see citing works
Development of an Improved YOLOv7-Based Model for Detecting Defects on Strip Steel Surfaces
Wang, R., Liang, F., Mou, X., Chen, L., Yu, X., Peng, Z., & Chen, H. (2023) · Coatings · doi:10.3390/coatings13030536 · Cited by 25 · see citing works
Self-citationZheng, X., Liu, W., & Huang, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3349628 · Cited by 25 · see citing works
Automated Product Boundary Defect Detection Based on Image Moment Feature Anomaly
Peng, Y., Ruan, S., Cao, G., Huang, S., Kwok, N. M., & Zhou, S. (2019) · IEEE Access · doi:10.1109/access.2019.2911358 · Cited by 24 · see citing works
Multiple Guidance Network for Industrial Product Surface Inspection With One Labeled Target Sample
Xu, J., & Liu, Y. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3165575 · Cited by 22 · see citing works
Asymmetric Teacher–Student Feature Pyramid Matching for Industrial Anomaly Detection
Zhu, J., Yan, P., Jiang, J., Cui, Y., & Xu, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3338681 · Cited by 21 · see citing works
A Scratch Detection Method Based on Deep Learning and Image Segmentation
Yang, L., Zhou, F., & Wang, L. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3186054 · Cited by 21 · see citing works
An end-to-end learning method for industrial defect detection
Wu, Y., Guo, D., Liu, H., & Huang, Y. (2019) · Assembly Automation · doi:10.1108/aa-08-2018-114 · Cited by 20 · see citing works
Qu, Z., Shen, J., Li, R., Liu, J., & Guan, Q. (2018) · doi:10.1145/3297156.3297190 · Cited by 20 · see citing works
Surface Defect Detection of Heat Sink Based on Lightweight Fully Convolutional Network
Yang, K., Liu, Y., Zhang, S., & Cao, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3188033 · Cited by 19 · see citing works
DuAK: Reinforcement Learning-Based Knowledge Graph Reasoning for Steel Surface Defect Detection
Zhang, Y., Wang, H., Shen, W., & Peng, G. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3307588 · Cited by 19 · see citing works
Self-citationWu, Q., Hu, Z., Qin, X., Huang, B., Dong, K., & Shi, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122225 · Cited by 19 · see citing works
A Novel ST-YOLO Network for Steel-Surface-Defect Detection
Ma, H., Zhang, Z., & Zhao, J. (2023) · Sensors · doi:10.3390/s23229152 · Cited by 22 · see citing works
Steel Sheet Defect Detection Based on Deep Learning Method
Zeng, W., You, Z., Huang, M., Kong, Z., Yu, Y., & Le, X. (2019) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip47338.2019.9012199 · Cited by 17 · see citing works
Wavelet Texture Descriptor for Steel Surface Defect Classification
Belila, D., Khaldi, B., & Aiadi, O. (2024) · Materials · doi:10.3390/ma17235873 · Cited by 16 · see citing works
Inception Dual Network for steel strip defect detection
Liu, Z., Wang, X., & Chen, X. (2019) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2019.8743190 · Cited by 15 · see citing works
Automatic Detection and Quantification of Hot-Rolled Steel Surface Defects Using Deep Learning
Liu, Z., Zeng, Z., Li, J., & Teng, S. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-022-07567-x · Cited by 15 · see citing works
Steel strip surface defect detection based on multiscale feature sensing and adaptive feature fusion
Mi, Z., Gao, Y., Xu, X., & Tang, J. (2024) · AIP Advances · doi:10.1063/5.0196580 · Cited by 14 · see citing works
Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips
Luo, Q., Jiang, W., Su, J., Ai, J., & Yang, C. (2021) · Sensors · doi:10.3390/s21217264 · Cited by 12 · see citing works
Weld defect segmentation and feature extraction from the acquired phased array scan images
Jayasudha, F. V., & Lalithakumari, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13030-8 · Cited by 12 · see citing works
A multiple feature-maps interaction pyramid network for defect detection of steel surface
Zhao, X., Zhao, J., & He, Z. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acb073 · Cited by 11 · see citing works
Prototype-guided domain adaptive one-stage object detector for defect detection
Ye, B., Lai, J., Xie, X., & Zhu, J. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102618 · Cited by 12 · see citing works
Multilayer Feature Extraction of AGCN on Surface Defect Detection of Steel Plates
Zhang, C., Cui, J., & Liu, W. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/2549683 · Cited by 13 · see citing works
A High-Quality Sample Generation Method for Improving Steel Surface Defect Inspection
He, Y., Li, S., Wen, X., & Xu, J. (2024) · Sensors · doi:10.3390/s24082642 · Cited by 10 · see citing works
Human-Guided Data Augmentation via Diffusion Model for Surface Defect Recognition Under Limited Data
Fang, T., Zhang, M., Song, R., Li, X., Wei, Z., & Zhang, W. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3541684 · Cited by 10 · see citing works
Camera calibration method based on circular array calibration board
Chen, H., Zhuang, J., Liu, B., Wang, L., & Zhang, L. (2023) · Systems Science & Control Engineering · doi:10.1080/21642583.2023.2233562 · Cited by 10 · see citing works
Self-citationZhang, C., & Zhang, X. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07990-z · Cited by 10 · see citing works
Sui, T., & Wang, J. (2023) · IEEE Access · doi:10.1109/access.2023.3343566 · Cited by 9 · see citing works
Steel surface defect detection algorithm based on improved YOLOv10
Zhang, L., Wang, Z., Ma, Y., & Li, G. (2025) · Scientific Reports · doi:10.1038/s41598-025-16725-8 · Cited by 9 · see citing works
Zhang, J., Sui, T., Lin, B., Lv, B., Du, H., & Song, N. (2023) · Materials & Design · doi:10.1016/j.matdes.2023.112600 · Cited by 9 · see citing works
Li, Y., He, L., Zhang, M., Cheng, Z., Liu, W., & Wu, Z. (2023) · Electronics · doi:10.3390/electronics12112440 · Cited by 10 · see citing works
High-Frequency Dual-Branch Network for Steel Small Defect Detection
Ma, C., Li, Z., Xue, Y., Li, S., & Sun, X. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09352-4 · Cited by 8 · see citing works
Madhavan, S., Sridharan, N. V., Mahanta, T. K., & Sugumaran, V. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09744-6 · Cited by 8 · see citing works
Effective Bi-decoding networks for rail-surface defect detection by knowledge distillation
Zhou, W., Wu, Y., Qiu, W., Xu, C., & Qiang, F. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112422 · Cited by 7 · see citing works
Zhong, Z., Wang, H., Zhou, C., Yuan, C., Peng, J., & Yue, S. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113798 · Cited by 7 · see citing works
Self-citationAccurate Vision-Based PCB Positioning Using Cosine-Convolutional Neural Networks
Solórzano, C. A. P., & Tsai, D. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3263887 · Cited by 7 · see citing works
Learning-Based Approach for Automated Surface Inspection with Industrial Tomography Imaging
Saied, M., Ali, R., Francis, C., Radilla, G., & Noun, Z. (2024) · Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems · doi:10.1115/1.4065428 · Cited by 7 · see citing works
Research on Surface Defect Detection Method of E-TPU Midsole Based on Machine Vision
Li, R., Tian, F., & Chen, S. (2020) · Journal of Computer and Communications · doi:10.4236/jcc.2020.811011 · Cited by 7 · see citing works
Segmentation of shallow scratches image using an improved multi-scale line detection approach
Jiang, X., Yang, X., Ying, Z., Zhang, L., Pan, J., & Chen, S. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6222-z · Cited by 7 · see citing works
Li, L., Hu, S., Hou, Y., Ke, Y., & Chen, Z. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae08dc · Cited by 6 · see citing works
Li, S., Chen, Z., Zhang, X., Yang, T., & Gong, D. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae140a · Cited by 6 · see citing works
Cylinder Liner Defect Detection and Classification based on Deep Learning
Gao, C., Hao, F., Song, J., Chen, R., Wang, F., & Liu, B. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130818 · Cited by 6 · see citing works
Weighted Matrix Decomposition for Small Surface Defect Detection
Zhong, Z., Wang, H., & Xiang, D. (2022) · Micromachines · doi:10.3390/mi14010092 · Cited by 6 · see citing works
Self-citation
- Authors
- Liu, K., Wang, H., Chen, H., Qu, E., Tian, Y., & Sun, H.
- Affiliations
- Hebei University of Technology (CN)
- Tianjin University (CN)
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2017-10
- Volume
- 66
- Issue
- 10
- Pages
- 2585-2596
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W2736973763
- Cited by
- 161
- References
- 31
- Citations by year
- 2026: 8 · 2025: 21 · 2024: 26 · 2023: 29 · 2022: 20 · 2021: 14 · 2020: 17 · 2019: 14 · 2018: 11 · 2017: 1
- FWCI
- 12.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Funders
- National Natural Science Foundation of China (61403119)
- Natural Science Foundation of Hebei Province (F201402166)
- 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
- steel surface defect detection, unsupervised defect detection, anisotropic diffusion, texture distribution, background extraction, adaptive thresholding
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.
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 ›
Are you an author, or do you see a problem with this page? Request a review
