A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM
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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.
“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.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [59]
Otsu is a classical adaptive threshold method for separating defects from the background in flat steel images, obtaining a threshold value based on the characteristic of the large variance between the background and foreground.
“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.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [59]
Zhang et al. succeeded in identifying seven classes of steel surface defects effectively based on a multiclass SVM by simultaneously optimizing kernel function selection and parameter settings.
“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.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [59]
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Cites
An intelligent real-time vision system for surface defect detection
Jia, H., Murphey, Y. L., Shi, J., & Chang, T.-S. (2004) · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · doi:10.1109/icpr.2004.1334512 · Cited by 174 · see citing works
Surface defects inspection of cold rolled strips based on neural network
Kang, G.-W., & Liu, H.-B. (2005) · 2005 International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2005.1527830 · Cited by 62 · see citing works
Feature extraction algorithm based on adaptive wavelet packet for surface defect classification
Lee, C. S., Choi, C.-H., Choi, J. Y., Kim, Y. K., & Choi, S. H. (2002) · Proceedings of 3rd IEEE International Conference on Image Processing · doi:10.1109/icip.1996.560968 · Cited by 30 · see citing works
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Song, K., & Yan, Y. (2013) · Applied Surface Science · doi:10.1016/j.apsusc.2013.09.002 · Cited by 1,026 · 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
Classification of surface defects on steel sheet using convolutional neural networks
Zhou, S., Chen, Y., Zhang, D., Xie, J., & Zhou, Y. (2017) · Materiali in tehnologije · doi:10.17222/mit.2015.335 · Cited by 130 · see citing works
Ai, Y.-H., & Xu, K. (2013) · Journal of Iron and Steel Research International · doi:10.1016/s1006-706x(13)60102-8 · Cited by 53 · see citing works
Image data assessment approach for deep learning-based metal surface defect-detection systems
Lin, H.-I., & Wibowo, F. S. (2021) · IEEE Access · doi:10.1109/access.2021.3068256 · Cited by 43 · see citing works
Automated surface inspection for steel products using computer vision approach
Xi, J., Shentu, L., Hu, J., & Li, M. (2017) · Applied Optics · doi:10.1364/ao.56.000184 · Cited by 40 · see citing works
Surface defect identification of aluminium strips with non-subsampled shearlet transform
Liu, X., Xu, K., Zhou, P., Zhou, D., & Zhou, Y. (2020) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.105986 · Cited by 24 · see citing works
Liu, X., Xu, K., Zhou, D., & Zhou, P. (2020) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-020-00720-5 · Cited by 9 · see citing works
- Authors
- Xue-wu, Z., Yan-qiong, D., Yan-yun, L., Ai-ye, S., & Rui-yu, L.
- Affiliations
- Hohai University (CN)
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2011-05
- Volume
- 38
- Issue
- 5
- Pages
- 5930-5939
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2044342579
- Cited by
- 188
- References
- 3
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 16 · 2023: 19 · 2022: 18 · 2021: 21 · 2020: 19 · 2019: 13 · 2018: 12 · 2017: 7 · 2016: 12 · 2015: 9 · 2014: 5 · 2013: 10 · 2012: 2
- FWCI
- 13.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- Funders
- National Natural Science Foundation of China (60872096)
- 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
- surface defect detection, image-based inspection, support vector machine, multi-class classification, wavelet smoothing, Otsu thresholding, spectral properties, kernel selection, image preprocessing
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