Application of multi-scale feature extraction to surface defect classification of hot-rolled steels
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Wavelet transform has difficulty choosing a wavelet basis.
“Difficulty in choosing wavelet basis.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [100]
SVM is a generalized linear classifier for binary classification and has been widely used for defect classification of flat steel surfaces.
“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].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [100]
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Fast Discrete Curvelet Transforms
Candès, E., Demanet, L., Donoho, D., & Ying, L. (2006) · Multiscale Modeling & Simulation · doi:10.1137/05064182x · Cited by 2,573 · 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
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
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
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
Application of RNAMlet to surface defect identification of steels
Xu, K., Xu, Y., Zhou, P., & Wang, L. (2018) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2018.01.010 · Cited by 66 · see citing works
Self-citationClassification of steel samples by laser-induced breakdown spectroscopy and random forest
Zhang, T., Xia, D., Tang, H., Yang, X., & Li, H. (2016) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2016.07.001 · Cited by 60 · see citing works
Application of Shearlet transform to classification of surface defects for metals
Xu, K., Liu, S., & Ai, Y. (2015) · Image and Vision Computing · doi:10.1016/j.imavis.2015.01.001 · Cited by 55 · see citing works
Self-citationSong, K.-C., Hu, S.-P., Yan, Y.-H., & Li, J. (2014) · ISIJ International · doi:10.2355/isijinternational.54.2598 · Cited by 41 · see citing works
- Authors
- Xu, K., Ai, Y.-H., & Wu, X.-Y.
- Affiliations
- University of Science and Technology Beijing (CN)
- Corresponding author
- Xu, Ke
- ORCID
- Xu, Ke
- Venue
- International Journal of Minerals, Metallurgy, and Materials
- Published
- 2013-01
- Volume
- 20
- Issue
- 1
- Pages
- 37-41
- ISSN
- 1005-8850 (ISSN-L)
- 1674-4799
- 1869-103X
- OpenAlex
- W1969361207
- Cited by
- 40
- References
- 1
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 7 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 4 · 2018: 1 · 2017: 2 · 2016: 5 · 2015: 2 · 2014: 1 · 2013: 2
- FWCI
- 7.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Minerals Metallurgy and Materials (publishedVersion)
- Funders
- Program for New Century Excellent Talents in University
- 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 › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- hot-rolled steel, surface defect classification, multi-scale feature extraction, multiscale geometric analysis, curvelet transform, graph embedding, support vector machine
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