Learning a Class-specific and Shared Dictionary for Classifying Surface Defects of Steel Sheet
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An approach to a class-specific and shared dictionary learning (CDSDL) for sparse representation is proposed to classify surface defects of steel sheet. The proposed CDSDL algorithm is modelled as a unified objective function, covering reconstructive error, sparse and discriminative promotion constraints. With the high-quality dictionary, the compact, reconstructive and discriminative feature representation of an image can be extracted. Then the classification can be efficiently performed by discriminative information obtained from the reconstructive error or the sparse vector. Based on a dataset of surface images captured from a practical steel production line, the CDSDL algorithm is carried out to verify its effectiveness. Experimental results indicate that the CDSDL algorithm is more effective in classifying surface defects of steel sheet than other algorithms.
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Testing results prove that Zhou et al.'s scheme improved classification performance efficiently due to discriminative information obtained from the reconstructive error or the sparse vector.
“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.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [114]
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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
- Authors
- Zhou, S., Chen, Y., Zhang, D., Xie, J., & Zhou, Y.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Venue
- ISIJ International
- Publisher
- Iron and Steel Institute of Japan
- Published
- 2017
- Volume
- 57
- Issue
- 1
- Pages
- 123-130
- ISSN
- 0915-1559 (ISSN-L)
- 1347-5460
- OpenAlex
- W2552846726
- Cited by
- 9
- References
- 8
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 1 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 1
- FWCI
- 0.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- ISIJ International (publishedVersion)
- Funders
- National Natural Science Foundation of China (51174151)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- dictionary learning, sparse representation, surface defect classification, steel sheet, discriminative representations, industrial inspection
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