The Strip Steel Surface Defect Recognition Based on Multiple Support Vector Hyper-Sphere with Feature and Sample Weights
Abstract
For strip steel surface defect dataset with abnormal samples and weakly relevant features, a novel multi-class classification method is realized in this paper. Firstly, weight parameter representing the importance degree of sample is obtained by calculating the clustering information of samples in the training dataset. Then, weight parameter representing the importance degree of feature is obtained by utilizing sparse important samples dataset and Relief-F algorithm. After that, a multiple support vector hyper-sphere with feature and sample weights (FSW-MSVH) is proposed. The novel classifier can realize multi-class classification. Finally, the multi-class classifier is used to do classification experiments for eight classes of strip steel surface defect. Experimental results show that FSW-MSVH classifier can greatly improve classification accuracy and speed.
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Optimized filters are effective not only in filtering mixed noise, but also in maintaining edge details.
“The optimized filters have been very effective not only in filtering mixed noise, but also in maintaining edge details.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [39]
Chu's team came up with a series of SVM variants: enhanced least squares twin SVM, multiple support vector hypersphere with feature and sample weights, machine learning with quantile hyperspheres, and multiple hyperspheres vector machine with additional information.
“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].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [39]
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Tang, B., Chen, L., Sun, W. M., & Lin, Z. (2022) · IET Image Processing · doi:10.1049/ipr2.12647 · Cited by 298 · see citing works
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Self-citationA multi-class classifier based on support vector hyper-spheres for steel plate surface defects
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- Authors
- Gong, R., Wu, C., Chu, M., & Liu, X.
- Affiliations
- University of Science and Technology Liaoning (CN)
- Northeastern University (CN)
- Lakehead University (CA)
- Corresponding author
- Gong, Rongfen
- Venue
- steel research international
- Publisher
- Wiley
- Published
- 2016-04-25
- Volume
- 87
- Issue
- 12
- Pages
- 1678-1685
- ISSN
- 1611-3683 (ISSN-L)
- OpenAlex
- W2343595925
- Cited by
- 7
- References
- 6
- Citations by year
- 2024: 1 · 2022: 1 · 2020: 3 · 2019: 1 · 2017: 1
- FWCI
- 0.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- steel research international (publishedVersion)
- Funders
- University of Science and Technology Liaoning
- 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 › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- sample weighting, feature weighting, multi-class classification, sample clustering
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