Real-Time Steel Inspection System Based on Support Vector Machine and Multiple Kernel Learning
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Median filters have the best performance on the suppression of salt and pepper noise, and Gaussian filter and Wiener filter have better effects on Gaussian noise.
“Among them, it is proved that the median filters have the best performance on the suppression of salt and pepper noise, and the Gaussian filter and Wiener filter have better effects on Gaussian noise.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [53]
Gray-level Co-occurrence Matrix can reflect the comprehensive distribution characteristic of the image gray level, such as direction, adjacent interval, and change range.
“Can reflect the comprehensive distribution characteristic of the image gray level, such as direction, adjacent interval, change range, etc.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [53]
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Cites
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Multiple kernel learning, conic duality, and the SMO algorithm
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The SHOGUN Machine Learning Toolbox
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An intelligent real-time vision system for surface defect detection
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Automatic inspection of metallic surface defects using genetic algorithms
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Review of surface defect detection of steel products based on machine vision
Tang, B., Chen, L., Sun, W. M., & Lin, Z. (2022) · IET Image Processing · doi:10.1049/ipr2.12647 · Cited by 298 · see citing works
Self-citationAutomated 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
Infrared Thermal Imaging-Based Crack Detection Using Deep Learning
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An end-to-end steel surface defect detection approach via Swin transformer
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Self-citationAn adaptive weighting multimodal fusion classification system for steel plate surface defect
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Compact and Efficient Feature Representation for Defect in hot rolled strip steel:A postion paper
Su, J., Luo, Q., & Yang, C. (2022) · Asian Control Conference (ASCC) · doi:10.23919/ascc56756.2022.9828012
- Authors
- Chen, Y., Chen, L., Liu, X., Ding, S., & Zhang, H.
- Affiliations
- Wuhan University of Science and Technology (CN)
- Corresponding author
- Chen, Li
- Venue
- Advances in Intelligent and Soft Computing
- Publisher
- Springer Berlin Heidelberg
- Published
- 2011
- Pages
- 185-190
- ISSN
- 1867-5662 (ISSN-L)
- 1867-5670
- OpenAlex
- W21721183
- Cited by
- 9
- References
- 11
- Citations by year
- 2023: 1 · 2022: 3 · 2020: 1 · 2019: 2 · 2018: 1 · 2017: 1
- FWCI
- 0.00
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Advances in intelligent and soft computing (publishedVersion)
- 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, real-time inspection, support vector machine, multiple kernel learning, machine vision, defect classification
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