Surface Defect Target Identification on Copper Strip Based on Adaptive Genetic Algorithm and Feature Saliency
Open accessAbstract
To enhance the stability and robustness of visual inspection system (VIS), a new surface defect target identification method for copper strip based on adaptive genetic algorithm (AGA) and feature saliency is proposed. First, the study uses gray level cooccurrence matrix (GLCM) and HU invariant moments for feature extraction. Then, adaptive genetic algorithm, which is used for feature selection, is evaluated and discussed. In AGA, total error rates and false alarm rates are integrated to calculate the fitness value, and the probability of crossover and mutation is adjusted dynamically according to the fitness value. At last, the selected features are optimized in accordance with feature saliency and are inputted into a support vector machine (SVM). Furthermore, for comparison, we conduct experiments using the selected optimal feature subsequence (OFS) and the total feature sequence (TFS) separately. The experimental results demonstrate that the proposed method can guarantee the correct rates of classification and can lower the false alarm rates.
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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 [91]
The adaptive genetic algorithm and the hybrid chromosome genetic algorithm were proposed to enhance visual feature selection.
“To enhance the visual feature selection, the adaptive genetic algorithm (AGA) [91] and the hybrid chromosome genetic algorithm (HCGA) [67] were proposed.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [91]
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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
Song, K., & Yan, Y. (2013) · Mathematical Problems in Engineering · doi:10.1155/2013/429094 · Cited by 44 · see citing works
- Authors
- Zhang, X., Li, W., Xi, J., Zhang, Z., & Fan, X.
- Affiliations
- Hohai University (CN)
- Corresponding author
- Zhang, Xuewu
- Venue
- Mathematical Problems in Engineering
- Publisher
- Wiley
- Published
- 2013
- Volume
- 2013
- Pages
- 1-10
- ISSN
- 1024-123X (ISSN-L)
- 1563-5147
- OpenAlex
- W2128100226
- Cited by
- 11
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2022: 1 · 2021: 1 · 2020: 2 · 2016: 1 · 2015: 1 · 2014: 1 · 2013: 1
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Mathematical Problems in Engineering (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (61273170)
- 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
- copper strip, surface defect detection, visual inspection, adaptive genetic algorithm, feature selection, feature saliency, gray level co-occurrence matrix, Hu moments, support vector machine, false alarm reduction
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