Deep-learning-based small surface defect detection via an exaggerated local variation-based generative adversarial network
Abstract
Surface detection of small defects plays a vital role in manufacturing and has attracted broad interest. It remains challenging primarily due to the small size of the defect relative to the large surface and the rare occurrence of defects. To address this problem, in this article we propose a novel machine vision approach for automatically identifying the tiny flaws that may appear in a single image. First, the presented defect exaggeration approach produces both the flawless image and the corresponding exaggerated version of the defect by taking the variations in the image as regularization terms. Second, a generative adversarial network (GAN) in conjunction with a convolutional neural network (CNN) is proposed to guarantee the accuracy of tiny surface defect detection by producing exaggerated defect image samples. Furthermore, the limited dataset of the training samples for defect detection is enlarged by exploiting the GAN technique with the variation exaggerated images. To evaluate the performance of our proposed method, we conduct comparison experiments between the state-of-the-art techniques with and without the proposed algorithm as well as comparison experiments between the state-of-the-art techniques and our method. The experimental results on different types of surface image samples demonstrate that the proposed method can significantly improve the performance of the state-of-the-art approaches while achieving a defect detection accuracy of 99.2%.
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The LED light source is the most widely used in the field of steel defect recognition.
“Among them, the LED light source is the most widely used in the field of steel defect recognition [40][41][42].”
From Steel Surface Defect Recognition: A Survey (2022) · cites this paper as [40]
GANs are often utilized to generate labelled defective images based on limited labelled samples and sufficient unlabelled samples from industrial sites.
“GANs are often utilized to generate labelled defective images based on the existing limited labelled samples and sufficient unlabelled samples from industrial sites.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [132]
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- Authors
- Lian, J., Jia, W., Zareapoor, M., Zheng, Y., Luo, R., Jain, D. K., & Kumar, N.
- Affiliations
- Shandong Normal University (CN)
- Shanghai Jiao Tong University (CN)
- Qilu University of Technology (CN)
- Chongqing University of Posts and Telecommunications (CN)
- Thapar Institute of Engineering & Technology (IN)
- ORCID
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2020-02
- Volume
- 16
- Issue
- 2
- Pages
- 1343-1351
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2978742590
- Cited by
- 137
- References
- 30
- Citations by year
- 2026: 9 · 2025: 20 · 2024: 27 · 2023: 23 · 2022: 24 · 2021: 19 · 2020: 14 · 2019: 1
- FWCI
- 10.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Natural Science Foundation of China (81871508, 61572300, 61773246)
- Focus on Research and Development Plan in Shandong Province (2019GNC106115)
- Taishan Scholar Program of Shandong Province of China (TSHW201502038)
- China Postdoctoral Science Foundation (2018M630797)
- 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
- generative adversarial networks, data augmentation, convolutional neural networks, machine vision, manufacturing quality inspection
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