High-performance surface defect detection of aluminum substrate based on event camera
Αποθηκεύτηκε σε:
| Εκδόθηκε σε: | Journal of Computational Design and Engineering vol. 11, no. 6 (Dec 2024), p. 232 |
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| Κύριος συγγραφέας: | |
| Άλλοι συγγραφείς: | , |
| Έκδοση: |
Oxford University Press
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| Θέματα: | |
| Διαθέσιμο Online: | Citation/Abstract Full Text - PDF |
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| Περίληψη: | Traditional industrial surface defect detection method often employs CCD/CMOS cameras, but they are unable to detect the minute defects on aluminum substrates in highly dynamic industrial scenes due to their nature. Event camera is a novel high-resolution vision sensor that measures per-pixel brightness changes in an asynchronous manner and outputs as event information flow (EIF). Small and weak defects on aluminum substrate can be captured by event camera effectively, but the EIF contains a large amount of noise, making it difficult to perform accurate and high-precision defect detection. To address this problem, we propose a frame aggregation method to realize good event information flow processing, and then use an improved circle detection method to locate the aluminum substrate in each frame, removing abundant event information outside the aluminum substrate. Subsequently, we enhance the event signals under different frames based on optical flow tracking using multiple features, and construct a semi-supervised detector based on pseudo-labels to achieve high-precision defect localization. Finally, considering the small inter-class differences in defects on the surface of aluminum substrates, we construct a defect class corrector based on ensemble learning to enhance the ability to determine defect classes, achieving high-precision automatic quality inspection of aluminum substrate surfaces. The performance of our method is compared with other advanced methods based on event camera data of aluminum substrates in real industrial scenarios. The experimental results show that our method has improved the detection accuracy by ∼10% and the classification accuracy by ∼25% compared to the original state-of-the-art methods. |
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| ISSN: | 2288-5048 |
| DOI: | 10.1093/jcde/qwae097 |
| Πηγή: | Engineering Database |