Abstract
The surface defect detection of aluminum sheet is of great significance to ensure the appearance and quality of aluminum sheet. The surface defects of aluminum sheets have the characteristics of different shapes, obvious size differences, and difficult to obtain defect samples, which make defect detection challenging. In order to solve this problem, we make the following improvements to YOLOv3: Adding attention mechanism modules after the three feature layers output by the model backbone and after neck upsampling; Freezing the model backbone and using pretrained for transfer learning. The proposed YOLOv3 + ECA model is compared with the target detection models such as YOLOv3 and Faster-RCNN. It is found that the mAP of our model reaches 96.22%, which is higher than the current conventional algorithm. The AP values for different types of defects have good detection results.
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Funding
This work was supported by the Science and Technology Project of Jiangxi Provincial Department of Education under Grant no. GJJ200305 and GJJ191689, the Natural Science Foundation of Jiangxi Province under Grants no. 20202BABL202016.
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yang, L., Hu, G., Huang, L. (2023). Surface Defect Detection Algorithm of Aluminum Sheet Based on Improved Yolov3. In: Cao, Y., Shao, X. (eds) Mobile Networks and Management. MONAMI 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-031-32443-7_9
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DOI: https://doi.org/10.1007/978-3-031-32443-7_9
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