A new method of surface defect detection of steel ball based on pre-trained YOLOv4 model

Yiwen Wang; Kaijiao Wang; Lijie Zhou; Yun Chen; Pengfei Li

doi:10.1117/12.2604531

5 October 2021 A new method of surface defect detection of steel ball based on pre-trained YOLOv4 model

Yiwen Wang, Kaijiao Wang, Lijie Zhou, Yun Chen, Pengfei Li

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119111A (2021) https://doi.org/10.1117/12.2604531
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

The surface quality of steel ball is one of the most critical assessment indicators for evaluating the quality of steel ball. Therefore, a new method is presented in this study which uses the pre-trained YOLOv4 model based on the deep transfer learning to detect the surface defect of steel ball. The method could automatically extract features from surface detection of steel ball and accurately detect the surface defects of steel ball. The experimental results show that IOU and MAP are 0.9325 and 0.9137 respectively, which are better than other methods in the previous studies.

Citation Download Citation

Yiwen Wang, Kaijiao Wang, Lijie Zhou, Yun Chen, and Pengfei Li "A new method of surface defect detection of steel ball based on pre-trained YOLOv4 model", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119111A (5 October 2021); https://doi.org/10.1117/12.2604531

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