Paper
11 October 2023 A strip steel surface defect detection algorithm based on YOLOv5s
Ping Zhao, YongXia Zhou, Wei Sheng, JunJie Chen
Author Affiliations +
Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 1280006 (2023) https://doi.org/10.1117/12.3004053
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China
Abstract
It is very dangerous to put the strip containing defects into use, and such defects are often highly susceptible to be misdiagnosed and missed in the actual inspection process due to the small difference from the background and large scale variation. Therefore, an improved surface defect detection algorithm based on YOLOv5s is proposed. Firstly, combined the idea of DenseNet network, a new C3 structure for the backbone network of the model is obtained by adding the convolution module inside the residual component and making multi-level dense connection to enhance feature reuse. Secondly, in the neck network, the original feature pyramid module is replaced by Generalized-FPN (GFPN) to achieve the full feature fusion. Finally, the experiments on NEU-DET show that the mean average precision (mAP) of the improved algorithm is 79.8%, and meets the needs of practical industrial detection.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ping Zhao, YongXia Zhou, Wei Sheng, and JunJie Chen "A strip steel surface defect detection algorithm based on YOLOv5s", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 1280006 (11 October 2023); https://doi.org/10.1117/12.3004053
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KEYWORDS
Detection and tracking algorithms

Defect detection

Feature fusion

Object detection

Target detection

Neck

Algorithm development

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