Research on improving the detection method of welding defects in drum welding of CoT-YOLOv7

Wenyuan Hu; Yusheng Zhai; Ruiguang Yun; Qiulai Huang

doi:10.1117/12.3021485

19 February 2024 Research on improving the detection method of welding defects in drum welding of CoT-YOLOv7

Wenyuan Hu, Yusheng Zhai, Ruiguang Yun, Qiulai Huang

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130632D (2024) https://doi.org/10.1117/12.3021485
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

In response to the problem of low efficiency and accuracy in detecting welding defects on the surface of coal mining machine drums, a drum welding defect automatic detection method based on the improved YOLOv7 algorithm is proposed. By utilizing the characteristic of long-distance fusion of multiple feature information in the Transformer model, the global context CoT model is introduced into the YOLOv7 algorithm network, and experimental verification is conducted. The results show that this detection method has high detection accuracy in welding defect detection and can eliminate the image redundancy problem caused by the introduction of attention mechanism. It can be applied to the quality inspection of coal mining machine drum welding products.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenyuan Hu, Yusheng Zhai, Ruiguang Yun, and Qiulai Huang "Research on improving the detection method of welding defects in drum welding of CoT-YOLOv7", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130632D (19 February 2024); https://doi.org/10.1117/12.3021485

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