X-ray weld defect detection based on data augmentation and improved YOLO V7

Baizhen Li; Zhenhuai Ma; Tianyu Qi; Quancheng Dong; Kexin Hu

doi:10.1117/12.3038335

5 August 2024 X-ray weld defect detection based on data augmentation and improved YOLO V7

Baizhen Li, Zhenhuai Ma, Tianyu Qi, Quancheng Dong, Kexin Hu

Proceedings Volume 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024); 132261O (2024) https://doi.org/10.1117/12.3038335
Event: 3rd International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 2024, Changsha, China

Abstract

X-ray inspection for weld defects is very important for the welding industry, but insufficient defect samples restrict the implementation of deep learning technology in this field. This paper proposes a strategy combining supervised and unsupervised data augmentation to solve this problem. DCGAN is optimized to generate synthetic defect images of appropriate resolution to expand the number of datasets. The E-ELAN structure of YOLOV7 is optimized to improve its detection accuracy. CBAM is integrated into different network models to improve their detection performance of X-ray weld defects. The experiments show that the scheme of “Improved YOLOV7 and CBAM” has the best detection performance, and its mAP is 95.57%.

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

Citation Download Citation

Baizhen Li, Zhenhuai Ma, Tianyu Qi, Quancheng Dong, and Kexin Hu "X-ray weld defect detection based on data augmentation and improved YOLO V7", Proc. SPIE 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 132261O (5 August 2024); https://doi.org/10.1117/12.3038335

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