A laser welding defect detection model based on improved YOLOv5

Yatao Yang; Yunhao Zhou; Junqing Li; Yunjie He; Yanfeng Chen; Li Zhang

doi:10.1117/12.3003782

11 October 2023 A laser welding defect detection model based on improved YOLOv5

Yatao Yang, Yunhao Zhou, Junqing Li, Yunjie He, Yanfeng Chen, Li Zhang

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128002L (2023) https://doi.org/10.1117/12.3003782
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

With the expanding market demand for new energy power batteries, the industrial requirement for detecting laser welding defects of lithium battery’s pole are also increasing. Since the traditional machine vision defect detection has the problems of low detection accuracy and slow speed, this paper proposes a laser welding defect detection model based on the improved YOLOv5 algorithm to meet the requirements of industrial production. First, all C3 modules of the backbone was substituted for the newly proposed PConv module, which not only reduces the amount of redundant computation, improves the detection speed, but also increases the feature extraction efficiency of the backbone network for input images; second, we add the attention mechanism SE to the penultimate layer of the backbone network, which enhances the useful features of the backbone network for input images without additionally increasing the computation. This approach enhances the backbone network's ability to extract useful features from the input images and improves the accuracy of the model. The experimental results show that the improved model improves the average detection accuracy by 1.1% to 97% compared with the original YOLOv5 model. The FPS reaches 333, which can meet the current industrial demand for real-time defect detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yatao Yang, Yunhao Zhou, Junqing Li, Yunjie He, Yanfeng Chen, and Li Zhang "A laser welding defect detection model based on improved YOLOv5", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128002L (11 October 2023); https://doi.org/10.1117/12.3003782

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KEYWORDS

Defect detection

Laser welding

Detection and tracking algorithms

Feature extraction

Batteries

Performance modeling

Target detection

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