Research on real-time detection model of chainsaw chain based on improved YOLOv5s

Jingwen Chen; Lingzhi Xu; Yu Li

doi:10.1117/12.2684206

1 August 2023 Research on real-time detection model of chainsaw chain based on improved YOLOv5s

Jingwen Chen, Lingzhi Xu, Yu Li

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541V (2023) https://doi.org/10.1117/12.2684206
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Real-time defect detection on industrial production lines is a goal pursued by manufacturing companies. Currently, traditional visual inspection methods are still used for defect detection on chainsaw chain production lines, which suffer from long inspection times, high missed detection rates, and high costs. In order to solve the problem of defect detection in the production process, this paper proposes a method based on an improved YOLOv5s algorithm model, and designs a new YOLOv5s-GBC model to improve detection speed and accuracy. A cost-effective convolution method is introduced to replace the ordinary convolution in the YOLOv5s backbone network, reducing the computational cost of feature extraction; Then, an additional weight is automatically calculated based on the importance of different feature layers in the Neck section, and the fusion effect of semantic information at different levels is enhanced by deleting nodes and adding input edges; Finally, a mixed attention mechanism is added to the minimum size feature output end to make the network more focused on the feature information of small targets, improving the accuracy of model regression and prediction. Through experiments, the YOLOv5s-GBC model achieves mAP of 97.4%, F1-score of 91.9%, FLOPs of 15.9G, and weight of 13.4Mb on the chainsaw chain product test dataset. The mAP and F1-score of the YOLOv5s-GBC model are 3.1% and 2.8% higher, respectively, than those of the YOLOv5s model, and FLOPs and weight are both reduced by 0.4.

Citation Download Citation

Jingwen Chen, Lingzhi Xu, and Yu Li "Research on real-time detection model of chainsaw chain based on improved YOLOv5s", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541V (1 August 2023); https://doi.org/10.1117/12.2684206

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KEYWORDS

Object detection

Manufacturing

Defect detection

Neck

Convolution

Industry

Inspection

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