Research on mechanical parts classification and recognition technology based on improved convolutional neural network YOLOv5

Weijia Xu; Yuan Guo

doi:10.1117/12.3008196

14 November 2023 Research on mechanical parts classification and recognition technology based on improved convolutional neural network YOLOv5

Weijia Xu, Yuan Guo

Author Affiliations +

Proceedings Volume 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023); 1293418 (2023) https://doi.org/10.1117/12.3008196
Event: 2023 3rd International Conference on Computer Graphics, Image and Virtualization (ICCGIV 2023), 2023, Nanjing, China

Abstract

In the field of classification and recognition of mechanical parts, in order to solve the difficulties of small target detection, adhesion and overlapping parts classification and recognition, this paper proposes an automatic classification and recognition algorithm of mechanical parts based on improved convolutional neural network YOLOv5. The feature pyramid module PANet is added to the traditional YOLOv5 network structure to strengthen the characterization ability of the network. At the same time, NMS algorithm and image segmentation algorithm are used to improve the detection ability of network boundary segmentation, to improve the detection accuracy and recall rate. The experimental results show that both the traditional YOLOv5 and the improved YOLOv5 in this paper can realize the classification and identification of mechanical parts, and the improved YOLOv5 has better detection precision and accuracy, faster detection speed, and better network robustness.

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

Citation Download Citation

Weijia Xu and Yuan Guo "Research on mechanical parts classification and recognition technology based on improved convolutional neural network YOLOv5", Proc. SPIE 12934, Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023), 1293418 (14 November 2023); https://doi.org/10.1117/12.3008196

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