Research on surface microdefect detection method for metal parts based on YOLOv5

Biao Xiao

doi:10.1117/12.3048482

12 November 2024 Research on surface microdefect detection method for metal parts based on YOLOv5

Biao Xiao

Author Affiliations +

Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133951Q (2024) https://doi.org/10.1117/12.3048482
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China

Abstract

To improve the accuracy of identifying non-conforming parts and improve production efficiency, this paper proposes an intelligent identification method for micro defects in metal parts based on the YOLOv5 algorithm. This method first preprocesses the collected images and then extracts part features by enhancing fixed features, extracting image defect feature points, and training the YOLO network. Then, based on the YOLOv5 algorithm, an intelligent recognition model is established to achieve intelligent recognition of micro defects in parts. The experimental results show that the detection accuracy of this method is higher than 93.9%, with an average detection time of 2.57ms, which is better than the comparison method and has an ideal detection effect.

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

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Biao Xiao "Research on surface microdefect detection method for metal parts based on YOLOv5", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133951Q (12 November 2024); https://doi.org/10.1117/12.3048482

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KEYWORDS

Detection and tracking algorithms

Metals

Feature extraction

Education and training

Defect detection

Data modeling

Image processing

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