Research on steel surface defect detection based on YOLOv5

Chi Zhang; Ying Chen; Weimin Qi; Xinyi Huang

doi:10.1117/12.2681126

8 June 2023 Research on steel surface defect detection based on YOLOv5

Chi Zhang, Ying Chen, Weimin Qi, Xinyi Huang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270715 (2023) https://doi.org/10.1117/12.2681126
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Aiming at the problems of low detection efficiency and poor detection accuracy of traditional steel surface defect detection methods, this paper proposes a steel surface defect detection algorithm based on improved YOLOv5 network. First, the lightweight GhostNet network is introduced, and the Ghost module is used to optimize the YOLOv5 backbone feature extraction network to obtain a lightweight model to reduce the complexity of the model and improve the speed of crack detection; Then, the efficient CA (Coordinate Attention) channel attention mechanism is fused at the model prediction output to further enhance the defect feature extraction ability and improve the model detection performance. The simulation results show that the model volume of this method is reduced by 10.3% and the mAP value is increased by 4.7% compared with the existing YOLOv5 algorithm. Compared with traditional steel surface defect detection methods, the proposed algorithm can detect the types and locations of steel surface defects more accurately and quickly, and has a smaller model volume, which is convenient for deployment in mobile terminals.

Citation Download Citation

Chi Zhang, Ying Chen, Weimin Qi, and Xinyi Huang "Research on steel surface defect detection based on YOLOv5", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270715 (8 June 2023); https://doi.org/10.1117/12.2681126

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KEYWORDS

Performance modeling

Defect detection

Convolution

Feature extraction

Detection and tracking algorithms

Education and training

Target detection

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