Lightweight Surface Defect Detection Method of Steel

doi:10.3901/JME.2025.08.018

Abstract

Abstract: Aiming at the problems of low detection efficiency, poor detection accuracy and poor real-time performance of traditional steel surface defect detection methods, An algorithm for surface defect identification of lightweight steel based on improved YOLOv5 is proposed, which is suitable for the engineering deployment of hardware equipment with low cost, low computational power and low memory. Firstly, K-means++ algorithm is used to cluster anchor boxes in the NEU-DET dataset to optimize the matching degree between the prior box and the ground-truth box. Then, by improving the activation function and loss function, HardSwish activation function reduces the computational cost and improves the stability at the same time, while SIoU loss function can effectively accelerate the convergence speed of the network. Secondly, in order to extract rich details of the target region, the CA coordinate attention module is added on the basis of the original YOLOv5 algorithm. Furthermore, the improved Repvgg block is introduced by referring to the structure reparameterization, and the number of model channels is halved to further increase the engineering deployability of the model. Finally, through ablation experiment and a series of comparative experiments, the performance of the proposed algorithm is proved to be superior. Compared with the original YOLOv5 algorithm, the number of parameters is reduced by about 73.6%, the floating point calculation is reduced by about 72.3%, and the mAP value is increased by 1.5%. The results provide a new method and idea for the fine inspection of steel surface defects and have practical significance for improving the quality of steel products.

Key words: steel surface defect, deep learning, light weight, YOLOv5, convolutional neural network

CLC Number:

TP29

WANG Qi, YE Renchuan, MA Guojie, MA Peijue, FAN Jie, YANG Wenlong. Lightweight Surface Defect Detection Method of Steel[J]. Journal of Mechanical Engineering, 2025, 61(8): 18-31.

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