Research on steel surface defect detection based on YOLOv5

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.