Steel surface defect detection algorithm combined with attention mechanism

Abstract

To solve the problem of low detection accuracy of steel surface defects due to background interference and various target shapes, a steel surface defect detection algorithm with attention mechanism is proposed to improve detection accuracy. In view of the small proportion of the target defect area in the overall image and background interference, a two-way attention module (TWA-Block) is proposed to establish the long-distance dependence of the spatial domain and channel domain features. It enhances the contour and texture features of defect area in shallow features, and suppresses the background to a certain extent. The experimental results show that the average accuracy (MAP) of the YOLOv3 model fused with the attention mechanism on the NEU-DET dataset reaches 79.5%, which is 14.4% higher than the YOLOv3 algorithm. Compared with the standard steel surface defect detection methods, the algorithm effectively improves the detection accuracy.