STDPNet: a dual-path surface defect detection neural network based on shearlet transform

Defect detection systems based on machine vision have been widely used as an essential part of intelligent manufacturing systems. However, in traditional object detection methods that rely on images as input, differences in defect areas, blurred images, and complex background interference can seriously impair detection accuracy. To meet these challenges, this paper proposed a dual-path neural network based on shearlet transform (STDPNet) by taking advantage of shearlet transform in multi-scale analysis and combining it with the improved object detection algorithm proposed in this paper. First, images are multi-scale and multi-directional decomposed with shearlet transform, and multi-directional sub-band information is input to the detection network instead of image information. Then, this paper proposed a dual-path object detection network for the differences between different frequency bands and introduced a transfer learning strategy between paths to improve the model performance. Finally, the training results on the NEU surface defect public dataset show that the mean average precision of STDPNet achieves 86.81% at a detection speed of 44.45 f/s, which exceeds that of Faster R-CNN by 12%. Experiments on different datasets prove that the accuracy is significantly superior to other models, and the proposed method is more advantageous compared to other models in large, fuzzy, and indistinguishable defect types.

Automated summary

In this paper, a dual-path neural network based on shearlet transform (STDPNet) is proposed for defect detection systems. By utilizing shearlet transform in multi-scale analysis and combining it with an improved object detection algorithm, this method achieves high accuracy and speed in image defect detection.