The Prior Model-Guided Network for Bearing Surface Defect Detection

Surface defect detection is a key task in industrial production processes. However, the existing methods still suffer from low detective accuracy to pit and small defects. To solve those problems, we establish a dataset of pit defects and propose a prior model-guided network for defect detection. This network is composed of a segmentation network with a weight label, classification network, and pyramid feature fusion module. The segmentation network as the prior model can improve the accuracy of the classification network. The weight label with center distance transformation can reduce the label cost of the segmentation network. The pyramid feature fusion module can adapt defects of different scales and avoid information loss of small defects. The comparison experiments are implemented to identify the performance of the proposed network. Ablation experiments are designed to specify the effectiveness of every module. Finally, the network is performed on a public dataset to verify its robustness. Experimental results reveal that the proposed method can effectively identify pit defects of different scales and improve the accuracy of defect detection. The accuracy can reach 99.3%, which is increased by 2 similar to 5% compared with other methods, revealing its excellent applicability in automatic quality inspection of industrial production.