A dual-structure attention-based multi-level feature fusion network for automatic surface defect detection

The detection of surface defects is crucial to industrial manufacturing. In recent years, numerous detection methods based on computer vision have been successfully applied in the industry. However, industrial defect detection is still full of challenges. In one aspect, most of the industrial defects are extremely small. In another aspect, even though the intra-class defects have numerous similar elements, their outward appearances differ significantly. In this paper, we propose a dual-structure attention-based multi-level feature fusion network (DaMFFN) to address these two issues. In the first attention-based multi-level feature extraction structure, we introduce novel attention pooling to preserve more detailed information about the defective features of the tiny defect by giving certain regions varying weights. In the second attention-based multi-level feature fusion structure, we propose channel attention to capture the defect feature with the greatest potential for discrimination rather than all possible defect features, which is employed to prevent the incorrect detection of intra-class defects. The experiments demonstrate that the detection performance of the DaMFFN is better than other methods in five surface defect datasets.