Unequal deep learning for industrial surface defect detection with various vision morphologies

Surface defect detection is indispensable for managing product quality in industrial manufacturing. However, owing to the various defect morphologies, it remains difficult to improve accuracy in cross-domain surface defect detection. This paper proposes a defect-aware unequal network (DAU-Net) for detecting industrial surface defects with multi-morphology. In DAU-Net, the defect-aware global representation module is developed to overcome the limitation of local convolution for modeling large-scale surface defects. The defect-aware interactive computation strategy is designed to ensure complete aggregation of surface defect by perceive pixels of patches highly-correlated to surface defect. Moreover, an unequal loss is introduced to weight the regression error of important estimated keypoints, strengthening the model’s focus on learning highly-related bounding boxes of surface defects during training. The proposed DAU-Net is comprehensive evaluated on three cross-domain surface defect datasets, achieving better detection accuracy with multi-morphology surface defects compared with four state-of-the-art methods.