Unequal deep learning for industrial surface defect detection with various vision morphologies
Year: 2026
Type: article
Abstract: 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.
Institutions: Donghua University, South China Agricultural University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Awards: G4481110984, G5931849682, G6386153180
OpenAlex ID: W7141516088