Fabric defect detection via Explicit De-Background

Fabric defect detection under complex backgrounds faces challenges like high interference, false positives, and lack of robustness. To address these issues, a frequency-domain Explicit De-Background method is proposed to separate background from defects and enhance defect focus. The network uses a pixel-level De-background Layer to suppress noise and emphasize defects after extracting multi-scale features with Swin Transformer. This layer includes the Frequency-domain Background Extraction Module (F-BEM) and the Background Suppression Unit (BSU): F-BEM leverages Fourier amplitude to capture global background features, while BSU suppresses them to produce a difference map that accentuates defect regions. The De-Background Attention (DBA) module leverages the difference map as a weighting matrix to enhance spatial focus on defect features while minimizing background interference. To integrate multi-scale information, the Feature Cross-Shrinking Decoder (FCSD) progressively fuses adjacent layers via Cross Aggregation Nodes(CAN), ensuring semantic consistency, reducing redundancy, and mitigating information loss and gradient vanishing for precise defect segmentation. Our method enhances robustness and accuracy in complex backgrounds using explicit background separation and multi-stage feature processing. It surpasses state-of-the-art techniques on fabric defect datasets and shows good generalization in complex and transfer learning scenarios, providing a practical solution for industrial defect detection.

Authors

• Yuntao Chen
• Hao Liu The Hong Kong University of Science and Technology (Guangzhou) The Hong Kong University of Science and Technology
• Jiuzhen Liang

Keywords

• Background Suppression Unit
• De-background
• Fabric defect detection
• Feature Cross-Shrinking Decode