A Dual-Domain Reverse Distillation Algorithm for Unsupervised Industrial Surface Defect Detection: Application to Non-Woven Fabrics

Industrial surface defect detection faces challenges of complex textures, diverse defect morphologies, and scarce labeled data, especially for non-woven fabrics. This paper proposes a dual-domain reverse distillation algorithm for unsupervised defect detection (DDRD). The algorithm integrates frequency-domain wavelet enhancement and spatial-domain self-attention to enhance defect features synergistically. A Wavelet High-frequency Deformable Enhancement Module amplifies fine-grained defect details, while a Convolutional Self-Attention Spatial Global Enhancement Module captures long-range spatial dependencies. The enhanced dual-domain features are embedded into a reverse distillation framework for end-to-end training, achieving precise defect localization via feature reconstruction errors. Extensive experiments are conducted on the self-built large-scale non-woven fabric WFB dataset and the public MVTec AD benchmark dataset. The results show that DDRD achieves 98.0% pixel-level AUROC and 93.0% image-level AUROC on the WFB dataset, outperforming the state-of-the-art RD4AD method by 0.5% and 1.5%, respectively. On the MVTec AD dataset, it attains an average of 97.5% pixel-level AUROC and 99.7% image-level AUROC, with perfect 100% image-level detection accuracy on multiple categories. These results validate the efficacy and robustness of the dual-domain enhancement strategy for industrial defect detection tasks.