Unsupervised Fabric Defect Detection Framework Based on Knowledge Distillation

Haotian Liu, Siqi Wang, Chang Meng, Hengyu Zhang, Xianjing Xiao, Xiu Li

Abstract: Fabric defect detection is a critical task in the textile industry. Efficient and accurate automated detection schemes, such as computer vision fabric quality inspection, are urgently needed. However, traditional feature-based methods are often limited and difficult to implement universal solutions in industrial scenarios due to their specificity towards certain defect types or textures. Meanwhile, machine learning methods may face difficulties in harsh industrial production environments due to insufficient data and labels. To address these issues, we propose an unsupervised defect detection framework based on knowledge distillation, which includes a visual localization module to assist with the detection task. Our approach significantly improves classification and segmentation accuracy compared to previous unsupervised methods. Besides, we perform a comprehensive set of ablation experiments to determine the optimal values of different parameters. Furthermore, our method demonstrates promising performance in both open databases and real industrial scenarios, highlighting its high practical value.