Enhanced fabric detection network with retained low-level texture features and adaptive anchors

Fabric defect detection is crucial in the textile industry, as it suffers from challenges such as small defect sizes, diverse morphologies, and imbalanced sample distributions. Current mainstream methods approach it as an object detection problem. Many fabric defects, particularly small ones, are caused by production faults that disrupt the fabric texture. These defects often lack distinct structural information, which poses a challenge for object detection methods that rely heavily on such features. To address these limitations, we propose a multi-scale detection channel based on Low-level Texture Feature Retention (LTFR), which significantly enhances the detection capability for small-size defects. Additionally, we introduce Powerful-IoU (PIoU) to guide anchors along effective paths for regression, improving localization accuracy for defects with extreme aspect ratios. To further tackle the issue of imbalanced sample distribution, we adopt the SlideLoss function, which adaptively adjusts the weights of easy and hard samples, offering a more effective solution than the traditional binary cross-entropy classification loss. Experimental results on the publicly available fabric defect dataset from Alibaba Cloud Tianchi show that the model achieves mAP@0.5 and mAP@0.5:0.95 values of 49% and 24%, respectively, which represent improvements of 8.4% and 4.8% over the baseline YOLOv8. Even compared to the state-of-the-art YOLOv9-c, the mAP@0.5 value shows a further improvement of 5.1%. These results confirm the effectiveness of the proposed method and highlight its potential as a valuable guide for researchers in the field of fabric defect detection.