Feature purification fusion structure for fabric defect detection

Fabric defect detection is an important part of the textile industry to ensure product quality. To solve the problems such as the difficulty of detecting small defects and the coexistence of multi-scale defects in fabric defect detection, a fabric defect detection method based on the feature purification fusion structure is proposed in this paper. Specifically, we improve the feature extraction network to enhance the network's ability to focus on small defective features and effectively reduce the model parameters. The existing methods use direct fusion between multi-level feature maps, which will lead to feature confusion. Therefore, we propose the feature purification fusion structure (FPF), which includes the semantic information supplementation strategy (SIS) and the detail information supplementation strategy (DIS). SIS extracts valid information from the deep feature map and supplements it to the shallow feature map, weakening feature information irrelevant to the shallow feature map detection task. DIS adaptively supplements the feature information required by the deep feature map detection task from the shallow feature map. FPF improves the ability of the network to detect small defects and effectively mitigates the aliasing effect generated during feature fusion. The experimental results show that compared to the baseline model YOLOv5s algorithm, our model achieved a 6.8% improvement in detection accuracy, with an average inference frame rate of 37.6 FPS, demonstrating better detection performance in fabric defect detection. Furthermore, extending this model to aluminum profile defect datasets also demonstrates strong performance.