Research on a Lightweight Textile Defect Detection Algorithm Based on WSF-RTDETR

Textile defect detection technology has become a core component of industrial quality control. With the advancement of artificial intelligence technologies, an increasing number of intelligent recognition methods are being actively researched and deployed in the textile defect detection. To further improve detection accuracy and quality, we propose a new lightweight process named WSF-RTDETR with reduced computational resources. Firstly, we integrated WTConv convolution with residual blocks to form a lightweight WTConv-Block module, which could enhance the capability of capturing detailed features of tiny defective targets while reducing computational overhead. Subsequently, a lightweight slimneck-SSFF feature fusion architecture was constructed to enhance the feature fusion performance. In addition, the Focaler–MPDIoU loss function was presented by incorporating dynamic weight adjustment and multi-scale perception mechanism, which could improve the detection accuracy and convergence speed for tiny defective targets. Finally, we conducted experiments on a textile defect dataset to further validate the effectiveness of the WSF-RTDETR model. The results demonstrate that the model improves mean average precision (mAP50) by 4.71% while reducing GFLOPs and the number of parameters by 24.39% and 31.11%, respectively. The improvements in both detection performance and computational efficiency would provide an effective and reliable solution for industrial textile defect detection.