Improved YOLOv9 with Dual Convolution and LSKA Attention for Robust Small Defect Detection in Textiles

To mitigate the challenges of false positives and undetected small-scale defects in fabric inspection, this study proposes an advanced fabric defect detection system that leverages an optimized YOLOv9 algorithm. First, redundant computations are reduced by introducing DualConv to replace standard convolution. Second, the LSKA attention mechanism is incorporated to increase the weight of important features, thereby enhancing the accuracy of small target detection and improving the generalization ability. Additionally, the focal modulation network is employed to replace the fast spatial pyramid module, mitigating the loss of detailed information caused by the feature pooling operation. Furthermore, the conventional feature pyramid network is replaced with bidirectional feature pyramid network, which is utilized for efficient feature fusion, thereby enhancing multiscale feature representation and improving detection accuracy. Finally, the bounding box loss function is optimized by introducing the shape-IoU loss function, which facilitates more rapid model convergence and significantly improves detection accuracy. Experiments conducted on a fabric defect dataset demonstrate that the proposed algorithm yields a 6.7% increase in mAP@0.5 and a 14.7% improvement in mAP@0.5–0.95, while simultaneously reducing the model’s total parameters by 17.8% and computational FLOPs by 14.4%, compared with those of the original algorithm. The improved YOLOv9 model significantly enhances the precision and accuracy of defect detection while maintaining inference speed (55.8 FPS) that meets industrial requirements.