Automatic detection of fabric defects is a critical step for quality assurance in the textile industry. However, current methods often miss defects because of difficult feature extraction. They can also misclassify defects that have little contrast with the fabric background.To address these challenges, we proposed an improved algorithm based on the YOLO version 11 nano model. First, we introduced a parameterized block with dilated convolutions into the main network backbone to enhance its ability to represent features at multiple scales. We also developed a new module to improve small object detection and model the global context. In the network′s neck, we designed a specialized attention module. This module strengthens the capture of defect shapes and textures, which reduces confusion between defects and the background. Finally, we refined the downsampling process using a re-parameterized stem structure. This change expanded the model′s receptive field and added more paths for feature extraction.Experimental results show that our improved algorithm achieves 93.2% precision, 90% recall, and 94.1% mean average precision. These results are 1.3%, 7.53% and 4.21% higher than the original model, respectively. The proposed algorithm effectively improves the accuracy of fabric defect detection and meets the practical demands of industrial production.