Abstract: In order to address the challenges of complex background textures, and limited hardware resources in fabric defect detection, a lightweight fabric defect detection method based on improved YOLOv8n (GSL-YOLOv8n) is proposed. Firstly, to reduce the parameter count and complexity of the YOLOv8n model, a C2f Ghost module is constructed based on the Ghost idea and utilized to replace the regular convolutions (Conv) in the YOLOv8n network structure. Secondly, a parameter-free attention mechanism, SimAM, is embedded at the end of the backbone network to remove redundant background, enhance semantic information of small targets, and improve global information, enhancing the network’s feature extraction capability. Finally, a lightweight shared convolutional detection head (LSCDH) is designed to scale the features using a Scale layer, minimizing accuracy loss while ensuring model lightweightness. Compared with the original YOLOv8n model, the improved algorithm GSL-YOLOv8n achieves an average precision improvement of 0.60%, reaching 98.29%, and the detection speed FPS remains basically the same . The model size, computational complexity, and parameter count are reduced by 66.7%, 58.0%, and 67.4% respectively, meeting the application requirements of fabric defect detection in the textile industry.

Key words: fabric defects, YOLOv8, GhostNet, attention mechanism, lightweight, object detection