Research on Seamless Fabric Defect Detection Based on Improved YOLOv8n

An improved YOLOv8n seamless fabric defect detection model is proposed to solve the current issues in seamless fabric defects in factories in this paper. The improvement in this paper first introduces the SPPF_LSKA module, which not only optimizes the extraction of multi-scale features but also enhances the adaptability of the model in detecting defects of different sizes by improving the feature fusion mechanism, enabling efficient recognition of both large-sized and small-sized defects. Secondly, the CARAFE upsampling method is used to adaptively learn the relationship between pixels, which not only reduces information loss but also improves the reconstruction quality of feature maps, which is crucial for capturing complex textures and subtle defects of seamless fabrics. In addition, adding a small object detection layer particularly improves the detection accuracy of the model for small-sized defects, making it no longer limited to traditional models when dealing with high-density fabrics or small defects. Finally, integrating OREPA technology significantly reduces computational complexity, reduces redundant computing burden, and accelerates the training process by optimizing the model structure. The experimental results show that the precision, recall, and mAP@0.5 of the model on the seamless fabric defect dataset have improved by 7.3%, 8.5%, and 5.1%, respectively, compared to the baseline model YOLOv8n. Future research aims to explore the application of the model further in practical scenarios and complete the actual deployment of the seamless fabric defect detection system.