Research on Fabric Defect Detection Method Based on Improved YOLOv8n
Aiming at the problems of incorrect defect type recognition and low identification rate of small defects in the YOLOv8n model for fabric defect detection tasks,this study proposes a fabric defect detection algorithm based on an im-proved YOLOv8n.In this research,dynamic serpentine convolution and SE attention mechanism have been introduced into the fabric defect detection model.Part of the C2f modules in the backbone network of the YOLOv8n model have been combined with dynamic serpentine convolution,and the SE attention mechanism has been added to the backbone network to enhance the detection performance of the model.Experimental results show that,compared with the original YOLOv8n,the improved algorithm has improved precision by 7.2%,recall by 2.1%,mAP50 by 3.6%,and mAP50-90 by 1%.This in-dicates that the fabric defect detection model based on the improved YOLOv8n has achieved significant enhancements in defect detection capabilities.
万方数据
维普
