Color-patterned fabric defect detection based on the improved YOLOv5s model

Abstract

The complex background pattern of color-patterned fabrics, the small target of some defects, the difficulty separating them from the background, and the extreme aspect ratio present challenges for their automated, real-time detection. To solve the above problems, the YOLOv5s-based color-patterned fabric defect detection algorithm was proposed by combining lightweight modules. For the small target defects of color-patterned fabrics, coordinate attention was introduced in the feature extraction part to guide the model to focus entirely on the target defect area and suppress the background noise of color-patterned fabrics. Meanwhile, the bidirectional feature pyramid network was introduced in the feature fusion part to give different fusion weights to the extracted feature maps, improve the efficiency of feature fusion, and guide the model further to distinguish the fabric defects from the color-patterned background. Finally, the k-means algorithm was used to generate anchor boxes for the extreme aspect ratio of fabric defects to improve the training efficiency and accuracy of the model. Self-built datasets were experimented with to verify the improved model’s detection effect. The results show that the improved YOLOv5s model can reach 87.7% and 0.881 in mean average precision and F1 score, which are 2.3% and 0.02 better than the original model, respectively. The detection speed of the improved YOLOv5s model reached 60.24 frames per second (GPU 1660). After deployment on the fabric defect detection platform, the speed of detecting color-patterned fabrics can reach 15 m/min.