Study on the detection of viscose filament defects based on improved YOLOv5

Abstract

In the production process of viscose filament, broken filament inspection is the most important part of detecting filament defects. To solve the problem of low speed and accuracy of broken filament detection and improve the online quality inspection system. In this paper, we design a broken filament detection method for viscose filaments based on the improved YOLOv5 algorithm. The GhostNet network structure is introduced to replace and modify the backbone network layer of YOLOv5 to reduce the complexity and computation of the structure and realize the light weight of the overall network structure; the ECA attention mechanism is introduced in the backbone network to enhance the feature perception of the broken filament target and increase the mobility of the feature information in the deep network. The improved YOLOv5 algorithm achieves an average detection accuracy of 93.9% and an average detection speed of 64 FPS in the final experimental results, which is better than the traditional methods of image recognition detection and can meet the realtime detection requirements of broken filament detection in practical engineering.