棉纺织技术2023，Vol.51Issue(12)：12-19,8.
基于改进YOLOv5的玻璃纤维管纱缺陷检测方法
Detection method of glass fiber tube yarn defect based on improved YOLOv5
摘要
Abstract
Aiming at the problems of poor anti-interference ability,low detection accuracy and slow detection speed in glass fiber tube yarn defect detection,a glass fiber tube yarn defect detection method based on improved YOLOv5(BY-YOLO)was proposed.Firstly,Efficient Reparameterization Network(ER-Net)was established as the backbone network to optimize the extraction of defective features of tube yarn.And structural reparameterization techniques and Refined Spatial Pyramid Pooling(R-SPP)were used to enhance the detection speed and diminish the influence of noise information of features on the detection effect.Secondly,Depth Attention Path Aggregation Network(DA-PANet)was proposed as a neck network to fuse the multi-scale features of tube yarn.The semantic information of defective features of tube yarn was enhanced by the feature enhancement module Depth-Mixer and the attention mechanism module.The detection capability of the model for multi-scale defects was improved.The experimental results showed that the method was able to improve mAP value of the detection on tube yarn defects to 94.43%.At the same time,the detection speed was increased to 103 flame/s.Compared with other mainstream detection models,the method proposed in this paper had higher robustness,accuracy and real-time performance.关键词
管纱缺陷检测/机器视觉/深度学习/YOLOv5/结构重参数化技术/注意力机制模块/平均精度均值Key words
tube yarn defect detection/machine vision/deep learning/YOLOv5/structural reparameterization technique/attention mechanism module/mean average precision分类
计算机与自动化引用本文复制引用
董振宇,景军锋..基于改进YOLOv5的玻璃纤维管纱缺陷检测方法[J].棉纺织技术,2023,51(12):12-19,8.基金项目
国家自然科学基金项目(62176204) （62176204）
陕西省创新能力支撑计划项目(2021TD-29) （2021TD-29）
陕西省秦创原"科学家+工程师"队伍建设项目(2023KXJ-061) （2023KXJ-061）
陕西高校青年创新团队项目 （）
