计算机工程与应用2024，Vol.60Issue(20)：302-311,10.DOI:10.3778/j.issn.1002-8331.2306-0142
改进YOLOv5的织物缺陷检测方法
Fabric Defect Detection Method with Improved YOLOv5
摘要
Abstract
In order to improve the accuracy of deep learning method for fabric defect detection without increasing the amount of network parameters,a fabric defect detection method based on improved YOLOv5 is proposed.Firstly,the channel attention is transformed by depthwise convolution,the maximum pooling of clipping is used to optimize the spa-tial attention,and the feature extraction sub-network is built through the double-cascade attention mechanism constructed by the two,so as to improve the network's ability to extract the texture and semantic features of the defect area.Secondly,the ghost-shuffle convolution is used to improve the feature fusion sub-network to strengthen the screening of extracted features,which reduces the amount of model parameters and improves the problem of defect information loss and invalid information redundancy.Finally,a new loss function SIOU with angular loss is introduced at the detection end to promote the fitting of the real box and the prediction box and improve the accuracy of defect prediction.The results show that the improved YOLOv5 method can reduce the complexity and calculation amount of YOLOv5 benchmark model,and can obtain higher detection accuracy compared with six advanced methods such as YOLOv7,which increases the mAP@0.5 value by 2.6 percentage points and the mAP@0.5:0.9 value by 1.3 percentage points compared with the original model.关键词
织物缺陷检测/卷积神经网络/YOLOv5/双级联注意力机制/损失函数Key words
fabric defect detection/convolutional neural networks/YOLOv5/dual cascade attention mechanism/loss function分类
信息技术与安全科学引用本文复制引用
朱磊,王倩倩,姚丽娜,潘杨,张博..改进YOLOv5的织物缺陷检测方法[J].计算机工程与应用,2024,60(20):302-311,10.基金项目
国家自然科学基金(61971339) （61971339）
陕西省重点研发计划(2019GY-113) （2019GY-113）
陕西省自然科学基础研究计划(2019JQ-361). （2019JQ-361）
