基于改进DeepLabv3+的接触网开口销缺陷检测  

Defect Detection of the Split Pins in Catenary Based on Improved DeepLabv3+

作　　者：王晓明 温锐 姚道金 董文涛 Wang Xiaoming;Wen Rui;Yao Daojin;Dong Wentao(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China)

机构地区：[1]华东交通大学电气与自动化工程学院,江西南昌330013

出　　处：《华东交通大学学报》2023年第5期120-126,共7页Journal of East China Jiaotong University

基　　金：国家自然科学基金地区项目(52165069);国家自然科学基金青年基金(52005182);江西省教育厅科技项目(GJJ2200621)。

摘　　要：针对当前基于语义分割的开口销缺陷检测算法存在分割精度不高、检测效率低等问题,提出一种基于改进DeepLabv3+的接触网开口销缺陷检测方法。首先,采用减枝后的MobileNetv2作为DeepLabv3+的骨干特征提取网络,提高检测效率。然后,通过在DeepLabv3+的编码器网络中引入CBAM注意力机制,提高开口销语义分割精度。同时,为缓解开口销区域和背景之间不平衡带来的负面影响,采用CEDice Loss作为损失函数。最后,根据开口销语义分割图像的颜色、形态信息,对开口销进行缺陷识别。实验结果表明:在语义分割方面,相比于原DeepLabv3+模型,改进DeepLabv3+模型的平均像素准确率和平均交并比分别提高了3.54%和3.42%,且测试用时减少了14.41 ms/张,模型参数量缩减了88.61%;在缺陷识别方面,对开口销缺失,松脱,正常三种状态的识别准确率分别为100%,98.1%,99.5%,能够快速有效地识别出开口销缺陷。Aiming at the problems of low segmentation accuracy and low detection efficiency of split pin defect detection algorithms based on semantic segmentation,this paper proposes an improved method of split pin defect detection for catenary based on DeepLabv3+.Firstly,the MobileNetv2 network is pruned,and the MobileNetv2 network is replaced with Xception for feature extraction,which can greatly reduce the consumption of computing resources and improve the detection efficiency.Then,CBAM(Convolutional Block Attention Module)is integrated into ASPP(Atrous Spatial Pyramid Pooling)module,and CBAM is introduced to process shallow features of input Decoder network,enhance the perception of split pin edge features,and improve the accuracy of model semantic segmentation.In order to alleviate the negative impact caused by the imbalance between split pin region and background region and improve split pin segmentation accuracy,CEDice Loss is used as the Loss function in this paper,combining the advantages of Cross-Entropy Loss and Dice Loss.Finally,according to the principle of split pin defect discrimination formulated in this paper,the state recognition of split pin is carried out according to the color and shape information of image segmentation.The experimental results show that compared with the original DeepLabv3+model,the MPA and MIOU of the improved DeepLabv3+model are improved by 3.54%and 3.42%,respectively,and the testing time is reduced by 14.41 ms per image,and the model parameters is reduced by 88.61%.In terms of defect identification,the accuracy of the method for missing,loose and normal split pins is 100%,98.1%and 99.5%,respectively,which can quickly and effectively identify split pin defects.

关 键 词：开口销 缺陷检测 DeepLabv3+ MobileNetv2 CBAM 

分 类 号：U225.4[交通运输工程—道路与铁道工程]