基于TR-YOLOv5的输电线路多类缺陷目标检测方法  

Multi-class defect target detection method for transmission lines based on TR-YOLOv5

作　　者：郝帅[1] 赵新生 马旭[1] 张旭 何田 侯李祥 HAO Shuai;ZHAO Xin-sheng;MA Xu;ZHANG Xu;HE Tian;HOU Li-xiang(School of Electrical and Control Engineering,Xi’an University of Science and Technology,Xi’an Shaanxi 710054,China)

机构地区：[1]西安科技大学电气与控制工程学院,陕西西安710054

出　　处：《图学学报》2023年第4期667-676,共10页Journal of Graphics

基　　金：国家自然科学基金项目(51804250);中国博士后科学基金项目(2019M653874,2020M683522);陕西省科技计划项目(2021JQ-572,2020JQ-757);陕西省教育厅科研计划项目(18JK0512,17JK0503);西安市碑林区科技计划项目(GX2116)。

摘　　要：针对复杂环境中输电线路多类缺陷目标的多尺度检测问题,提出一种基于Transformer和感受野模块的YOLOv5输电线路多类缺陷目标检测算法,简记为TR-YOLOv5。首先,搭建了YOLOv5网络,针对复杂背景造成缺陷目标的显著性低,进而影响检测精度的问题,在Backbone部分引入Transformer模块,通过利用多头注意力结构获取特征图像素点间的相关性和全局信息,增强缺陷目标的特征表达能力,从而提升模型检测精度;其次,由于待检测目标受多尺度影响,在Neck部分引入感受野模块提取目标不同尺度的特征,利用空洞卷积增大感受野,为后续PANet结构保留更细致的特征,增强Neck特征融合能力,提高模型对多尺度缺陷目标的检测精度;然后,为了提升预测边框回归精度,引入CIOU函数,进一步提高算法检测精度;最后,利用某电力巡检部门近3年的数据对该算法进行验证。实验结果表明,相比于7种对比算法,本文算法具有较高检测精度的同时具有较好的实时性,其平均检测精度可达95.6%,1280×720分辨率的巡检图像检测速度为125帧/秒。To address the problem of multi-scale detection of multi-class defect targets in transmission lines in complex environments,a YOLOv5 transmission line multi-class defect target detection algorithm was proposed based on Transformer and perceptual field modules,abbreviated as TR-YOLOv5.First,a YOLOv5 network was built to address the problem of low saliency of defect targets caused by complex backgrounds,which hindered accurate detection.The Transformer module was introduced in the Backbone part.By utilizing a multi-head attention structure to capture the correlations and global information between the pixels of feature maps,the feature expression capability of the defect targets was enhanced,thereby improving the detection accuracy of the model.Secondly,since the target being detected is impacted by multiple scales,a perceptual field module was introduced in the Neck part to extract features of different scales of the target.Null convolution was also employed to increase the perceptual field,while more detailed features were reserved for the subsequent PANet structure.Furthermore,the Neck feature fusion capability was bolstered to enhance the detection accuracy of the model for multi-scale defective targets.In addition,to enhance the precision of predicted border regression,the CIOU function was introduced to further boost the detection accuracy of the algorithm.Finally,the proposed algorithm was validated using the data of a power inspection department for the past three years.The experimental results demonstrated that the proposed algorithm could surpass seven comparative algorithms in terms of detection accuracy and real-time performance,with an average detection accuracy of 95.6%and the inspection image detection speed for 1280×720 resolution reaching 125 frames/second.

关 键 词：YOLOv5 输电线路缺陷检测 空洞卷积 TRANSFORMER 感受野模块 损失函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]