基于压缩图像与YOLOv5模型的架空输电线路缺陷检测技术  

Efect detection technology for overhead transmission lines based on compressed images and YOLOv5 model

作　　者：刘敏 姜亮 田杨阳 张璐 陈岑 LIU Min;JIANG Liang;TIAN Yangyang;ZHANG Lu;CHEN Cen(School of Systems Science,Beijing Normal University,Beijing 100875,China;Department of Equipment Management,State Grid Henan Electric Power Co.,Ltd.,Zhengzhou 450052,Henan,China;Electric Power Research Institute,State Grid Henan Electric Power Co.,Ltd.,Zhengzhou 450052,Henan,China)

机构地区：[1]北京师范大学系统科学学院,北京100875 [2]国网河南省电力有限公司设备管理部,河南郑州450052 [3]国网河南省电力有限公司电力科学研究院,河南郑州450052

出　　处：《沈阳工业大学学报》2025年第2期152-159,共8页Journal of Shenyang University of Technology

基　　金：河南省自然科学基金项目(2122300410147);国家电网公司科技项目(5600-202046347A-0-0-00)。

摘　　要：【目的】输电线路作为电能传输和使用过程中的重要环节,其安全稳定对电力系统的正常运行起着至关重要的作用,因此输电线路日常巡检具有重要作用。重大事故通常由微小缺陷隐患发展而来,日常巡检通常采用人工、无人机、可视化通道等手段,无论何种方式都需要处理大量可视化、红外或者紫外照片。但由于输电线路的特殊性,架设条件涉及多种环境,其巡检图像背景通常较为复杂,采用人工复核审查的方式精度较高,但对经验依赖较大且效率极低。如何快速、准确地识别架空线路巡检图片是架空输电线路缺陷识别的关键。传统输电线路巡检图片识别方法在复杂背景的干扰下,容易出现缺陷识别精确度不高的问题。【方法】为提高架空输电线路巡检图像复杂背景下的检测准确率,提出了一种兼顾识别效率和准确性的缺陷检测方法。基于压缩图像技术并结合YOLOv5模型,设计了一种基于稀疏卷积的非对称特征聚合压缩算法,将原始图像通过编码减少图像存储所需空间以便于存储和传输,经过信息通道传输到解密器后,再将压缩图像进行解码复原以提升局部集合特征的学习效率。同时,通过融入通道空间注意力模块从特征图中得到注意力通道权重矩阵和空间权重矩阵,并通过权重矩阵判断特征图区域的重要程度,完成对YOLOv5模型处理效率的提升。【结果】将压缩恢复后的图像输入改进YOLOv5模型中,利用通道注意力模块(CAM)和空间注意力模块(SAM)分别对图像进行通道与空间上的注意力数据处理,通过全局平均池化和最大池化处理增强目标区域的特征,并引入空间注意力模块增强通道注意力对特征位置信息的关注,以检测出存在缺陷的设备,并通过实验验证了方法的有效性。【结论】以某架空线路的巡检图像数据集为基础,对检测方法开展训练与测试,结果表明,巡[Objective]Transmission lines are an important link in the transmission and use of electrical energy,and their safety and stability play a crucial role in the normal operation of the power system.Therefore,daily inspections of transmission lines are of great importance.Major accidents usually develop from small defects and hidden dangers.Daily inspections usually use manual,unmanned aerial vehicle,visualization channels,and other means.Regardless of the method,a large number of visualization,infrared,or ultraviolet photos need to be processed.However,due to the particularity of transmission lines,the installation conditions involve multiple environments,and the inspection image background is usually complex.Although the manual review method has high accuracy,it relies heavily on experience and has extremely low efficiency.Therefore,how to quickly and accurately identify inspection images of overhead transmission lines is the key to identifying defects in overhead transmission lines.The traditional image recognition method for transmission line inspection is prone to low defect recognition accuracy under complex background interference.[Methods]Therefore,to enhance the recognition accuracy of detection images of overhead transmission lines under complex backgrounds,a defect detection method that balances recognition efficiency and accuracy was proposed.The proposed method was based on compressed image technology combined with the YOLOv5 model.Firstly,an asymmetric feature aggregation compression algorithm based on sparse convolution was designed.The original image was encoded to reduce the space required by image storage data for storage and transmission.After being transmitted to the decryptor through the information channel,the compressed image was decoded and restored to improve the learning efficiency of local set features.At the same time,by the integration of the channel-spatial attention module(CSAM),the attention channel weight matrix and spatial weight matrix were obtained from the feature map,and the imp

关 键 词：架空输电线路 缺陷检测 图像压缩 改进YOLOv5模型 非对称特征聚合编解码网络 通道空间注意力模块 逐通道稀疏残差卷积 检测准确率 

分 类 号：TM755[电气工程—电力系统及自动化]