面向输电线路绝缘子的GER-YOLO缺陷检测算法  

GER-YOLO Fault-Detection Algorithm for Transmission-Line Insulators

作　　者：袁博雅 李尧 叶青[1] Yuan Boya;Li Yao;Ye Qing(School of Computer Science,Yangtze University,Jingzhou 434023,Hubei,China;School of Electronic Information and Electrical Engineering,Yangtze University,Jingzhou 434023,Hubei,China)

机构地区：[1]长江大学计算机科学学院,湖北荆州434023 [2]长江大学电子信息与电气工程学院,湖北荆州434023

出　　处：《激光与光电子学进展》2024年第22期139-149,共11页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(62006028);湖北省自然科学基金(2023AFB909)。

摘　　要：针对无人机检测绝缘子缺陷存在的算法参数量大、图像背景复杂、绝缘子尺度变化大等问题,提出了一种新的用于绝缘子缺陷检测的GER-YOLO算法。首先,利用GhostNetV2构建C2fGhostV2模块,能够在显著减少参数量和计算量的同时维持算法检测精度。然后,引入具有跨空间学习能力的高效多尺度注意力(EMA)网络,充分挖掘特征信息,抑制无用信息。最后,提出C2fRFE模块,进一步捕获长程信息,学习多尺度特征,提高对不同尺度绝缘子及其缺陷的检测能力。实验结果表明,GER-YOLO算法相较于基线模型YOLOv8s,平均精度均值(mAP)提升了1.1%,参数量、计算量分别减少了30.2%和31.0%,该算法能够有效完成绝缘子缺陷检测任务。A novel algorithm named GER-YOLO for insulator defect detection is proposed to address the issues of large algorithm parameters,complex image backgrounds,and significant insulator-scale changes in the unmanned-aerial-vehicle detection of insulator defects.First,GhostNetV2 is used to construct the C2fGhostV2 module,which significantly reduces the number of parameters and computation while maintaining the algorithm’s detection accuracy.Second,an efficient multi-scale attention(EMA)network with cross-spatial-learning ability is introduced,which enables the complete mining of feature information and suppresses meaningless information.Finally,the C2fRFE module is proposed to capture long-range information,learn multiscale features,and improve the detection ability of insulators and their defects at different scales.Experimental results show that compared with the baseline model YOLOv8s,GER-YOLO offers a higher mean average precision(mAP)by 1.1%,reduces the parameter and computational costs by 30.2%and 31.0%,respectively,and can effectively detect insulator defects.

关 键 词：绝缘子缺陷检测 轻量化 注意力机制 多尺度信息 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]