基于YOLOv8的输电线路绝缘子表面缺陷识别算法  

Surface defect detection algorithm of transmission line insulators based on YOLOv8

作　　者：熊伟 路鑫 邱维进 王平强 Xiong Wei;Lu Xin;Qiu Weijin;Wang Pingqiang(Department of Computer,North China Electric Power University,Baoding 071000,China;Hebei Key Laboratory of Knowledge Computing for Energy&Power,Baoding 071000,China)

机构地区：[1]华北电力大学计算机系,保定071000 [2]河北省能源电力知识计算重点实验室,保定071000

出　　处：《电子测量技术》2025年第2期178-188,共11页Electronic Measurement Technology

基　　金：国家电网有限公司总部管理科技项目(5700-202340289A-1-1-ZN)资助。

摘　　要：针对当前绝缘子表面缺陷识别存在的图像背景复杂、缺陷小目标识别效果差的问题,提出一种基于YOLOv8的输电线路绝缘子表面缺陷识别算法。首先,在主干网络引入CAF模块,增强模型对复杂图像场景的解析,增强全局和局部特征的提取能力;其次,在模型的颈部网络增加GD机制,减少特征融合过程中信息的丢失,提升小目标检测能力;最后,采用ATFL分类损失函数,削弱复杂背景对小目标检测的干扰,引入PIOU边界框损失函数,提高识别精度,加快模型收敛速度。实验结果表明,该算法的mAP50达到94.1%,精确率达到92.5%,召回率达到91.3%,相较于基线模型分别提高了3.1%、0.7%、3.9%,且综合性能优于最近的YOLOv9s、YOLOv10s等代表性算法。Aiming at the problems of complex image background and poor recognition of small defect targets in the current insulator surface defect recognition,a transmission line insulator surface defect recognition algorithm based on YOLOv8 is proposed.Firstly,the CAF module is introduced in the backbone network to enhance the model′s analysis of complex image scenes and enhance the ability to extract global and local features;secondly,the GD mechanism is added to the neck network of the model to reduce the loss of information in the feature fusion process and improve the small target detection ability;finally,the ATFL classification loss function is used to weaken the interference of complex background on small target detection,and the PIOU bounding box loss function is introduced to improve the recognition accuracy and accelerate the model convergence speed.Experimental results show that the mAP50 of the algorithm reaches 94.1%,the precision rate reaches 92.5%,and the recall rate reaches 91.3%,which are 3.1%、0.7%and 3.9%higher than the baseline model,respectively,and the comprehensive performance is better than the recent YOLOv9s,YOLOv10s and other representative algorithms.

关 键 词：目标检测 绝缘子表面缺陷识别 小目标 卷积和注意力融合 边界框损失函数 分类损失函数 

分 类 号：TM216[一般工业技术—材料科学与工程] TN919.8[电气工程—电工理论与新技术]