计算机与现代化Issue(7)：9-14,6.DOI:10.3969/j.issn.1006-2475.2025.07.002
基于轻量化YOLOv8的无人机对绝缘子缺陷检测
Insulator Defect Detection by UAV Based on Lightweight YOLOv8
摘要
Abstract
In order to solve the problems of insulator string and insulator self-explosion,damage,flashover defects in complex background,different scales,small target factors leading to false detection and missed detection,resulting in low detection accu-racy,the CPCW-YOLOv8 algorithm is proposed.Firstly,a lightweight CBAM attention mechanism is introduced into the back-bone part,so that the model can enhance the extraction ability of insulator strings and insulator defect features in complex back-grounds from both channel and space aspects.Then,the small target detection layer is added,and the multi-scale fusion is used to enhance the extraction of shallow semantic information by the network,so as to capture more details of insulator defects and improve the detection accuracy of small targets.Secondly,in order to make the model more lightweight,a lightweight module C2f-Faster is constructed.Finally,the original CIoU is optimized to WIoU to accelerate convergence and improve the detection accuracy.Experimental results show that compared with the original model,the number of parameters of CPCW-YOLOv8 is re-duced by 12.6 precentage points,and the average accuracy is increased by 5.2 precentage points.The proposed network provides a more efficient method for the defect detection of insulators in power systems.关键词
绝缘子检测/小目标检测/轻量化模块/损失函数Key words
insulator detection/small target detection/lightweight modules/loss function分类
信息技术与安全科学引用本文复制引用
江志伟,傅晓锦,陈文彬,江毅晨..基于轻量化YOLOv8的无人机对绝缘子缺陷检测[J].计算机与现代化,2025,(7):9-14,6.基金项目
上海市自然科学基金资助项目(11ZR1413800) （11ZR1413800）
