高电压技术2025，Vol.51Issue(2)：669-677,9.DOI:10.13336/j.1003-6520.hve.20232175
基于YOLO-GSS的输电线路边缘端实时缺陷检测方法
Real-time Defect Detection Method for Edge-end of Transmission Line Based on YOLO-GSS
摘要
Abstract
The combination of edge-end devices and transmission line intelligent inspection can meet the needs of re-al-time defect detection in the field.However,the current research on algorithms for edge-end devices applicable to low-computing-power,low-memory devices is rarely available.Aiming at the above problems,this paper proposes a re-al-time defect detection method based on YOLO-GSS transmission line edge-end.Firstly,Mosaic-9 is used to improve the input end of YOLOv8 network,which improves the number of input features of the algorithm and enhances the robust-ness of the algorithm.Then,GhostNet and S-FPN are introduced to improve the Backbone and Neck part,which improves the inference speed of the algorithm and corrects the accuracy at the same time.Finally,SIoU is used to correct the YOLOv8's CIoU loss function to further improve the detection accuracy of the algorithm.The experimental results show that,compared with the original YOLOv8,the method proposed in this paper can be adopted to realize a quattuor increase in inference speed on Nvidia Jetson NX edge-end devices without too much decrease in accuracy,which can meet the demand for real-time detection of defects on transmission line sites.关键词
边缘计算/输电线路/缺陷检测/GhostNet/深度学习Key words
edge compute/transmission line/defect detect/GhostNet/deep learning引用本文复制引用
葛召,李洪文,刘海峰,贾志辉,周开峰,邢雨辰..基于YOLO-GSS的输电线路边缘端实时缺陷检测方法[J].高电压技术,2025,51(2):669-677,9.基金项目
国网河北省电力有限公司资助项目(kj2021-015).Project supported by Program of State Grid Hebei Electric Power Company Limited(kj2021-015). （kj2021-015）
