计算机与现代化Issue(3)：6-11,6.DOI:10.3969/j.issn.1006-2475.2025.03.002
基于GiraffeDet的改进YOLOv8s输电线路覆冰检测
Improved YOLOv8s Algorithm Based on GiraffeDet for Transmission Line Icing Detection
摘要
Abstract
The icing of transmission lines can greatly impact the safety and stability of the power grid system.Due to the distribu-tion of transmission lines in mountainous areas,forest areas,and unmanned open areas,workers cannot obtain on-site informa-tion in the event of damage such as rain,snow,and freezing.To accurately identify the icing situation of transmission lines in complex environments such as mountainous and uninhabited areas,this paper proposes an improved YOLOv8s-based detection method.Firstly,SIoU is adopted as the loss function to improve the training speed and accuracy of the model.Secondly,by re-placing some ordinary convolutions with dual convolutions,the information exchange between different channels is enhanced,ef-fectively improving the efficiency of feature extraction,thereby further accelerating the convergence speed of the model.Finally,the GiraffeDet network structure is introduced to replace the original network structure and utilizes multi-scale information and the global context of feature map to make the model perform better in detecting small targets and complex scenes,improving the accuracy and robustness of detection.The experimental results show that compared with YOLOv8s,the improved method meets certain requirements for accuracy,reduces the model size by 7.3 MB,and significantly improves speed.关键词
深度学习/电力系统/YOLOv8s/输电线路覆冰检测Key words
deep learning/power systems/YOLOv8s/transmission line icing detection分类
计算机与自动化引用本文复制引用
唐锐,武建超,陈剑波,柴江,王迁,何雨辰..基于GiraffeDet的改进YOLOv8s输电线路覆冰检测[J].计算机与现代化,2025,(3):6-11,6.基金项目
国网新疆电力有限公司科技项目(5230BD230001) （5230BD230001）
