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

作　　者：唐锐 武建超 陈剑波 柴江 王迁 何雨辰 TANG Rui;WU Jianchao;CHEN Jianbo;CHAI Jiang;WANG Qian;HE Yuchen(Bazhou Power Supply Company,State Grid Xinjiang Electric Power Co.LTD.,Korla 841000,China;School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]国网新疆电力有限公司巴州供电公司,新疆库尔勒841000 [2]华北电力大学电气与电子工程学院,北京102206

出　　处：《计算机与现代化》2025年第3期6-11,共6页Computer and Modernization

基　　金：国网新疆电力有限公司科技项目(5230BD230001)。

摘　　要：输电线路覆冰会给电网系统的安全稳定造成巨大影响,由于输电线路多分布在山区、林区、无人的空旷地带,发生雨雪冰冻等破坏时工作人员无法在第一时间获取现场的信息。为了精确识别山区、无人区等复杂环境下输电线路覆冰情况,本文提出一种基于改进的YOLOv8s的检测方法。首先,采用SIoU作为损失函数,以提高模型的训练速度和精确率;其次,通过将部分普通卷积替换为对偶卷积,增强不同通道间的信息交换,有效提高特征提取效率,从而进一步加快了模型的收敛速度;最后,引入GiraffeDet网络结构来替代原有的网络结构,充分利用多尺度信息和特征图的全局上下文,使得模型在检测小目标和复杂场景时表现更为出色,提升检测的准确性和鲁棒性。实验结果表明,改进方法与YO-LOv8s相比,精确率满足一定的需求,模型大小轻量化了7.3 MB,速度也有显著提升。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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]