GAE-YOLO:全局感知增强的输电线路外破隐患目标检测方法  

GAE-YOLO:Global awareness enhanced method for detecting external force damage in power transmission lines

作　　者：刘敏[1] 陈明[1] 武明虎[1] 叶永钢 Liu Min;Chen Ming;Wu Minghu;Ye Yonggang(Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学太阳能高效利用及储能运行控制湖北省重点实验室,武汉430068

出　　处：《仪器仪表学报》2025年第2期267-278,共12页Chinese Journal of Scientific Instrument

基　　金：湖北省自然科学基金(2022CFA007);湖北省中央引导地方科技发展资金(2023EGA027)项目资助。

摘　　要：超高压架空输电线路在电力系统中至关重要,但常面临建筑施工、山火等外力因素引发的事故。这不仅损害了国家经济,影响电网稳定性,还对电力工作人员的人身安全造成威胁。基于深度学习的目标检测方法为检测外破隐患提供了新方案,但现有方法往往依赖局部邻域信息执行采样操作,限制了感知范围和表达能力。为解决这一问题,提出了一种基于YOLOv10的实时全局感知增强方法GAE-YOLO,旨在提高超高压架空输电线路外破隐患目标的检测精度。针对传统方法中局部感知的局限,设计了2个新的上下采样模块:全局感知下采样模块(GADM)和全局感知上采样模块(GAUM)。GADM通过学习特征图的全局空间信息生成全局感知权重,优化下采样过程的感知性能;GAUM则通过利用深层特征图的通道信息生成全局感知权重,动态增强采样点的隶属关系,有效突出目标边界。为验证GAE-YOLO的有效性,构建了一个针对超高压架空输电线路外破隐患的大规模数据集,并在该数据集上取得了93.05%的平均精度均值(mAP),相较于基线模型mAP提升了5.13%。实验结果表明,GAE-YOLO能够显著提高外破隐患目标的检测精度,具有重要的应用价值,为电网安全运行提供了新的技术支持。Ultra-high-voltage overhead transmission lines are crucial in power systems.But,they often face accidents triggered by external factors,such as construction activities and wildfires.These incidents not only damage the national economy and affect grid stability,but also pose a threat to the safety of power workers.Deep learning-based object detection methods offer a novel solution for detecting external force damage objects.However,existing methods often rely on local neighborhood information for sampling operations,which limits their perceptual range and expressive capabilities.To address this issue,a real-time global awareness-enhanced method,GAE-YOLO,based on YOLOv10,is proposed to improve the detection accuracy of external force damage objects in ultra-high-voltage overhead transmission lines.To overcome the limitations of local perception in traditional methods,two novel upsampling and downsampling modules are designed,including the global awareness downsampling module(GADM)and the global awareness upsampling module(GAUM).GADM enhances perceptual performance by learning global spatial information from the feature map and generating global perception weights to optimize the downsampling process.GAUM dynamically enhances the membership relationship of sampling points by utilizing channel information from deep feature maps,effectively highlighting object boundaries.To evaluate the effectiveness of GAE-YOLO,a large-scale dataset for detecting external force damages in ultra-high-voltage overhead transmission lines is constructed.The model achieves mAP of 93.05%,a mAP 5.13%improvement over the baseline model.Experimental results show that GAE-YOLO significantly improves the detection accuracy of external-force damage objects,offering substantial application value and providing new technical support for the safe operation of power grids.

关 键 词：目标检测 全局感知 下采样 上采样 外破隐患 输电线路 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TM773[自动化与计算机技术—计算机科学与技术] TH183.3[电气工程—电力系统及自动化]