南方电网技术2026，Vol.20Issue(3)：40-50,11.DOI:10.13648/j.cnki.issn1674-0629.2026.03.005
基于EDR-YOLOv7的架空输电线路无人机巡检边缘端目标检测方法
Edge-End Target Detection Method for UAVs Inspection of Overhead Transmission Lines Based on EDR-YOLOv7
摘要
Abstract
From the perspective of aerial photography of UAVs(unmanned aerial vehicles)during power inspections,taking into ac-count the particularity of the image characteristics of power equipment,a visual detection model EDR-YOLOv7 is proposed suitable for the edge of UAVs to address the common problems of the ubiquitous small target detection,target point occlusion,and the increase in model missed detection rate caused by the variable scale of aerial images,as well as increased calculation amount caused by dense detection.Firstly,a display visual center module is introduced into the neck network to capture the implicit relationship of pixels and solve the problem of missing small target features.Secondly,the dynamic sampling module is used to replace the transposed convolution to achieve flexible sampling of feature points and reduce the complexity of model calculation.Finally,in order to solve the problem of variable viewing angle scale of UAVs and the problem of accidental deletion and deviation of prediction frames caused by partial occlusion,the Inner-SIoU(inner-scylla intersection over union)loss term and the repulsion factor are added to the loss function,continuously reducing the prediction error during training iterations.After experimental verification,EDR-YOLOv7 compared to the original model increases mAP@0.5 and the detection frame rate by 3.89%and 5.2 frames/s respectively.The model is finally deployed on the Jetson XAVIER NX edge computer and accelerated by TensorRT reasoning,which performs well in video stream detection tasks.关键词
无人机/电力巡检/架空输电线路/YOLOv7/边缘端/小目标检测/多尺度识别/局部遮挡Key words
UAV/electric power inspection/overhead transmission lines/YOLOv7/edge deployment/small object detection/multiscale identification/partial occlusion分类
信息技术与安全科学引用本文复制引用
赵文俊,刘凯,许国伟,吴田,方春华,普子恒..基于EDR-YOLOv7的架空输电线路无人机巡检边缘端目标检测方法[J].南方电网技术,2026,20(3):40-50,11.基金项目
国家自然科学基金资助项目(51807110). Supported by the National Natural Science Foundation of China(51807110). （51807110）
