基于双无人机合作的输电线路应变夹钳实时检测系统

Drones Pub Date : 2024-07-19 DOI:10.3390/drones8070333
Zhiwei Jia, Yongkang Ouyang, Chao Feng, Shaosheng Fan, Zheng Liu, Chenhao Sun
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引用次数: 0

摘要

应变夹具是高压架空输电线路的关键部件,检测其缺陷成为输电线路定期检查的重要组成部分。我们提出了一种双无人飞行器(UAV)系统,用于检测多根分相导线中的应变夹钳。主无人机配备了数字射线成像(DR)装置、机械臂和带视觉传感器的边缘智能模块。从属无人机配备了数字成像板和视觉传感器。针对这种双无人机系统提出了一套工作流程。目标检测、应变夹具的距离检测以及在 DR 图像中检测应变夹具的缺陷是该工作流程的主要程序。为满足无人机搭载和实时部署的需求,提出了改进的 YOLOv8-TR 算法用于检测应变夹具(mAP@50 为 60.9%),KD-ResRPA 算法用于检测 DR 图像中的缺陷(三个数据集的平均 AUCROC 为 82.7%)。现场实验验证了我们基于双无人机的系统对双分相导线应变夹具带电检测的适用性,证明了其在带电检测系统中的实际应用潜力。
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A Live Detecting System for Strain Clamps of Transmission Lines Based on Dual UAVs’ Cooperation
Strain clamps are critical components in high-voltage overhead transmission lines, and detection of their defects becomes an important part of regular inspection of transmission lines. A dual UAV (unmanned aerial vehicle) system was proposed to detect strain clamps in multiple split-phase conductors. The main UAV was equipped with a digital radiography (DR) imaging device, a mechanical arm, and an edge intelligence module with visual sensors. The slave UAV was equipped with a digital imaging board and visual sensors. A workflow was proposed for this dual UAV system. Target detection and distance detection of the strain clamps, as well as detection of the defects of strain clamps in DR images, are the main procedures of this workflow. To satisfy the demands of UAV-borne and real-time deployment, the improved YOLOv8-TR algorithm was proposed for the detection of strain clamps (the mAP@50 was 60.9%), and the KD-ResRPA algorithm is used for detecting defects in DR images (the average AUCROC of the three datasets was 82.7%). Field experiments validated the suitability of our dual UAV-based system for charged detection of strain clamps in double split-phase conductors, demonstrating its potential for practical application in live detecting systems.
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