广东电力2025，Vol.38Issue(11)：46-53,8.DOI:10.3969/j.issn.1007-290X.2025.11.005
基于无人机喷雾拍照及深度学习的绝缘子憎水性检测系统设计
Design of Insulator Hydrophobicity Detection System Based on UAV Spray Photography and Deep Learning
摘要
Abstract
Hydrophobicity detection of the insulators in operation is a difficulty in power inspection work.To address this issue,an insulator hydrophobicity detection system based on UAV platform and the deep learning model is designed.First,the spray UAV implements spray on the insulator surface,then collects the insulator images and processes the images through the deep learning model to complete hydrophobicity classification detection.The system calls the edge detection device through the drone remote control to achieve image exchange,and completes the user interaction interface on the remote control.The system can be friendly adapted to the existing inspection work.The proposed deep learning classification model adopts the FasterNet backbone network fused with efficient channel attention(ECA)mechanism and has good detection performance.The dataset validation and field experiments prove that the proposed system runs well with an average discrimination accuracy of 98.4%for hydrophobicity,which can meet the requirements of daily inspection of insulators.关键词
电力巡检/绝缘子/憎水性/无人机/深度学习Key words
power inspection/insulator/hydrophobicity/unmanned aerial vehicle(UAV)/deep learning分类
信息技术与安全科学引用本文复制引用
杨传凯,边少聪,辛蕾,张雷,杜建超..基于无人机喷雾拍照及深度学习的绝缘子憎水性检测系统设计[J].广东电力,2025,38(11):46-53,8.基金项目
国网陕西省电力有限公司科技项目(5226KY24000H) （5226KY24000H）
