计算技术与自动化2025，Vol.44Issue(3)：123-127,5.DOI:10.16339/j.cnki.jsjsyzdh.202503022
基于改进YOLO的高压配电网污闪绝缘子无人机检测方法
A UAV Detection Method for Pollution Flashover Insulators in High Voltage Distribution Networks Based on Improved YOLO
邱略能 1郑志祥 1孟德威1
作者信息
- 1. 国网衢州供电公司,浙江衢州 324000
- 折叠
摘要
Abstract
Aiming at the problems of complex data collection process and low prediction accuracy in the detection of high-voltage distribution network insulators under unmanned aerial vehicles(UAVs),a UAV detection method for high-voltage distribution network pollution flashover insulators based on image processing technology is proposed.Introducing CA mechanism to decompose channel attention and embed position information into channel attention,thereby enhancing the network's ability to detect insulators in complex backgrounds.The use of Bi-FPN instead of the original PANet feature fu-sion framework enhances the network's ability to detect small targets by weighting each scale to balance feature information at different scales.In the experimental stage,through ablation experiments,compared with the original network,the mAP of the proposed improved YOLO model increased by about 2.7％,and the Recall increased by about 4.0％.Compared with SSD,RetinaNet,YOLOv4,YOLOv5x,and YOLOv7,the proposed model has the highest mAP at approximately 90.3％.The experimental results validate the effectiveness and practicality of the proposed method,and the model has broad applica-tion prospects.关键词
高压配电网/电力巡检/深度学习/特征提取/注意力机制/多尺度特征Key words
high voltage distribution network/electric power inspection/deep learning/feature extraction/attention mechanism/multiscale features分类
信息技术与安全科学引用本文复制引用
邱略能,郑志祥,孟德威..基于改进YOLO的高压配电网污闪绝缘子无人机检测方法[J].计算技术与自动化,2025,44(3):123-127,5.
