摘要
绝缘子作为输电线路重要的组成部件,其功能完整性对电网的安全运行至关重要。绝缘子所处的户外环境极易导致其存在异物搭挂,有必要监控绝缘子的运行状态。提出一种基于改进型目标检测算法(YOLO v3)的绝缘子异物检测方法:Dense-YOLO v3,设计密集网络(Dense-net)替代原网络其中一个卷积层,实现绝缘子的多层特征复用和融合,提高了检测精度。扩增了训练集以提升网络的训练效果;提出误检代价函数以度量误检的风险。实验结果表明,Dense-YOLO v3在测试集上的检测精度达到94.54%,误检代价低于YOLO v3和Faster-RCNN,可初步应用于输电线路的无人机巡检作业。
As an important component of transmission lines,insulator plays an essential role in the stable operation of the power grid.However,the outdoor environment in which the insulators are located can easily lead to the hanging of foreign objects.This paper proposes a novel method for foreign object detection on insulators based on the improved YOLO v3:Dense-YOLO v3.A dense network is designed to replace one of the convolutional layers of the original network in order to realize the multi-layer feature reuse and fusion of the insulator,which improves the detection accuracy.In addition,we amplify the training set to improve the training effect of the network and propose a wrong detection cost function to measure the risk of false detection.The experiment shows that the proposed algorithm has a detection precision rate reaching up to 94.54%.Meanwhile,the Dense-YOLO v3 outperforms YOLO v3 and Faster R-CNN,both in terms of detection accuracy and wrong detection cost.The result shows that the presented approach can be applied to the UAV inspection of transmission lines.
作者
张焕坤
李军毅
张斌
ZHANG Huankun;LI Junyi;ZHANG Bin(School of Automation,Guangdong University of Technology,Guangzhou 510006,China)
出处
《中国电力》
CSCD
北大核心
2020年第2期49-55,共7页
Electric Power
基金
国家自然科学基金资助项目(61803099)~~
