摘要
绝缘子为配网架空输电线路的重要组成部分,对无人机航拍的绝缘子图像进行精准识别是实现其缺陷检测和故障诊断的重要前提。针对图像中绝缘子目标小、背景复杂的问题,提出了一种基于多尺度密集网络的配网架空输电线路绝缘子识别算法。首先,利用K-means算法对数据集的目标框进行分析,获取合适的锚框;然后,将基础网络中的残差模块替换为密集连接模块,以增强网络特征信息的复用与融合,同时添加空间金字塔池化模块、优化多尺度特征融合,以对绝缘子进行预测;最后,用融合交叉熵函数和Focal损失函数的损失函数替换原始损失函数,构建航拍巡检图像数据集并进行实验。实验结果表明,相比原始算法,本算法的准确率提高了约12个百分点,且鲁棒性更强,基本满足电网巡检对绝缘子识别的要求。
Insulators are an essential part of overhead transmission lines in distribution networks.Accurate identification of insulator images by drone aerial photography is an important prerequisite for defect detection and fault diagnosis.Aiming at the problem of small insulator targets and complex backgrounds in images,an algorithm for insulators identification on overhead transmission lines in distribution networks based on multi-scale dense networks is proposed in this paper.First,use the K-means algorithm to analyze the target frame of the dataset to obtain a suitable anchor frame.Second,replace the residual module in the basic network with a dense connection module to enhance the multiplexing and fusion of network feature information.At the same time,add a spatial pyramid pooling module and optimize multi-scale feature fusion to predict insulators.Finally,replace the original loss function with a loss function that combines the cross-entropy function and the Focal loss function to construct an aerial inspection image data set and perform experiments.The experimental results showed that the algorithm accuracy is improved by about 12 percentage points and has a stronger robustness than the original algorithm,which meets the requirements of the grid inspection for insulator identification.
作者
陈志豪
肖业伟
李志强
刘洋
Chen Zhihao;Xiao Yewei;Li Zhiqiang;Liu Yang(School of Automation and Electronic Information,Xiangtan University,Xiangtan,Hunan 411105,China;Key Laboratory of Intelligent Computing&Information Processing of Ministry of Education,Xiangtan University,Xiangtan,Hunan 411105,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第8期338-347,共10页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61773330)
湖南省自然科学基金(2017JJ2251)。
作者简介
陈志豪,E-mail:zhihao630@126.com;肖业伟,E-mail:10802795@qq.com。
