摘要
由于电力线路绝缘子缺陷容易导致输电系统故障,因此,研究缺陷检测算法至关重要。传统的检测方法只能在有足够的前提知识、干扰低或在特定条件下才能准确定位绝缘子并检测出故障。为了能够在无人机航拍图像中自动定位绝缘子并检测出绝缘子缺陷,提出了一种全新的深度卷积神经网络(CNN)架构,该架构不仅能定位绝缘子而且还能检测绝缘子的缺陷。该架构分为两个模块,第一个模块为绝缘子定位,负责检测图像中的所有绝缘子;第二个模块为绝缘子缺陷检测,负责检测图像中所有绝缘子的缺陷。使用具有候选区域网络(Region Proposal Network,RPN)的CNN将绝缘子缺陷检测转换为两级对象检测问题。最后,在真实数据集上进行实验,所提方法缺陷检测精确率和召回率分别为91.2%和95.6%,满足了鲁棒性和准确性要求。
Since power line insulator defects can easily lead to transmission system failures,it is critical to study defect detection algorithms.Traditional detection methods can only accurately locate insulators and detect faults with sufficient prerequisite knowledge,low interference,or under specific conditions.For automatically locating insulators and detecting insulator defects in UAV aerial images,we propose a novel deep Convolutional Neural Network(CNN)architecture that not only locates insulators but also detects insulator defects.The architecture is divided into two modules,the first module for insulator localization is responsible for detecting all insulators in the image,and the second module for insulator defect detection is responsible for detecting all insulator defects in the image,using a CNN with a Region Proposal Network(RPN)to convert insulator defect detection into a two-level object detection problem.Finally,we perform experiments using real datasets with defect detection accuracy and recall rates of 91.2%and 95.6%,respectively,satisfying the robustness and accuracy requirements.
作者
戴永东
金扬
戴雨凡
付晶
王茂飞
刘玺
DAI Yongdong;JIN Yang;DAI Yufan;FU Jing;WANG Maofei;LIU Xi(School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210023,China;State Grid Jiangsu Electric Power Co.,Ltd.Taizhou Power Supply Branch,Taizhou,Jiangsu 225300,China;Wuhan Branch of China Electric Power Research Institute Co.,Ltd.Wuhan 430070,China)
出处
《计算机科学》
CSCD
北大核心
2024年第S01期518-522,共5页
Computer Science
基金
国家自然科学基金(61601071)
国网科技项目(5500-202018082A-0-0-00)。
