摘要
针对"三跨"输电线路出现故障,将会对电力保障及铁路公路等基础交通设施造成重大影响这一问题,该文提出了一种结合深度学习和图像处理技术的输电线路安全区域内工程机械设备的的识别与检测方法。采用区域卷积神经网络(Faster-RCNN)对位于输电线路安全区域内的各类工程机械设备进行识别与检测,并基于caff框架下进行了实现;算法还结合同态滤波等图像处理技术,以进一步提高在不良光照等复杂环境下的目标检测结果的准确度。多组实验结果表明算法对于各种复杂环境下的各类工程机械设备均具有较高的检测识别率,算法实现了对位于输电线路安全区域内的工程机械类的风险预警识别,为输电线路故障与风险智能识别平台的建立提供了基础。
The failure of ＂three-cross＂ transnfission lines will have a major impact on the basic transportation facilities such as power security-and railways and highways. Therefore, it is crucial to identify and warn of the potential risks of ＂three-cross＂ transnfission lines. In response to this problem, this paper proposes a method combined with deep learning and image processing technology. It can be used to identify- and detect engineering machinery equipments in the safe area of transnfission lines. The algorithm uses the regional convolutional neural network（Faster-RCNN） to identify and detect various types of engineering machinery equipment located in the safe area of the transnfission line, and implements it based on the caff framework. The algorithm also combines image processing techniques such as homomorphic filtering to further improve the accuracy of target detection results in complex environments such as poor illumination. The results of multiple experiments show that the algorithm has a high detection and recognition rate for all kinds of engineering machinery and equipment in various complex environments. The algorithm realizes the early warning and identification of the risk of engineering machinery located in the safe area of the transmission line. It provides a basis for the establishment of the intelligent identification platform of the transmission line fault and risk.
作者
陈良琴
唐海城
肖新华
CHEN Liangqin;TANG Haicheng;XIAO Xinhua(Fuzhou University,Fuzhou 350018 Fujian,China;State Grid Fujian Electric Power Company Maintenance Branch,Fuzhou 350001 Fujian,China;State Grid hfformation ＆ Telecommincation Group Co.,Ltd.,Fuzhou 350001 Fujian,China)
出处
《电力大数据》
2018年第12期1-5,共5页
Power Systems and Big Data
关键词
深度学习
输电线路
工程机械
风险识别
deep learning
transmission line
engineering machinery
risk recognition
