摘要
输电线路异常目标检测是电力系统监控的重要环节。现有的检测方法并未针对输电线路场景进行有效设计,存在深度网络所提取的特征不够充分,在目标环境多变、尺度变化等影响下缺乏鲁棒性等问题。本文提出一种面向输电线路的异常目标检测方法,该方法采用HRNet作为主干网络提取高分辨率特征,结合HRFPN优化目标特征表示的质量与在RPN阶段均衡产生的正负锚点数量比例,并使用级联的目标检测器进行分类和边界框回归。在输电线路场景的检测结果表明,本文提出的方法具有更高的检测性能,优于Faster R-CNN、Cascade R-CNN。
Abnormal object detection of transmission lines is an important part of power system monitoring.However,the existing detection methods are not designed effectively for transmission line scenes.There are some problems such as insufficient features extracted by the depth network and lack of robustness under the influence of variable target environment and scale changes.This paper proposes an abnormal object detection method for electric transmission line,using HRNet as the backbone network to extract high-resolution features,combined with HRFPN to optimizing the quality of object feature representation,and balancing the proportion of positive and negative anchor count generated in the RPN,using cascaded object detectors for classification and bounding box regression.The test results in the transmission line scene show that the proposed detection method has higher detection performance,which performs better than Faster R-CNN and Cascade R-CNN.
作者
李辉
周航
董燕
张淑军
LI Hui;ZHOU Hang;DONG Yan;ZHANG Shu-jun(School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China)
出处
《计算机与现代化》
2020年第8期8-13,20,共7页
Computer and Modernization
基金
国家自然科学基金资助项目(61702295,61672305)。
