基于Faster-RCNN及一维曲线分析的表计指针识读方法  

Pointer Reading Based on Faster-RCNN and One-dimensional Curve Analysis

作　　者：钟力强 屈娟娟 姜新丽 黄炎 ZHONG Liqiang;QU Juanjuan;JIANG Xinli;HUANG Yan(China Southern Power Grid Technology Co.,Ltd.,Guangdong Engineering Research Center of Special Robots for Special lndustries,Guangzhou,Guangdong510080,China;China Southern Power Grid Technology Co.,Ltd.,China Southern Power Grid Joint Laboratory for Electric Power Robots,Guangzhou,Guangdong 510080,China;Guangzhou Xinhua College,Guangzhou,Guangdong 510520,China;Xuchang KETOP Electricl Research Institute Co.,Ltd.,Xuchang,Henan 461000,China)

机构地区：[1]南方电网电力科技股份有限公司,广东省特殊行业特种机器人工程技术研究中心,广东广州510080 [2]南方电网电力科技股份有限公司,中国南方电网电力机器人联合实验室,广东广州510080 [3]广州新华学院,广东广州510520 [4]许昌开普电气研究院有限公司,河南许昌461000

出　　处：《广东电力》2022年第2期27-35,共9页Guangdong Electric Power

基　　金：广东省“珠江人才计划”本土创新科研团队项目(2019BT02Z426)。

摘　　要：针对机器人部署调试中表计需逐个配置的问题,提出基于神经网络学习及一维曲线分析的表盘指针、刻度识别方法。基于Faster-RCNN(faster region-based convolutional network),初步识别刻度盘位置,从而确定指针运动圆心。将图像以圆心为中心进行极坐标展开后,通过分析一维曲线的频域特征筛选刻度所在区域,统计不同区域灰度峰谷值的形式确定刻度和指针的真实位置。最后对几种典型表计进行实验分析,验证所提方法的有效性。实验结果显示,该方法对表计样本量较多的表计类型,可成功实现自动读数功能。According to the situation the dials need to be configured one by one in electrical robot deployment and debugging,an auto-reading method of dial pointer and scale is proposed based on neural network learning and one-dimensional curve analysis.Through learning and recognizing the dial position based on Faster-RCNN(faster region-based convolutional network),the motion center of the pointer is initially determined.After the image is expanded in polar coordinates with the center of the circle,the region where the scale is screened by frequency analyzing with the one-dimensional curves,and the true position of the scale and pointer is determined by using image gray statistical characteristic of different regions.Finally,the effectiveness of the proposed method is verified by experimental analysis of several typical dials.The results show the proposed method can successfully realize automatic reading function for large sample meter types.

关 键 词：表计识别 指针 快速傅里叶变换 神经网络 Faster-RCNN 

分 类 号：TM63[电气工程—电力系统及自动化] TP391.41[自动化与计算机技术—计算机应用技术]