Automatic Detection Method of Operation Error of Smart Electricity Meter Based on Machine Vision

Jueyu Chen, Zhou Yang, Zhenglei Zhou
{"title":"Automatic Detection Method of Operation Error of Smart Electricity Meter Based on Machine Vision","authors":"Jueyu Chen, Zhou Yang, Zhenglei Zhou","doi":"10.1109/CONIT59222.2023.10205619","DOIUrl":null,"url":null,"abstract":"In order to improve the detection effect and reduce the number of electricity theft, an automatic detection method of operation error of intelligent electricity meter based on machine vision is proposed. Firstly, the smart meter image is collected, the Otsu threshold binarization method is used to extract the pulse lamp area image, and the improved lifting wavelet and morphology are used to preprocess the area image. Then, the pulse lamp in the reconstructed image is segmented by projection combined with a priori knowledge feedback method, and the template matching method is used to automatically detect whether the segmented pulse lamp is on, Finally, the experimental results show that this method can effectively detect the meter operation error under different line loss and power factor, effectively reduce the occurrence times of power theft, and has high detection accuracy. When different image brightness, this method has high dice value and high segmentation effect quality.","PeriodicalId":377623,"journal":{"name":"2023 3rd International Conference on Intelligent Technologies (CONIT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 3rd International Conference on Intelligent Technologies (CONIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CONIT59222.2023.10205619","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In order to improve the detection effect and reduce the number of electricity theft, an automatic detection method of operation error of intelligent electricity meter based on machine vision is proposed. Firstly, the smart meter image is collected, the Otsu threshold binarization method is used to extract the pulse lamp area image, and the improved lifting wavelet and morphology are used to preprocess the area image. Then, the pulse lamp in the reconstructed image is segmented by projection combined with a priori knowledge feedback method, and the template matching method is used to automatically detect whether the segmented pulse lamp is on, Finally, the experimental results show that this method can effectively detect the meter operation error under different line loss and power factor, effectively reduce the occurrence times of power theft, and has high detection accuracy. When different image brightness, this method has high dice value and high segmentation effect quality.
基于机器视觉的智能电表运行误差自动检测方法
为了提高检测效果,减少窃电次数,提出了一种基于机器视觉的智能电表操作误差自动检测方法。首先采集智能电表图像,采用Otsu阈值二值化方法提取脉冲灯区域图像,并采用改进的提升小波和形态学对区域图像进行预处理。然后,结合先验知识反馈方法对重构图像中的脉冲灯进行投影分割,并采用模板匹配方法自动检测分割后的脉冲灯是否亮起。最后,实验结果表明,该方法能够有效检测不同线损和功率因数下的电表运行误差,有效减少窃电的发生次数,具有较高的检测精度。当图像亮度不同时,该方法具有较高的分割值和分割效果质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信