基于图像识别算法的变电站仪表识别

Yunhai Song, Zhenzhen Zhou, Pengfei Xiang, Su Fang
{"title":"基于图像识别算法的变电站仪表识别","authors":"Yunhai Song, Zhenzhen Zhou, Pengfei Xiang, Su Fang","doi":"10.1109/ACIRS49895.2020.9162619","DOIUrl":null,"url":null,"abstract":"To improve the automatic operation of remote video monitoring equipment of substation, improve the operation efficiency of equipment, and realize the function of automatic self-check, the instrument identification system based on image recognition is studied. Firstly, the research significance of the application of image recognition in instrument recognition is analyzed. On the basis of previous studies, the structure of neuron network is introduced. Then, based on the Scale Invariant Feature Transform (SIFT), the instrument positioning is studied, the Gaussian difference model is constructed, and the reading method is determined, so that the instrument identification system can automatically read. Next, based on SIFT, the instrument identification system is established and the design scheme of the system is proposed. Finally, the experiment proves that the instrument recognition system based on image recognition is able to automatically recognize simple graphical interface and learn independently. This study has a great influence on the research field of image recognition instrument recognition.","PeriodicalId":293428,"journal":{"name":"2020 5th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Instrument Recognition in Transformer Substation Base on Image Recognition Algorithm\",\"authors\":\"Yunhai Song, Zhenzhen Zhou, Pengfei Xiang, Su Fang\",\"doi\":\"10.1109/ACIRS49895.2020.9162619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To improve the automatic operation of remote video monitoring equipment of substation, improve the operation efficiency of equipment, and realize the function of automatic self-check, the instrument identification system based on image recognition is studied. Firstly, the research significance of the application of image recognition in instrument recognition is analyzed. On the basis of previous studies, the structure of neuron network is introduced. Then, based on the Scale Invariant Feature Transform (SIFT), the instrument positioning is studied, the Gaussian difference model is constructed, and the reading method is determined, so that the instrument identification system can automatically read. Next, based on SIFT, the instrument identification system is established and the design scheme of the system is proposed. Finally, the experiment proves that the instrument recognition system based on image recognition is able to automatically recognize simple graphical interface and learn independently. This study has a great influence on the research field of image recognition instrument recognition.\",\"PeriodicalId\":293428,\"journal\":{\"name\":\"2020 5th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 5th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACIRS49895.2020.9162619\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 5th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACIRS49895.2020.9162619","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

为提高变电站远程视频监控设备的自动化运行,提高设备运行效率,实现自动自检功能,研究了基于图像识别的仪表识别系统。首先,分析了图像识别在仪器识别中的应用的研究意义。在前人研究的基础上,介绍了神经元网络的结构。然后,基于尺度不变特征变换(SIFT)对仪器定位进行研究,构建高斯差分模型,确定读取方法,使仪器识别系统能够自动读取。其次,建立了基于SIFT的仪器识别系统,并提出了系统的设计方案。最后,通过实验证明,基于图像识别的仪器识别系统能够自动识别简单的图形界面并自主学习。该研究对图像识别仪器识别的研究领域具有重要的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Instrument Recognition in Transformer Substation Base on Image Recognition Algorithm
To improve the automatic operation of remote video monitoring equipment of substation, improve the operation efficiency of equipment, and realize the function of automatic self-check, the instrument identification system based on image recognition is studied. Firstly, the research significance of the application of image recognition in instrument recognition is analyzed. On the basis of previous studies, the structure of neuron network is introduced. Then, based on the Scale Invariant Feature Transform (SIFT), the instrument positioning is studied, the Gaussian difference model is constructed, and the reading method is determined, so that the instrument identification system can automatically read. Next, based on SIFT, the instrument identification system is established and the design scheme of the system is proposed. Finally, the experiment proves that the instrument recognition system based on image recognition is able to automatically recognize simple graphical interface and learn independently. This study has a great influence on the research field of image recognition instrument recognition.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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