可见光-红外图像融合技术在电力系统故障检测中的应用

Sichao Chen, Yang Luo, Jianbo Yin, Guohua Zhou, Dilong Shen, Liang Shen
{"title":"可见光-红外图像融合技术在电力系统故障检测中的应用","authors":"Sichao Chen, Yang Luo, Jianbo Yin, Guohua Zhou, Dilong Shen, Liang Shen","doi":"10.1145/3596286.3596294","DOIUrl":null,"url":null,"abstract":"Infrared thermal imaging is widely used in industrial inspection due to its advantages such as passive identification, non-contact detection, long detection distance and strong environmental adaptability. In power systems, infrared thermal imaging can be used to carry out live detection of power equipment to prevent or examine potential risk and threats. This paper provides a fault detection method for power equipment through the visible light-infrared image fusion technology. The information of infrared image is collected through infrared thermal imager, and the infrared image is preprocessed. The scale invariant feature transform (SIFT) feature point detection algorithm is used to extract the difference between visible light image and infrared image. The feature points are screened and registered by random sample consensus (RANSAC) algorithm to realize the fusion of the visible light image and the infrared image of the power equipment, so as to detect the working status of the power equipment and accurately locate the fault source when a fault occurs.","PeriodicalId":208318,"journal":{"name":"Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of visible light-infrared image fusion technology in power system fault detection\",\"authors\":\"Sichao Chen, Yang Luo, Jianbo Yin, Guohua Zhou, Dilong Shen, Liang Shen\",\"doi\":\"10.1145/3596286.3596294\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Infrared thermal imaging is widely used in industrial inspection due to its advantages such as passive identification, non-contact detection, long detection distance and strong environmental adaptability. In power systems, infrared thermal imaging can be used to carry out live detection of power equipment to prevent or examine potential risk and threats. This paper provides a fault detection method for power equipment through the visible light-infrared image fusion technology. The information of infrared image is collected through infrared thermal imager, and the infrared image is preprocessed. The scale invariant feature transform (SIFT) feature point detection algorithm is used to extract the difference between visible light image and infrared image. The feature points are screened and registered by random sample consensus (RANSAC) algorithm to realize the fusion of the visible light image and the infrared image of the power equipment, so as to detect the working status of the power equipment and accurately locate the fault source when a fault occurs.\",\"PeriodicalId\":208318,\"journal\":{\"name\":\"Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3596286.3596294\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3596286.3596294","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

红外热成像具有被动识别、非接触检测、检测距离远、环境适应性强等优点,在工业检测中得到了广泛的应用。在电力系统中,红外热成像可用于对电力设备进行实时检测,以预防或检查潜在的风险和威胁。本文提出了一种利用可见光-红外图像融合技术对电力设备进行故障检测的方法。通过红外热成像仪采集红外图像信息,并对红外图像进行预处理。采用尺度不变特征变换(SIFT)特征点检测算法提取可见光图像与红外图像之间的差异。采用随机样本一致性(RANSAC)算法对特征点进行筛选配准,实现电力设备可见光图像与红外图像的融合,从而检测电力设备的工作状态,在故障发生时准确定位故障源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Application of visible light-infrared image fusion technology in power system fault detection
Infrared thermal imaging is widely used in industrial inspection due to its advantages such as passive identification, non-contact detection, long detection distance and strong environmental adaptability. In power systems, infrared thermal imaging can be used to carry out live detection of power equipment to prevent or examine potential risk and threats. This paper provides a fault detection method for power equipment through the visible light-infrared image fusion technology. The information of infrared image is collected through infrared thermal imager, and the infrared image is preprocessed. The scale invariant feature transform (SIFT) feature point detection algorithm is used to extract the difference between visible light image and infrared image. The feature points are screened and registered by random sample consensus (RANSAC) algorithm to realize the fusion of the visible light image and the infrared image of the power equipment, so as to detect the working status of the power equipment and accurately locate the fault source when a fault occurs.
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
期刊最新文献
Deep Object Detection for Complex Architectural Floor Plans with Efficient Receptive Fields Skeleton-based Generative Adversarial Networks for Font Shape Style Transfer: Learning text style from some characters and transferring the style to any unseen characters Medical Image Segmentation Approach via Transformer Knowledge Distillation Self-adaptive Methods with Flexible Detection Criteria of the Battery Cell Dent Defect Improved Mask R-CNN Network Method for PV Panel Defect Detection
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1