Effective and efficient landslide detection system to monitor Konkan railway tracks

S. Chavan, S. Pangotra, Sneha Nair, Vinayak More, Vineetha V Nair
{"title":"Effective and efficient landslide detection system to monitor Konkan railway tracks","authors":"S. Chavan, S. Pangotra, Sneha Nair, Vinayak More, Vineetha V Nair","doi":"10.1109/ICTSD.2015.7095844","DOIUrl":null,"url":null,"abstract":"Man has been developing various methods to protect himself from natural calamities since ages. The only scientific solution to natural calamities is development of systems to predict, detect and take preventive measures using recent advancement in technology. Along the highly landslide prone Konkan railway line, many people have lost their lives due to landslides. It is now high time to replace the present obsolete manual detection systems deployed along this line. In this paper, a highly accurate, effective and efficient landslide detection system has been proposed which can be used along the Konkan railway line to monitor tracks for landslide using image processing. The coding has been done using MATLAB and a low resolution webcam was used for acquiring sample video frames. Various techniques like Hamming distance, Entropy, Euclidean Distance, Correlation, Block processing etc. were used for event detection. The proposed technique gave a threshold margin of 80.24% and the average efficiency of the system was found to be 86.67% for the considered set of images. Using proposed technique, False Acceptance Ratio (FAR) of 0.067 and False Rejection Ratio (FRR) of 0.933 were achieved.","PeriodicalId":270099,"journal":{"name":"2015 International Conference on Technologies for Sustainable Development (ICTSD)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Technologies for Sustainable Development (ICTSD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTSD.2015.7095844","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Man has been developing various methods to protect himself from natural calamities since ages. The only scientific solution to natural calamities is development of systems to predict, detect and take preventive measures using recent advancement in technology. Along the highly landslide prone Konkan railway line, many people have lost their lives due to landslides. It is now high time to replace the present obsolete manual detection systems deployed along this line. In this paper, a highly accurate, effective and efficient landslide detection system has been proposed which can be used along the Konkan railway line to monitor tracks for landslide using image processing. The coding has been done using MATLAB and a low resolution webcam was used for acquiring sample video frames. Various techniques like Hamming distance, Entropy, Euclidean Distance, Correlation, Block processing etc. were used for event detection. The proposed technique gave a threshold margin of 80.24% and the average efficiency of the system was found to be 86.67% for the considered set of images. Using proposed technique, False Acceptance Ratio (FAR) of 0.067 and False Rejection Ratio (FRR) of 0.933 were achieved.
有效高效的滑坡检测系统,监测康坎铁路轨道
自古以来,人类一直在开发各种方法来保护自己免受自然灾害的影响。对付自然灾害的唯一科学解决办法是利用最新的技术进步发展预测、检测和采取预防措施的系统。在山体滑坡高发的康坎铁路沿线,许多人因山体滑坡而丧生。现在是时候替换沿着这条线路部署的目前过时的人工检测系统了。本文提出了一种基于图像处理技术的高精度、高效的康坎铁路沿线滑坡监测系统。使用MATLAB进行编码,并使用低分辨率网络摄像头获取样本视频帧。各种技术,如汉明距离,熵,欧几里得距离,相关,块处理等用于事件检测。该方法的阈值裕度为80.24%,系统的平均效率为86.67%。采用该技术,系统的误接受比(FAR)为0.067,误拒比(FRR)为0.933。
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:481959085
Book学术官方微信