Real-time vehicle detection for traffic monitoring by applying a deep learning algorithm over images acquired from satellite and drone

D. Vohra, P. Garg, S. Ghosh
{"title":"Real-time vehicle detection for traffic monitoring by applying a deep learning algorithm over images acquired from satellite and drone","authors":"D. Vohra, P. Garg, S. Ghosh","doi":"10.1108/ijius-06-2022-0077","DOIUrl":null,"url":null,"abstract":"PurposeThe purpose is to design a system in which drones can control traffic most effectively using a deep learning algorithm.Design/methodology/approachDrones have now started entry into each facet of life. The entry of drones has made them a subject of great relevance in the present technological era. The span of drones is, however, very broad due to various kinds of usages leading to different types of drones. Out of the many usages, one usage which is presently being widely researched is traffic monitoring as traffic monitoring can hover over a particular area. This paper specifically brings out the basic algorithm You Look Only Once (YOLO) which may be used for identifying the vehicles. Consequently, using deep learning YOLO algorithm, identification of vehicles will, therefore, help in easy regulation of traffic in streetlights, avoiding accidents, finding out the culprit drivers due to which traffic jam would have taken place and recognition of a pattern of traffic at various timings of the day, thereby announcing the same through radio (namely, Frequency Modulation (FM)) channels, so that people can take the route which is the least jammed.FindingsThe study found that the object(s) detected by the deep learning algorithm is almost the same as if seen from a naked eye from the top view. This led to the conclusion that the drones may be used for traffic monitoring, in the days to come, which was not the case earlier.Originality/valueThe main research content and key algorithm have been introduced. The research is original. None of the parts of this research paper has been published anywhere.","PeriodicalId":42876,"journal":{"name":"International Journal of Intelligent Unmanned Systems","volume":" ","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Unmanned Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ijius-06-2022-0077","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 1

Abstract

PurposeThe purpose is to design a system in which drones can control traffic most effectively using a deep learning algorithm.Design/methodology/approachDrones have now started entry into each facet of life. The entry of drones has made them a subject of great relevance in the present technological era. The span of drones is, however, very broad due to various kinds of usages leading to different types of drones. Out of the many usages, one usage which is presently being widely researched is traffic monitoring as traffic monitoring can hover over a particular area. This paper specifically brings out the basic algorithm You Look Only Once (YOLO) which may be used for identifying the vehicles. Consequently, using deep learning YOLO algorithm, identification of vehicles will, therefore, help in easy regulation of traffic in streetlights, avoiding accidents, finding out the culprit drivers due to which traffic jam would have taken place and recognition of a pattern of traffic at various timings of the day, thereby announcing the same through radio (namely, Frequency Modulation (FM)) channels, so that people can take the route which is the least jammed.FindingsThe study found that the object(s) detected by the deep learning algorithm is almost the same as if seen from a naked eye from the top view. This led to the conclusion that the drones may be used for traffic monitoring, in the days to come, which was not the case earlier.Originality/valueThe main research content and key algorithm have been introduced. The research is original. None of the parts of this research paper has been published anywhere.
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
通过对卫星和无人机获取的图像应用深度学习算法进行交通监控的实时车辆检测
目的是设计一个系统,在这个系统中,无人机可以使用深度学习算法最有效地控制交通。设计/方法论/方法无人机现在已经开始进入生活的各个方面。无人机的进入使其成为当今科技时代的一个重要主题。然而,由于各种用途导致不同类型的无人机,无人机的范围非常广泛。在众多用途中,目前正在广泛研究的一种用途是交通监控,因为交通监控可以在特定区域上空盘旋。本文具体提出了可用于车辆识别的You Look Only Once (YOLO)基本算法。因此,使用深度学习YOLO算法,车辆识别将有助于轻松调节路灯交通,避免事故,找出导致交通拥堵的罪魁祸首司机,并识别一天中不同时间的交通模式,从而通过无线电(即调频(FM))频道宣布相同的情况,以便人们可以选择拥堵最少的路线。研究结果发现,深度学习算法检测到的物体与肉眼从俯视图看到的物体几乎相同。由此得出的结论是,在未来的日子里,无人机可能会被用于交通监控,而此前的情况并非如此。介绍了主要研究内容和关键算法。这项研究是原创的。这篇研究论文的任何部分都没有发表过。
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 去求助
来源期刊
CiteScore
3.50
自引率
0.00%
发文量
21
期刊最新文献
Design of hexacopter and finite element analysis for material selection Towards a novel cyber physical control system framework: a deep learning driven use case Employing a multi-sensor fusion array to detect objects for an orbital transfer vehicle to remove space debris Communication via quad/hexa-copters during disasters Nonlinear optimal control for UAVs with tilting rotors
×
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