用于智能主动监视和监控的未来无人机/无人机系统

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Tazeem Ahmad, Alicia Morel, Nuo Cheng, Kannappan Palaniappan, Prasad Calyam, Kun Sun, Jianli Pan
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引用次数: 0

摘要

物联网(IoT)的快速发展推动了无人驾驶飞行器(uav)或无人机在各个领域的广泛采用,包括它们在监视和监控等应用中的使用。无人机的飞行能力允许毫不费力地进入以前无法进入的位置,提供任何所需区域或目标的实时,高分辨率数据-图像和视频。此外,人工智能(AI)和边缘计算技术的发展赋予了无人机高计算能力,使其适用于农业、运输和边境安全等多种应用。这些技术进步还为无人机配备了强大的机载处理能力,用于复杂的决策,提高了无人机的主动性和智能性。本调查探讨了无人机在各种应用中的智能主动监视和监控的前景。首先,讨论了应用程序中不同层次的无人机活跃度;其次,对已有研究进行了审查,以确定驱动智能无人机系统的关键技术和架构;第三,探讨了无人机在监视和监控中的几种应用,从基本任务到高智能操作。最后,该调查通过讨论新兴的研究挑战来结束,并概述了基于无人机的监视和监测系统中高度跨学科和新兴领域的未来研究的指导性路线图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Future UAV/Drone Systems for Intelligent Active Surveillance and Monitoring
The rapid development of the Internet of Things (IoT) has fueled the widespread adoption of Unmanned Aerial Vehicles (UAVs) or drones across various fields, including their use in applications such as surveillance and monitoring. UAVs flight capabilities allow to effortlessly access previously inaccessible locations, providing real-time, high-resolution data – images and videos – of any desired area or target. Furthermore, the growth of Artificial Intelligence (AI), and edge computing technologies has empowered UAVs with high computational capabilities, making them suitable for diverse applications such as agriculture, transportation and border security. These technology advancements also equip UAVs with powerful on-board processing for sophisticated decision-making that enhances UAV activeness and intelligence. This survey explores the promising areas of UAVs for intelligent active surveillance and monitoring across diverse applications. First, the various levels of UAV activeness within applications are discussed; second, prior research is examined to identify the key technologies and architectures that power intelligent UAV systems; and third, several UAV applications in surveillance and monitoring, ranging from basic tasks to highly intelligent operations are explored. Finally, the survey concludes by discussing emerging research challenges and outlines a guiding road map for future research of highly interdisciplinary and emerging areas in UAV-based systems for surveillance and monitoring.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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