A Survey on Vision-Based Anti Unmanned Aerial Vehicles Methods

被引:7
作者
Wang, Bingshu [1 ,2 ]
Li, Qiang [1 ]
Mao, Qianchen [1 ]
Wang, Jinbao [2 ]
Chen, C. L. Philip [3 ,4 ]
Shangguan, Aihong [5 ]
Zhang, Haosu [5 ]
机构
[1] Northwestern Polytech Univ, Sch Software, Xian 710129, Peoples R China
[2] Shenzhen Univ, Natl Engn Lab Big Data Syst Comp Technol, Shenzhen 518060, Peoples R China
[3] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510641, Peoples R China
[4] Pazhou Lab, Guangzhou 510335, Peoples R China
[5] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
基金
中国国家自然科学基金;
关键词
anti-UAV detection; UAV tracking; anti-UAV systems; anti-UAV datasets; DRONE; TRANSFORMER; TRACKING;
D O I
10.3390/drones8090518
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The rapid development and widespread application of Unmanned Aerial Vehicles (UAV) have raised significant concerns about safety and privacy, thus requiring powerful anti-UAV systems. This survey provides an overview of anti-UAV detection and tracking methods in recent years. Firstly, we emphasize the key challenges of existing anti-UAV and delve into various detection and tracking methods. It is noteworthy that our study emphasizes the shift toward deep learning to enhance detection accuracy and tracking performance. Secondly, the survey organizes some public datasets, provides effective links, and discusses the characteristics and limitations of each dataset. Next, by analyzing current research trends, we have identified key areas of innovation, including the progress of deep learning techniques in real-time detection and tracking, multi-sensor fusion systems, and the automatic switching mechanisms that adapt to different conditions. Finally, this survey discusses the limitations and future research directions. This paper aims to deepen the understanding of innovations in anti-UAV detection and tracking methods. Hopefully our work can offer a valuable resource for researchers and practitioners involved in anti-UAV research.
引用
收藏
页数:24
相关论文
共 50 条
  • [21] Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles
    Shafique, Arslan
    Mehmood, Abid
    Elhadef, Mourad
    IEEE ACCESS, 2021, 9 : 46927 - 46948
  • [22] Unmanned Aerial Vehicles for Air Pollution Monitoring: A Survey
    Motlagh, Naser Hossein
    Kortoci, Pranvera
    Su, Xiang
    Loven, Lauri
    Hoel, Hans Kristian
    Haugsvaer, Sindre Bjerkestrand
    Srivastava, Varun
    Gulbrandsen, Casper Fabian
    Nurmi, Petteri
    Tarkoma, Sasu
    IEEE INTERNET OF THINGS JOURNAL, 2023, 10 (24) : 21687 - 21704
  • [23] Vision-Based Marker-Less Landing of an Unmanned Aerial System on Moving Ground Vehicle
    Krpec, Blake
    Valasek, John
    Nogar, Stephen
    JOURNAL OF AEROSPACE INFORMATION SYSTEMS, 2024, 21 (09): : 735 - 750
  • [24] Overview of Detection and Localization Methods of Small Unmanned Aerial Vehicles
    Sokolskyi, S. O.
    Movchanyuk, A. V.
    VISNYK NTUU KPI SERIIA-RADIOTEKHNIKA RADIOAPARATOBUDUVANNIA, 2021, (87): : 46 - 55
  • [25] Vision-Based Drone Detection in Complex Environments: A Survey
    Liu, Ziyi
    An, Pei
    Yang, You
    Qiu, Shaohua
    Liu, Qiong
    Xu, Xinghua
    DRONES, 2024, 8 (11)
  • [26] Intelligent Unmanned Aerial Vehicles
    Thakkar, Parth
    Balaji, Anand
    Narwane, Vaibhav S.
    PROCEEDINGS OF INTERNATIONAL CONFERENCE ON INTELLIGENT MANUFACTURING AND AUTOMATION (ICIMA 2018), 2019, : 485 - 494
  • [27] Unmanned aerial vehicles in astronomy
    Biondi, Federico
    Magrin, Demetrio
    Ragazzoni, Roberto
    Farinato, Jacopo
    Greggio, Davide
    Dima, Marco
    Gullieuszik, Marco
    Bergomi, Maria
    Carolo, Elena
    Marafatto, Luca
    Portaluri, Elisa
    ADVANCES IN OPTICAL AND MECHANICAL TECHNOLOGIES FOR TELESCOPES AND INSTRUMENTATION II, 2016, 9912
  • [28] Vision-Based Lane Keeping - A Survey
    Keatmanee, Chadaporn
    Jakborvornphan, Siriaksorn
    Potiwanna, Chakrapan
    San-Um, Wimol
    Dailey, Matthew N.
    2018 INTERNATIONAL CONFERENCE ON EMBEDDED SYSTEMS AND INTELLIGENT TECHNOLOGY & INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY FOR EMBEDDED SYSTEMS (ICESIT-ICICTES), 2018,
  • [29] Acoustic detection of unmanned aerial vehicles using biologically inspired vision processing
    Fang, Jian
    Finn, Anthony
    Wyber, Ron
    Brinkworth, Russell S. A.
    JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2022, 151 (02) : 968 - 981
  • [30] YOLO series algorithms in object detection of unmanned aerial vehicles: a survey
    Jiao, Li
    Abdullah, Muhammad Irsyad
    SERVICE ORIENTED COMPUTING AND APPLICATIONS, 2024, 18 (03) : 269 - 298