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Drones
Special Issues
UAV Detection, Classification, and Tracking
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UAV Detection, Classification, and Tracking

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 164

Special Issue Editors

Dr. Anastasios Dimou

E-Mail Website
Guest Editor
Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
Interests: UAV detection and classification; video surveillance applications; artificial intelligence; image and video processing; video coding
Special Issues, Collections and Topics in MDPI journals
Dr. Arne Schumann

E-Mail Website
Guest Editor
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, 76131 Karlsruhe, Germany
Interests: UAV detection, tracking and classification; deep learning image analysis; object detection and classification; person re-identification
Special Issues, Collections and Topics in MDPI journals
Dr. Lars Sommer

E-Mail Website
Guest Editor
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, 76131 Karlsruhe, Germany
Interests: deep learning image analysis; object classification; object segmentation
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitrios Zarpalas

E-Mail Website
Guest Editor
Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece
Interests: UAV detection and classification; 3D/4D computer vision; 3D human reconstruction and motion capturing; medical image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Alessio Fascista

E-Mail Website
Guest Editor
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: telecommunications; statistical signal processing; detection; estimation; localization
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Angelo Coluccia

E-Mail Website
Guest Editor
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
Interests: radar detection and localization; wireless networks; multi-sensor; multi-agent signal processing; cyber-physical systems; smart devices; social networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

At present, UAVs (a.k.a. drones) are widely available in a wide range of sizes and capabilities, introducing unprecedented opportunities but also threats in terms of safety, privacy, and security. While the introduction of artificial intelligence and deep learning in conjunction with hardware innovations have significantly improved the capabilities to detect and classify drones, counter-UAV systems are facing challenges to detect threats from diverse UAV types and makes, in diverse and ever-changing environments.

This Special Issue aims to highlight advances in the field of UAV detection, classification, and tracking using a variety of single and multi-sensor techniques. Topics include, but are not limited to:

  • Visual UAV detection and classification;
  • IR UAV detection and classification;
  • Radar UAV detection and classification;
  • RF UAV detection and classification;
  • Data fusion for UAV detection and classification;
  • UAV tracking.

This special issue is focused more on drones. Papers focus on sensors may choose our joint Special Issue in Sensors (ISSN 1424-8220).

Dr. Anastasios Dimou
Dr. Arne Schumann
Dr. Lars Sommer
Dr. Dimitrios Zarpalas
Dr. Alessio Fascista
Prof. Dr. Angelo Coluccia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAV detection
  • UAV classification
  • UAV tracking
  • optical sensors
  • IR sensors
  • RF sensors
  • radars
  • deep learning

Published Papers (1 paper)

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Research

24 pages, 6408 KiB  
Article
Towards Fully Autonomous Drone Tracking by a Reinforcement Learning Agent Controlling a Pan–Tilt–Zoom Camera
by Mariusz Wisniewski, Zeeshan A. Rana, Ivan Petrunin, Alan Holt and Stephen Harman
Drones 2024, 8(6), 235; https://0-doi-org.brum.beds.ac.uk/10.3390/drones8060235 - 30 May 2024
Abstract
Pan–tilt–zoom cameras are commonly used for surveillance applications. Their automation could reduce the workload of human operators and increase the safety of airports by tracking anomalous objects such as drones. Reinforcement learning is an artificial intelligence method that outperforms humans on certain specific [...] Read more.
Pan–tilt–zoom cameras are commonly used for surveillance applications. Their automation could reduce the workload of human operators and increase the safety of airports by tracking anomalous objects such as drones. Reinforcement learning is an artificial intelligence method that outperforms humans on certain specific tasks. However, there exists a lack of data and benchmarks for pan–tilt–zoom control mechanisms in tracking airborne objects. Here, we show a simulated environment that contains a pan–tilt–zoom camera being used to train and evaluate a reinforcement learning agent. We found that the agent can learn to track the drone in our basic tracking scenario, outperforming a solved scenario benchmark value. The agent is also tested on more complex scenarios, where the drone is occluded behind obstacles. While the agent does not quantitatively outperform the optimal human model, it shows qualitative signs of learning to solve the complex, occluded non-linear trajectory scenario. Given further training, investigation, and different algorithms, we believe a reinforcement learning agent could be used to solve such scenarios consistently. Our results demonstrate how complex drone surveillance tracking scenarios may be solved and fully autonomized by reinforcement learning agents. We hope our environment becomes a starting point for more sophisticated autonomy in control of pan–tilt–zoom cameras tracking of drones and surveilling airspace for anomalous objects. For example, distributed, multi-agent systems of pan–tilt–zoom cameras combined with other sensors could lead towards fully autonomous surveillance, challenging experienced human operators. Full article
(This article belongs to the Special Issue UAV Detection, Classification, and Tracking)
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