Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.6
4.4
Journals
Drones
Special Issues
UAV Detection, Classification, and Tracking
share announcement

UAV Detection, Classification, and Tracking

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3870

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

24 pages, 6403 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://doi.org/10.3390/drones8060235 - 30 May 2024
Viewed by 1551
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

24 pages, 1883 KiB  
Review
Applications of GANs to Aid Target Detection in SAR Operations: A Systematic Literature Review
by Vinícius Correa, Peter Funk, Nils Sundelius, Rickard Sohlberg and Alexandre Ramos
Drones 2024, 8(9), 448; https://doi.org/10.3390/drones8090448 - 31 Aug 2024
Viewed by 1763
Abstract
Research on unmanned autonomous vehicles (UAVs) for search and rescue (SAR) missions is widespread due to its cost-effectiveness and enhancement of security and flexibility in operations. However, a significant challenge arises from the quality of sensors, terrain variability, noise, and the sizes of [...] Read more.
Research on unmanned autonomous vehicles (UAVs) for search and rescue (SAR) missions is widespread due to its cost-effectiveness and enhancement of security and flexibility in operations. However, a significant challenge arises from the quality of sensors, terrain variability, noise, and the sizes of targets in the images and videos taken by them. Generative adversarial networks (GANs), introduced by Ian Goodfellow, among their variations, can offer excellent solutions for improving the quality of sensors, regarding super-resolution, noise removal, and other image processing issues. To identify new insights and guidance on how to apply GANs to detect living beings in SAR operations, a PRISMA-oriented systematic literature review was conducted to analyze primary studies that explore the usage of GANs for edge or object detection in images captured by drones. The results demonstrate the utilization of GAN algorithms in the realm of image enhancement for object detection, along with the metrics employed for tool validation. These findings provide insights on how to apply or modify them to aid in target identification during search stages. Full article
(This article belongs to the Special Issue UAV Detection, Classification, and Tracking)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop