Signals Intelligence Based Drone Detection Using YOLOv8 Models

Mihajlo Protic; Luka Jovanovic; Milos Dobrojevic; Miroslav Cajic; Miodrag Zivkovic; Hothefa Shaker; Nebojsa Bacanin

doi:10.2991/978-94-6463-482-2_6

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Signals Intelligence Based Drone Detection Using YOLOv8 Models

Authors

Mihajlo Protic¹, Luka Jovanovic¹, Milos Dobrojevic¹, Miroslav Cajic², Miodrag Zivkovic¹, Hothefa Shaker³, Nebojsa Bacanin¹^{, *}

¹Singidunum University, Belgrade, 11000, Serbia

²Faculty of Information Technology and Engineering, University “Union Nikola Tesla”, Belgrade, Serbia

³Modern College of Business and Science, Muscat, Oman

^*Corresponding author. Email: nbacanin@singidunum.ac.rs

Corresponding Author

Nebojsa Bacanin

Available Online 23 August 2024.

DOI: 10.2991/978-94-6463-482-2_6 How to use a DOI?
Keywords: Unmanned Aerial Vehicles; Signals Intelligence; YOLOv8; Computer vision; Radio frequency spectrum
Abstract: The reduced costs associated with deploying and utilizing Unmanned Aerial Vehicles (UAVs) have spurred their widespread adoption across various industries, including aerial photography, information gathering, and search and rescue operations. However, this rapid uptake has also raised concerns regarding safety and privacy, particularly due to instances of misuse and potential hazards posed by convertible drone technology. Addressing these concerns, this study investigates the application of emerging Artificial Intelligence (AI) techniques in computer vision for the detection and classification of ISM band transmissions, distinguishing between conventional Bluetooth signals and those used for drone control. Several YOLOv8 architectures, optimized for lighter hardware, are evaluated using a publicly available ISM band visual dataset. Results demonstrate that even lighter models, such as nano and small architectures, can achieve significant precision rates, with the best-performing models reaching a peak precision of 90%. However, medium-sized architectures are recommended for optimal performance.
Copyright: © 2024 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
Series: Advances in Computer Science Research
Publication Date: 23 August 2024
ISBN: 978-94-6463-482-2
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-482-2_6 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Mihajlo Protic
AU  - Luka Jovanovic
AU  - Milos Dobrojevic
AU  - Miroslav Cajic
AU  - Miodrag Zivkovic
AU  - Hothefa Shaker
AU  - Nebojsa Bacanin
PY  - 2024
DA  - 2024/08/23
TI  - Signals Intelligence Based Drone Detection Using YOLOv8 Models
BT  - Proceedings of the 2nd International Conference on Innovation in Information Technology and Business (ICIITB 2024)
PB  - Atlantis Press
SP  - 74
EP  - 86
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-482-2_6
DO  - 10.2991/978-94-6463-482-2_6
ID  - Protic2024
ER  -

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