Real-Time Drone Detection Using Deep Learning

Advanced Systems Laboratory Defence Research & Development Organisation

DOI

10.2991/978-94-6463-252-1_91How to use a DOI?

Keywords

Deep Learning; Object detection; Drone; YOLO (You Only Look Once); SSD (Single Shot Detector)

Abstract

Drone detection refers to the process of identifying the presence of unmanned aerial vehicles (UAVs) or drones within a specific airspace. This technology has become increasingly important in recent years due to the growing popularity and use of drones for both civilian and military purposes. With the increasing usage of drones, there is a growing concern over the potential risks they pose, such as privacy invasion, malicious activities, and collisions with other aircraft. It is a critical security measure to prevent unauthorized drone activities like espionage, smuggling, and terrorism. Drone detection technology employs a variety of methods, including radar, acoustic sensors, and video cameras. These systems are integrated with software algorithms to accurately detect and track drones in real-time. This paper primarily focuses on real-time drone detection using deep learning methods to detect real-time UAVs. For the anti-drone system, we are using the YOLOv5 algorithm. Our experiment has shown that the YOLOv5 model produces better accuracy and maintains high detection speed.

Copyright

© 2023 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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TY  - CONF
AU  - K. Sricharan
AU  - M. Venkat
PY  - 2023
DA  - 2023/11/09
TI  - Real-Time Drone Detection Using Deep Learning
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 905
EP  - 918
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_91
DO  - 10.2991/978-94-6463-252-1_91
ID  - Sricharan2023
ER  -