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.
Real-Time Drone Detection Using Deep Learning
Advanced Systems Laboratory Defence Research & Development Organisation
Advances in Engineering res
International Conference on Emerging Trends in Engineering ; 2023 ; Hyderabad, India April 28, 2023 - April 30, 2023
Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023) ; Chapter : 91 ; 905-918
2023-11-05
14 pages
Article/Chapter (Book)
Electronic Resource
English
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