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.


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    Titel :

    Real-Time Drone Detection Using Deep Learning


    Untertitel :

    Advanced Systems Laboratory Defence Research & Development Organisation


    Weitere Titelangaben:

    Advances in Engineering res


    Beteiligte:
    Raj, Bhiksha (Herausgeber:in) / Gill, Steve (Herausgeber:in) / Calderon, Carlos A.Gonzalez (Herausgeber:in) / Cihan, Onur (Herausgeber:in) / Tukkaraja, Purushotham (Herausgeber:in) / Venkatesh, Sriram (Herausgeber:in) / M. S., Venkataramayya (Herausgeber:in) / Mudigonda, Malini (Herausgeber:in) / Gaddam, Mallesham (Herausgeber:in) / Dasari, Rama Krishna (Herausgeber:in)

    Kongress:

    International Conference on Emerging Trends in Engineering ; 2023 ; Hyderabad, India April 28, 2023 - April 30, 2023



    Erscheinungsdatum :

    05.11.2023


    Format / Umfang :

    14 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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