The growing use of drones/unmanned aerial vehicles (UAV’s) has generated in the general public as well as in institutions the awareness of the possible misuse that can be given to these devices. For this reason, the detection of drones has become a necessity that poses in the case of detection using images a great difficulty due to its small size, in addition to the background that can make detection even more difficult. Regarding this work, the aim is not only to detect drones but also to distinguish the type of detected drone. Different networks were trained whereupon an analysis was conducted in which it was found that the YOLOv4tiny network with 416 × 416 input resolution presents the best results considering both the inference performance and the processing speed.


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    Titel :

    Towards Real-Time Drone Detection Using Deep Neural Networks


    Weitere Titelangaben:

    Smart Innovation, Systems and Technologies


    Beteiligte:
    Rocha, Álvaro (Herausgeber:in) / Fajardo-Toro, Carlos Hernan (Herausgeber:in) / Rodríguez, José María Riola (Herausgeber:in) / Pulido, Cristhiam (Autor:in) / Ceron, Alexander (Autor:in)


    Erscheinungsdatum :

    29.10.2021


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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