Abstract
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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The authors would like to thank to Universidad Militar Nueva Granada for the financing of the project INV_ING_3189.
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Pulido, C., Ceron, A. (2022). Towards Real-Time Drone Detection Using Deep Neural Networks. In: Rocha, Á., Fajardo-Toro, C.H., Rodríguez, J.M.R. (eds) Developments and Advances in Defense and Security . Smart Innovation, Systems and Technologies, vol 255. Springer, Singapore. https://doi.org/10.1007/978-981-16-4884-7_12
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DOI: https://doi.org/10.1007/978-981-16-4884-7_12
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