On-board drone classification with Deep Learning and System-on-Chip implementation
G Di Cecio,
A Manco,
G Gigante
Abstract:In recent years the increasing use of drones has raised significant concerns on safety and make them dramatic threats to security. To address these worries Counter-UAS Systems (CUS) are capturing the interest of research and of industry. Consequently, the development of effective drone detection technologies has become a critical research focus. The proposed work explores the application of edge computing to drone classification. It tunes a Deep Learning model, You Only Look Once (YOLO), and implements it on a… Show more
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