On-board drone classification with Deep Learning and System-on-Chip implementation

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 Field Programmable Gate Array (FPGA) technology. FPGAs are considered advantageous over conventional processors since they enable parallelism and can be used to create high-speed, low-power, and low-latency circuit designs and so to satisfy the stringent Size, weight and Power (SWaP) requirements of a drone-based implementation. In details, two different YOLO neural networks YOLO v3 and v8 are trained and evaluated on a large data set constructed with drones’ images at various distances. The two models are then implemented on a System-on-Chip (SoC). In order to demonstrate the feasibility of a drone on board image Artificial Intelligence processing, the evaluation assesses the accuracy of classification and the computational performances such as latency.