UAV detection using camera and radar

This paper presents a method for drone detection that combines the state-of-the-art YOLO-v8 model with Doppler radar to improve robustness in challenging conditions such as low light or bad weather. The YOLOv8 model is implemented on the NVIDIA Jetson Orin Nano, a power-efficient embedded platform suitable for real-time applications. We provide a brief overview of the YOLO algorithm, its working principles, and the steps taken to train the drone detection model. In addition, we describe the integration of Doppler radar using the ADALM-PLUTO phased array platform to enhance detection performance through motion-based information. Experimental results demonstrate the potential of combining visual and radar data for reliable drone detection in diverse environments.