Drone-based monitoring on the edge using a high-resolution payload

In this study, we investigate the feasibility of performing real-time object detection on an edge devices using 100 MP images. To demonstrate the monitoring capabilities over wide areas, we captured images of a peri-urban scenario containing vehicles and pedestrians and using a DJI M300 drone equipped with an iXM-100 Phase One camera. We fine-tuned a YOLOX-Tiny object detector on the VisDrone2019 dataset, achieving a mean average precision of 0.32 at IOU=0.5. Subsequently, we deployed the detector on a Jetson ORIN AGX board using tensoRT Nvidia framework and performing FP16 quantization. The so obtained YOLOX model was applied to the dataset collected using the iXM payload, employing extensively the sliding window technique. Our experiments demonstrate the trade-off between achieving real-time processing, i.e., 3 frames per second for the current setup, while maintaining the ability to detect an average of 200 targets per image. Additionally, we showcased the capability of detecting pedestrians up to 800 m away and vehicles up to 1 km away.