C2FDrone: Coarse-to-Fine Drone-to-Drone Detection Using Vision Transformer Networks

A vision-based drone-to-drone detection system offers a cost-effective solution for a range of applications, including collision avoidance, countering hostile drones, and enhancing search-and-rescue operations. However, drone-to-drone detection presents a more intricate set of challenges compared to regular object detection. These challenges encompass the need to detect extremely small-sized objects, contend with strong distortion, handle severe occlusion, operate in uncontrolled environments, and execute real-time processing. While current methods attempt to address these issues by integrating multi-scale feature fusion and temporal information, we propose that these techniques may not be sufficiently equipped to handle extreme blur and minuscule objects. Instead, we put forth a novel coarse-to-fine detection strategy based on vision transformers to achieve precise drone detection. We assess the effectiveness of our approach through a series of comprehensive experiments conducted on three challenging drone-to-drone detection datasets. Our results demonstrate notable improvements, with F1 score enhancements of 7%, 3%, and 1% on the FL-Drones, AOT, and NPS-Drones datasets, respectively. Furthermore, we showcase its real-time processing capability by deploying our model on an edge-computing device. We will make our code repository publicly available.