A vision-based drone-to-drone detection system is crucial for various applications like collision avoidance, countering hostile drones, and search-and-rescue operations. However, detecting drones presents unique challenges, including small object sizes, distortion, occlusion, and real-time processing requirements. Current methods integrating multi-scale feature fusion and temporal information have limitations in handling extreme blur and minuscule objects. To address this, we propose a novel coarse-to-fine detection strategy based on vision transformers. We evaluate our approach on three challenging drone-to-drone detection datasets, achieving F1 score enhancements of 7%, 3%, and 1% on the FL-Drones, AOT, and NPS-Drones datasets, respectively. Additionally, we demonstrate real-time processing capabilities by deploying our model on an edge-computing device. Our code will be made publicly available.
@article{arxiv.2404.19276,
title = {C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks},
author = {Sairam VC Rebbapragada and Pranoy Panda and Vineeth N Balasubramanian},
journal= {arXiv preprint arXiv:2404.19276},
year = {2024}
}