  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.
