  Drone-to-drone detection using visual feed has crucial applications, such as
detecting drone collisions, detecting drone attacks, or coordinating flight
with other drones. However, existing methods are computationally costly, follow
non-end-to-end optimization, and have complex multi-stage pipelines, making
them less suitable for real-time deployment on edge devices. In this work, we
propose a simple yet effective framework, \textit{TransVisDrone}, that provides
an end-to-end solution with higher computational efficiency. We utilize
CSPDarkNet-53 network to learn object-related spatial features and VideoSwin
model to improve drone detection in challenging scenarios by learning
spatio-temporal dependencies of drone motion. Our method achieves
state-of-the-art performance on three challenging real-world datasets (Average
Precision@0.5IOU): NPS 0.95, FLDrones 0.75, and AOT 0.80, and a higher
throughput than previous methods. We also demonstrate its deployment capability
on edge devices and its usefulness in detecting drone-collision (encounter).
Project: \url{https://tusharsangam.github.io/TransVisDrone-project-page/}.
