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

• URL: http://arxiv.org/abs/2404.19276v1
• Date: Tue, 30 Apr 2024 05:51:21 GMT
• Title: C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks
• Authors: Sairam VC Rebbapragada, Pranoy Panda, Vineeth N Balasubramanian,
• Abstract summary: A vision-based drone-to-drone detection system is crucial for various applications like collision avoidance, countering hostile drones, and search-and-rescue operations. detecting drones presents unique challenges, including small object sizes, distortion, and real-time processing requirements. We propose a novel coarse-to-fine detection strategy based on vision transformers.
• Score: 23.133250476580038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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