C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914777429704704 |
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| author | Rebbapragada, Sairam VC Panda, Pranoy Balasubramanian, Vineeth N |
| author_facet | Rebbapragada, Sairam VC Panda, Pranoy Balasubramanian, Vineeth N |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_19276 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks Rebbapragada, Sairam VC Panda, Pranoy Balasubramanian, Vineeth N Computer Vision and Pattern Recognition 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. |
| title | C2FDrone: Coarse-to-Fine Drone-to-Drone Detection using Vision Transformer Networks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.19276 |