UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection
<|reference_start|>UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection: The rapid advancement of drone technology has made accurate Unmanned Aerial Vehicle (UAV) detection essential for surveillance, security, and airspace management. This paper presents a novel trajectory-guided approach, the Patch Intensity Convergence (PIC) technique, which generates high-fidelity bounding boxes for UAV detection without manual labeling. This technique forms the foundation of UAVDB, a dedicated database designed specifically for UAV detection. Unlike datasets that often focus on large UAVs or simple backgrounds, UAVDB utilizes high-resolution RGB video to capture UAVs at various scales, from hundreds of pixels to near-single-digit sizes. This extensive scale variation enables robust evaluation of detection algorithms under diverse conditions. Using the PIC technique, bounding boxes can be efficiently generated from trajectory or position data. We benchmark UAVDB using state-of-the-art (SOTA) YOLO series detectors, providing a comprehensive performance analysis. Our results demonstrate UAVDB's potential as a critical resource for advancing UAV detection, particularly in high-resolution and long-distance tracking scenarios.<|reference_end|>
@article{chen2024uavdb:,
    title={UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV Detection},
    author={Yu-Hsi Chen},
    journal={arXiv preprint arXiv:2409.06490},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06490},
    primaryClass={cs.CV stat.AP}
}