For the safe and efficient deployment of unmanned aerial vehicles (UAVs) in complex urban landscapes, robust collision avoidance mechanisms are imperative. Although several methodologies exist for drone detection, current solutions are suboptimal for long-range detection, primarily due to the scarcity of comprehensive training datasets. In this work, we present a novel long-range drone detection dataset, encompassing a set of different UAV types, flight patterns, and environmental conditions. Utilizing this dataset, we trained a state-of-the-art YOLO object detection algorithm, demonstrating the ability to identify drones at distances up to 60 meters with a high mean average precision (mAP). Extensive real-world tests affirm the efficacy of our approach, achieving a detection accuracy exceeding 75%. This dataset and the accompanying machine learning model contribute a significant advancement in the realm of long-range drone detection, particularly well-suited for urban deployments.
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If you find this dataset useful in your work, please consider citing the dataset paper:
@INPROCEEDINGS{rouhi2024_LRDD,
author={Rouhi, Amirreza and Umare, Himanshu and Patal, Sneh and Kapoor, Ritik and Deshpande, Namit and Arezoomandan, Solmaz and Shah, Princie and Han, David},
booktitle={2024 IEEE International Conference on Consumer Electronics (ICCE)},
title={Long-Range Drone Detection Dataset},
year={2024},
volume={},
number={},
pages={1-6},
keywords={YOLO;Training;Meters;Autonomous aerial vehicles;Detection algorithms;Task analysis;Drones;Drone Detection;Drone Imagery;databases;artificial intelligence;computer vision;unmanned aerial vehicles},
doi={10.1109/ICCE59016.2024.10444135}
}