Conference proceeding
Long-Range Drone Detection Dataset
2024 IEEE International Conference on Consumer Electronics (ICCE), pp 1-6
06 Jan 2024
Abstract
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 paper, 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. For access to the complete Long-Range Drone Detection Dataset (LRDD), please visit https://research.coe.drexel.edu/ece/imaple/long-range-drone-detection-dataset/.
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21 Record Views
6 citations in Scopus
Details
- Title
- Long-Range Drone Detection Dataset
- Creators
- Amirreza Rouhi - Drexel UniversityHimanshu Umare - Drexel UniversitySneh Patal - Drexel UniversityRitik Kapoor - Drexel UniversityNamit Deshpande - Drexel UniversitySolmaz Arezoomandan - Drexel UniversityPrincie Shah - West Windsor-Plainsboro High School South,West Windsor,NJ,USADavid Han - Drexel University
- Publication Details
- 2024 IEEE International Conference on Consumer Electronics (ICCE), pp 1-6
- Publisher
- IEEE
- Number of pages
- 6
- Grant note
- Federal Aviation Administration (10.13039/100006282)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Scopus ID
- 2-s2.0-85187015937
- Other Identifier
- 991021930833504721