DOI | Resolve DOI: https://doi.org/10.1109/DASC58513.2023.10311339 |
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Author | Search for: Azad, Hamid; Search for: Mehta, Varun1; Search for: Dadboud, Fardad; Search for: Bolic, Miodrag; Search for: Mantegh, Iraj1 |
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Affiliation | - National Research Council of Canada. Aerospace
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Format | Text, Article |
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Conference | 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), October 1-5, 2023, Barcelona, Spain |
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Subject | drone; uncrewed aerial vehicle (UAV); counter-UAV measures; simulated dataset; training; atmospheric modeling; optical sensors; load modeling; meteorology; synthetic data |
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Abstract | This paper introduces the multi-view Air-to-Air Simulated Drone Dataset (A2A-SDD), a comprehensive simulated drone dataset captured using AirSim © . The dataset encompasses diverse scenarios where one or two drones are pursued by one to three monitoring drones. It includes five types of drones, such as DJI models and a generic quadrotor model, recorded in various weather conditions and environments. Both loaded and unloaded drones are represented, and the dataset provides extensive annotations, including object detection and XYZ co-ordinates. The dataset offers potential applications in training deep learning-based models for counter-UAV measures such as localization and payload detection in single- and multi-view cases. Furthermore, preliminary experiments demonstrate the promising performance of trained networks on practical data, affirming the dataset’s value in addressing real-world drone challenges using optical sensors. The synthetic dataset is publicly available on GitHub (https://github.com/CARG-uOttawa/Multiview-Air-to-Air-simulated-drone-dataset). |
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Publication date | 2023-10-01 |
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Publisher | IEEE |
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In | |
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Related data | |
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Language | English |
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Peer reviewed | Yes |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | 747cda8f-4303-4d4a-92aa-b53dc54c8648 |
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Record created | 2024-06-28 |
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Record modified | 2024-06-28 |
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