| DOI | Trouver le DOI : https://doi.org/10.1109/DASC58513.2023.10311339 |
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| Auteur | Rechercher : Azad, Hamid; Rechercher : Mehta, Varun1; Rechercher : Dadboud, Fardad; Rechercher : Bolic, Miodrag; Rechercher : Mantegh, Iraj1 |
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| Affiliation | - Conseil national de recherches du Canada. Aérospatiale
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| Format | Texte, Article |
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| Conférence | 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC), October 1-5, 2023, Barcelona, Spain |
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| Sujet | drone; uncrewed aerial vehicle (UAV); counter-UAV measures; simulated dataset; training; atmospheric modeling; optical sensors; load modeling; meteorology; synthetic data |
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| Résumé | 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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| Date de publication | 2023-10-01 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Données connexes | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 747cda8f-4303-4d4a-92aa-b53dc54c8648 |
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| Enregistrement créé | 2024-06-28 |
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| Enregistrement modifié | 2024-06-28 |
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