| DOI | Resolve DOI: https://doi.org/10.1109/AIPR60534.2023.10440658 |
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| Author | Search for: Drouin, Marc-Antoine1; Search for: Rainville, Marc-André2; Search for: Picard, Michel1; Search for: Stewart, Terrence C.1; Search for: Djupkep Dizeu, Frank Billy1; Search for: Gagné, Guillaume |
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| Affiliation | - National Research Council of Canada. Digital Technologies
- National Research Council of Canada. Medical Devices
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| Format | Text, Article |
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| Conference | 2023 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), September 27-29, 2023, St. Louis, Missouri, United States |
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| Subject | quadcopter; Uncrewed Aerial Systems (UAS); neuromorphic camera; event camera; Machine Learning (ML); labeled training dataset |
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| Abstract | The use of computer graphic tools typically associated with video games is a popular method to generate synthetic datasets for the training of machine learning algorithms. For optical detection of quadcopters, realistic imagery needs to be generated for multiple models of drones, in multiple types of environments and different flight profiles. By itself, the effort required to generate those virtual environments can be as important as flying actual drones. This is particularly true when a physics-based engine is required to model the behavior of the propellers. While some appearance-based drone detection methods may not need accurate propeller behavior, other detection methods that exploit the high-frequency and/or temporal signatures generated by rotating propellers do require such accurate simulations. This is especially the case for neuromorphic sensors, which are generally sensitive to the unique high-frequency visual signal from the propeller blades. We propose a synthetic approach to acquire training datasets for neuromorphic sensors using a flexible hardware system built from quadcopter components. This system allows efficient acquisition of training sets for drone detection sensors based on the propeller’s temporal and/or frequency signature. |
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| Publication date | 2024-02-22 |
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| Publisher | IEEE |
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| In | |
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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 | a811de90-a319-42e2-9cdc-bf2a83f9253a |
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| Record created | 2024-03-25 |
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| Record modified | 2024-03-25 |
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