Abstract
The growing market in Remotely Piloted Aircraft Systems (RPAS) and the need for cost-effective “Detect and Avoid (DAA)” systems are critical issues up to date towards enabling safe beyond visual line of sight (BVLOS) operations. In hopes of promoting earlier threat detection on DAA systems, we benchmark several object detection algorithms on multiple graphical processing units for the concrete DAA use case. Two state-of-the-art “real-time object detection” and “object detection” model sets are trained using our CENTINELA dataset, and their performances are compared for a wide range of configurations. Results demonstrate that one-stage architecture YOLO variants outperform ViT on all tested hardware in terms of mean average precision and inference speed despite their architecture complexity gap. Additional resources are available to the reader at https://github.com/fada-catec/detection-for-safe-rpas-operation.
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Acknowledgements
This work has been partially supported by the OMICRON project, funded by the EU H2020 programme under grant agreement 955269, and CEL.IA, a Cervera Network for applied artificial intelligence, funded by the Spanish government through CDTI (CER-20211022).
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Alarcón, V., Santana, P., Ramos, F., Pérez-Grau, F.J., Viguria, A., Ollero, A. (2023). Benchmark on Real-Time Long-Range Aircraft Detection for Safe RPAS Operations. In: Tardioli, D., Matellán, V., Heredia, G., Silva, M.F., Marques, L. (eds) ROBOT2022: Fifth Iberian Robotics Conference. ROBOT 2022. Lecture Notes in Networks and Systems, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-031-21062-4_28
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