Ponencia
Benchmark on real-time long-range aircraft detection for safe RPAS operations
Autor/es | Alarcón, Víctor
Santana, Pablo Ramos, Francisco Pérez-Grau, Francisco Javier Viguria, Antidio Ollero Baturone, Aníbal |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2023 |
Fecha de depósito | 2023-03-14 |
Publicado en |
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ISBN/ISSN | 978-303121061-7 2367-3370 |
Resumen | 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) ... 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. |
Agencias financiadoras | Unión Europea. Horizonte 2020 Centro para el Desarrollo Tecnológico Industrial |
Identificador del proyecto | 955269
CER-20211022 |
Cita | Alarcón, V., Santana, P., Ramos, F., Pérez-Grau, F.J., Viguria, A. y Ollero Baturone, A. (2023). Benchmark on real-time long-range aircraft detection for safe RPAS operations. En 5th Iberian Robotics Conference, ROBOT 2022, Lecture Notes in Networks and Systems, Volume 590 (341-352). |
Ficheros | Tamaño | Formato | Ver | Descripción |
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LNNS_2023_Alarcon_Ollero_Bench ... | 17.85Mb | [PDF] | Ver/ | |
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