Abstract
Robotic interventions in post-disaster environments to carry out search and rescue explorations allow optimizing time for identifying victims and safeguarding the rescuer’s integrity. The rising in the neural networks field and their application in image detection algorithms have made it possible to facilitate the early detection of victims in these first phases of exploration. This article analyses the effectiveness of applying neural network models obtained from training with different datasets of both: images captured in real environments and synthetic images from recreated virtual post-disaster environments for detecting victims. For this development, tests have been carried out in environments at the ETSII-Universidad Politécnica de Madrid, generating models from images obtained with the ARTU-R robot (A1 Rescue Task UPM Robot) and have been validated with real search and rescue exercises at the University of Malaga. The main results show that the models obtained from virtual environments apply to real ones.
This work has received funding from the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub “Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase IV”; S2018/NMT-4331), funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU. and TASAR (Team of Advanced Search And Rescue Robots), funded by “Proyectos de I+D+i del Ministerio de Ciencia, Innovacion y Universidades. Financed by MCIN/AEI/10.13039/501100011033.
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Acknowledgment
Special acknowledgment to the organization of the “JORNADAS INTERNACIO- NALES DE LA UNIVERSIDAD DE MÁLAGA SOBRE SEGURIDAD, EMERGEN- CIAS Y CATÁSTROFES 2022”.
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Cruz Ulloa, C., Garcia, M., del Cerro, J., Barrientos, A. (2023). Deep Learning for Victims Detection from Virtual and Real Search and Rescue Environments. 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_1
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