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Lightweight Density Map Architecture for UAVs Safe Landing in Crowded Areas

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Abstract

Safe landing in crowded areas is a crucial task for the successful integration of Unmanned Aerial Vehicles in common civilian applications. In this work, we propose a lightweight algorithm for identifying safe landing regions to prevent hurting people in emergency landing situations. To do so, a lightweight convolutional neural network architecture is developed to perform crowd detection and counting using fewer computer resources without a significant loss on accuracy. The architecture was trained using the Bayes loss function to improve its accuracy and then pruned to further reduce the computational resources used. The proposed architecture was tested over the USF-QNRF dataset, achieving good accuracy while maintaining a competitive number of parameters, around 0.067 Million, which is suitable for real-time embedded applications. The developed lightweight model is then used to obtain the density map of the crowd in real-time, which is employed to determine the largest circular region without persons by means of the polylabel algorithm. Real-time experiments validate the proposed strategy implemented in a low-cost mobile phone application and a commercial drone.

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Acknowledgements

This work was supported by the Mexican National Council of Science and Technology CONACYT, and the FORDECyT project 296737 “Consorcio en Inteligencia Artificial”.

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Authors and Affiliations

  1. Center for Research in Mathematics CIMAT AC, Campus Zacatecas, Avenida Lasec, Andador Galileo Galilei, Manzana 3 Lote 7, Parque Quantum, Zacatecas, 98160, Mexico

    Javier Antonio Gonzalez-Trejo

  2. Cátedras CONACyT at Center for Research in Mathematics CIMAT AC, Campus Zacatecas, Zacatecas, Mexico

    Diego A. Mercado-Ravell

Authors
  1. Javier Antonio Gonzalez-Trejo
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  2. Diego A. Mercado-Ravell
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Correspondence to Diego A. Mercado-Ravell.

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Gonzalez-Trejo, J.A., Mercado-Ravell, D.A. Lightweight Density Map Architecture for UAVs Safe Landing in Crowded Areas. J Intell Robot Syst 102, 7 (2021). https://doi.org/10.1007/s10846-021-01380-8

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