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Mine Clearance through an Artificial Intelligence Flying Drone

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Advances in Nonlinear Dynamics

Part of the book series: NODYCON Conference Proceedings Series ((NCPS))

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Abstract

This work presents an application to drone technology based on an algorithm which combines a convolutional neural network (CNN) and a symbolic data analysis (SDA) process to detect anti-personnel mines from GPR data acquisitions. The CNN is aimed at automatically detecting buried objects; the SDA reduces the probability of objects identified as mines, even though they are not.

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Acknowledgements

The present project has been funded by the L’Oréal-UNESCO Foundation through the “For Women In Science” prize, 2019 edition.

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Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Rome, Italy

    Federica Mezzani, Gianluca Pepe, Nicola Roveri & Antonio Carcaterra

Authors
  1. Federica Mezzani
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  2. Gianluca Pepe
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  3. Nicola Roveri
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  4. Antonio Carcaterra
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Corresponding author

Correspondence to Federica Mezzani .

Editor information

Editors and Affiliations

  1. Department of Structural and Geotechnical Engineering, Sapienza University of Rome, Rome, Italy

    Walter Lacarbonara

  2. Department of Mechanical Engineering, University of Maryland, College Park, MD, USA

    Balakumar Balachandran

  3. Department of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Michael J. Leamy

  4. Department of Physics, Lanzhou University of Technology, Lanzhou, Gansu, China

    Jun Ma

  5. ISEP-Institute of Engineering, Polytechnic of Porto, Porto, Portugal

    J. A. Tenreiro Machado

  6. Department of Applied Mechanics, Budapest University of Technology and Economics, Budapest, Hungary

    Gabor Stepan

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Mezzani, F., Pepe, G., Roveri, N., Carcaterra, A. (2022). Mine Clearance through an Artificial Intelligence Flying Drone. In: Lacarbonara, W., Balachandran, B., Leamy, M.J., Ma, J., Tenreiro Machado, J.A., Stepan, G. (eds) Advances in Nonlinear Dynamics. NODYCON Conference Proceedings Series. Springer, Cham. https://doi.org/10.1007/978-3-030-81166-2_37

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  • DOI: https://doi.org/10.1007/978-3-030-81166-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81165-5

  • Online ISBN: 978-3-030-81166-2

  • eBook Packages: EngineeringEngineering (R0)

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