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UIC Code Recognition Using Computer Vision and LSTM Networks

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Dependable Computing - EDCC 2020 Workshops (EDCC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1279))

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Abstract

UIC code is a key data for railway operations. This paper presents a method for UIC code recognition on locomotive and wagon. The approach is based on computer vision, to gain high-level understanding from digital images, and LSTM, a specific neural network with relevant performance in optical character recognition. Experimental results show that the proposed method has a good localization and recognition performance in complex scene, to improve the logistic and safety of a railway infrastructure.

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Author information

Authors and Affiliations

  1. Computer Vision and Multimedia Lab, University of Pavia, Pavia, Italy

    Roberto Marmo

Authors
  1. Roberto Marmo
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Corresponding author

Correspondence to Roberto Marmo .

Editor information

Editors and Affiliations

  1. University of Zaragoza, Zaragoza, Spain

    Simona Bernardi

  2. University of Naples Federico II, Naples, Italy

    Valeria Vittorini

  3. Linnaeus University, Växjö, Sweden

    Francesco Flammini

  4. University of Reggio Calabria, Reggio Calabria, Italy

    Roberto Nardone

  5. University of Naples Federico II, Naples, Italy

    Stefano Marrone

  6. Fraunhofer IESE, Kaiserslautern, Germany

    Rasmus Adler

  7. Fraunhofer IESE, Kaiserslautern, Germany

    Daniel Schneider

  8. Fraunhofer IKS, Munich, Germany

    Philipp Schleiß

  9. Resiltech s.r.l., Pontedera, Italy

    Nicola Nostro

  10. Aalborg University, Aalborg, Denmark

    Rasmus Løvenstein Olsen

  11. University of L'Aquila, L’Aquila, Italy

    Amleto Di Salle

  12. National Institute of Aerospace, Langley Research Center, Hampton, USA

    Paolo Masci

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Marmo, R. (2020). UIC Code Recognition Using Computer Vision and LSTM Networks. In: Bernardi, S., et al. Dependable Computing - EDCC 2020 Workshops. EDCC 2020. Communications in Computer and Information Science, vol 1279. Springer, Cham. https://doi.org/10.1007/978-3-030-58462-7_8

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  • DOI: https://doi.org/10.1007/978-3-030-58462-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58461-0

  • Online ISBN: 978-3-030-58462-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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