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Simultaneous Road Edge and Road Surface Markings Detection Using Convolutional Neural Networks

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Databases and Information Systems (DB&IS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1243))

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Abstract

Accurate road surface markings and road edges detection is a crucial task for operating self-driving cars and for advanced driver assistance systems deployment (e.g. lane detection) in general. This research proposes an original neural network based method that combines structural components of autoencoders, residual neural networks and densely connected neural networks. The resulting neural network is able to concurrently detect and segment accurate road edges and road surface markings from RGB images of road surfaces.

This study was partially supported by the Archimedes Foundation and Reach-U Ltd. in the scope of the smart specialization research and development project #LEP19022: “Applied research for creating a cost-effective interchangeable 3D spatial data infrastructure with survey-grade accuracy”.

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Authors and Affiliations

  1. Tallinn University of Technology, Ehitajate tee 5, 19086, Tallinn, Estonia

    René Pihlak & Andri Riid

Authors
  1. René Pihlak
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  2. Andri Riid
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Corresponding author

Correspondence to René Pihlak .

Editor information

Editors and Affiliations

  1. Department of Computer Systems, Tallinn University of Technology, Tallinn, Estonia

    Tarmo Robal

  2. Department of Software Science, Tallinn University of Technology, Tallinn, Estonia

    Hele-Mai Haav

  3. Department of Software Science, Tallinn University of Technology, Tallinn, Estonia

    Jaan Penjam

  4. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Raimundas Matulevičius

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Pihlak, R., Riid, A. (2020). Simultaneous Road Edge and Road Surface Markings Detection Using Convolutional Neural Networks. In: Robal, T., Haav, HM., Penjam, J., Matulevičius, R. (eds) Databases and Information Systems. DB&IS 2020. Communications in Computer and Information Science, vol 1243. Springer, Cham. https://doi.org/10.1007/978-3-030-57672-1_9

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  • DOI: https://doi.org/10.1007/978-3-030-57672-1_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57671-4

  • Online ISBN: 978-3-030-57672-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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