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Instance Segmentation Applied to Underground Infrastructures

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Image Analysis and Processing - ICIAP 2023 Workshops (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14365))

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Abstract

As underground infrastructures suffer from strikes during the ground excavation procedure resulting from poor subsurface utilities documentation, this paper aims to improve the mapping of such elements by integrating instance segmentation techniques. To perform supervised training of the well-known Mask R-CNN architecture, we created our own dataset based on surveys that we have access to from the SYSLOR company, resulting in around 2600 labelled images. Through several training sessions, performed with K-fold cross validation, we studied the level of contribution of each construction sites selected for the dataset creation. Currently we achieved a very good 38.4 mean average precision (mAP) on three defined classes: sheaths, twisted pipes and smooth pipes.

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Author information

Authors and Affiliations

  1. SYSLOR SAS, 57000, Metz, France

    R. Haenel, Q. Semler & E. Semin

  2. ICUBE Laboratory UMR 7357, Photogrammetry and Geomatics Group, University of Strasbourg, CNRS, INSA Strasbourg, 67000, Strasbourg, France

    R. Haenel & P. Grussenmeyer

  3. University of Lorraine, CNRS LORIA UMR7503, Campus Scientifique, 54506, Vandoeuvre-Lès-Nancy, France

    S. Tabbone

Authors
  1. R. Haenel
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  2. Q. Semler
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  3. E. Semin
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  4. S. Tabbone
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  5. P. Grussenmeyer
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Corresponding author

Correspondence to R. Haenel .

Editor information

Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

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Haenel, R., Semler, Q., Semin, E., Tabbone, S., Grussenmeyer, P. (2024). Instance Segmentation Applied to Underground Infrastructures. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023. Lecture Notes in Computer Science, vol 14365. Springer, Cham. https://doi.org/10.1007/978-3-031-51023-6_1

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  • DOI: https://doi.org/10.1007/978-3-031-51023-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51022-9

  • Online ISBN: 978-3-031-51023-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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