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Image Based Approaches for Tunnels’ Defects Recognition via Robotic Inspectors

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

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Abstract

In this paper we present a visual based approach, utilized for the detection of concrete defects in tunnels. The detection mechanism is a hybrid approach, based on both image processing and deep learning models. Initial detections are validated by an expert, in order to create a robust data set in short time, saving resources during annotation process. Then, a deep-learning classifier is trained and applied for the inspection. The fully automated system, performs well, in various environments, and can be, easily, implemented with most robotic systems.

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Acknowledgments

The research leading to these results has received funding from the EC FP7 project ROBO-SPECT (Contract N.611145). Authors wish to thank all partners within the ROBO-SPECT consortium. The work has been, also, partially supported by IKY Fellowships of excellence for postgraduate studies in Greece-Siemens program.

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Authors and Affiliations

  1. Technical University of Crete, 73100, Chania, Greece

    Eftychios Protopapadakis

  2. National Technical University of Athens, 9 Iroon Polytechneiou, 15780, Athens, Greece

    Nikolaos Doulamis

  3. Institute of Communication and Computer Systems, Athens, Greece

    Eftychios Protopapadakis & Nikolaos Doulamis

Authors
  1. Eftychios Protopapadakis
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  2. Nikolaos Doulamis
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Corresponding author

Correspondence to Eftychios Protopapadakis .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Protopapadakis, E., Doulamis, N. (2015). Image Based Approaches for Tunnels’ Defects Recognition via Robotic Inspectors. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_63

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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