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Visual change detection on tunnel linings

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Abstract

We describe an automated system for detecting, localising, clustering and ranking visual changes on tunnel surfaces. The system is designed to provide assistance to expert human inspectors carrying out structural health monitoring and maintenance on ageing tunnel networks. A three-dimensional tunnel surface model is first recovered from a set of reference images using Structure from Motion techniques. New images are localised accurately within the model and changes are detected versus the reference images and model geometry. We formulate the problem of detecting changes probabilistically and evaluate the use of different feature maps and a novel geometric prior to achieve invariance to noise and nuisance sources such as parallax and lighting changes. A clustering and ranking method is proposed which efficiently presents detected changes and further improves the inspection efficiency. System performance is assessed on a real data set collected using a low-cost prototype capture device and labelled with ground truth. Results demonstrate that our system is a step towards higher frequency visual inspection at a reduced cost.

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Authors and Affiliations

  1. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, UK

    Simon Stent, Kenichi Soga & Roberto Cipolla

  2. Cambridge Research Laboratory, Toshiba Research Europe Ltd., 208 Cambridge Science Park, Milton Road, Cambridge, CB4 0GZ, UK

    Riccardo Gherardi & Björn Stenger

Authors
  1. Simon Stent
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  2. Riccardo Gherardi
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  3. Björn Stenger
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  4. Kenichi Soga
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  5. Roberto Cipolla
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Corresponding author

Correspondence to Simon Stent.

Additional information

The authors gratefully acknowledge the support by Toshiba Research Europe.

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Stent, S., Gherardi, R., Stenger, B. et al. Visual change detection on tunnel linings. Machine Vision and Applications 27, 319–330 (2016). https://doi.org/10.1007/s00138-014-0648-8

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  • DOI: https://doi.org/10.1007/s00138-014-0648-8

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