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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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References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels. EPFL Tech. Report No. 149300 (2010)

  2. Chaiyasarn, K., Kim, T.-K., Viola, F., Cipolla, R., Soga, K.: Image mosaicing via quadric surface estimation with priors for tunnel inspection. In: ICIP, pp. 537–540 (2009)

  3. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. Trans. PAMI 24(5), 603–619 (2002)

    Article  Google Scholar 

  4. Delaunoy, O., Gracias, N., Garcia, R.: Towards detecting changes in underwater image sequences. In: OCEANS 2008-MTS/IEEE Kobe Techno-Ocean (2008)

  5. Dianat, R., Kasaei, S.: Change detection in optical remote sensing images using difference-based methods and spatial information. Geosci. Remote Sens. Lett. IEEE 7(1), 215–219 (2010)

    Article  Google Scholar 

  6. Jahanshahi, M.R., Masri, S.F., Padgett, C.W., Sukhatme, G.S.: An innovative methodology for detection and quantification of cracks through incorporation of depth perception. Mach. Vis. Appl. 24, 227–241 (2013)

    Article  Google Scholar 

  7. Jobson, D.J., Rahman, Z., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–76 (1997)

    Article  Google Scholar 

  8. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)

    Article  Google Scholar 

  9. Lu, D., Mausel, P., Brondízio, E., Moran, E.: Change detection techniques. Int. J. Remote Sens. 25(12), 2365–2407 (2004)

    Article  Google Scholar 

  10. Ortner, T., Paar, G., Hesina, G., Tobler, R.F., Nauschnegg, B.: Towards true underground infrastructure surface documentation. Real Corp Proceedings 2010, Vienna, pp. 18–20 (2010)

  11. Pollard, T., Mundy, J.L.: Change detection in a 3-d world. In: CVPR (2007)

  12. Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE Trans. Image Process. 14(3), 294–307 (2005)

    Article  MathSciNet  Google Scholar 

  13. Ravichandran, A., Soatto, S.: Long-range spatio-temporal modeling of video with application to fire detection. ECCV, pp. 329–342 (2012)

  14. Sakurada, K., Okatani, T., Deguchi, K.: Detecting changes in 3D structure of a scene from multi-view images captured by a vehicle-mounted camera. In: CVPR (2013)

  15. Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from internet photo collections. Int. J. Comput. Vis. 80(2), 189–210 (2008)

    Article  Google Scholar 

  16. SPACETEC (2012)

  17. Stent, S., Gherardi, R., Stenger, B., Soga, K., Cipolla,R.: An image-based system for change detection ontunnel linings. In: MVA (2013)

  18. Taneja, A., Ballan, L., Pollefeys, M.: Image based detection of geometric changes in urban environments. In: ICCV pp. 2336–2343 (2011)

  19. tCrack system by terra vermessungen ag (2012)

  20. Ukai, M.: Advanced inspection system of tunnel wall deformation using image processing. Q. Rep. RTRI 48(2), 94–98 (2007)

    Article  Google Scholar 

  21. Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008)

  22. Wu, C.: SiftGPU: a GPU implementation of scale invariant feature transform (SIFT) (2007)

  23. Wu, C.: VisualSFM: a visual structure from motion system (2011)

  24. Yamaguchi, T., Hashimoto, S.: Fast crack detection method for large-size concrete surface images using percolation-based image processing. Mach. Vis. Appl. 21, 797–809 (2010)

    Article  Google Scholar 

  25. Yu, S.-G., Jang, J.-H., Han, C.-S.: Auto inspection system using a mobile robot for detecting concrete cracks in a tunnel. Autom. Constr. 16(3), 255–261 (2007)

    Article  Google Scholar 

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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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