Automated Rebar Detection for Ground-Penetrating Radar

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)

Abstract

Automated rebar detection in images from ground-penetrating radar (GPR) is a challenging problem and difficult to perform in real-time as a result of relatively low contrast images and the size of the images. This paper presents a rebar localization algorithm, which can accurately locate the pixel locations of rebar within a GPR scan image. The proposed algorithm uses image classification and statistical methods to locate hyperbola signatures within the image. The proposed approach takes advantage of adaptive histogram equalization to increase the visual signature of rebar within the image despite low contrast. A Naive Bayes classifier is used to approximately locate rebar within the image with histogram of oriented gradients feature vectors. In addition, a histogram based method is applied to more precisely locate individual rebar in the image, and then the proposed methods are validated using existing GPR data and data collected during the course of the research for this paper.

Notes

Acknowledgment

The authors would like to thank the University of Nevada, Reno and the National Science Foundation (NSF) for their financial support to conduct this research: NSF support under grant: NSF-IIP # 1639092.

References

  1. 1.
    Simi, A., Manacorda, G., Benedetto, A.: Bridge deck survey with high resolution ground penetrating radar. In: 2012 14th International Conference on Ground Penetrating Radar (GPR), pp. 489–495 (2012)Google Scholar
  2. 2.
    Krause, V., Abdel-Qader, I., Abudayyeh, O.: Detection and classification of small perturbations in GPR scans of reinforced concrete bridge decks. In: 2012 IEEE International Conference on Electro/Information Technology (EIT), pp. 1–4 (2012)Google Scholar
  3. 3.
    Hai-zhong, Y., Yu-feng, O., Hong, C.: Application of ground penetrating radar to inspect the metro tunnel. In: 2012 14th International Conference on Ground Penetrating Radar (GPR), pp. 759–763 (2012)Google Scholar
  4. 4.
    Marecos, V., Fontul, S., Antunes, M.L., Solla, M.: Assessment of a concrete pre-stressed runway pavement with ground penetrating radar. In: 2015 8th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), pp. 1–4 (2015)Google Scholar
  5. 5.
    Shaw, M., Millard, S., Molyneaux, T., Taylor, M., Bungey, J.: Location of steel reinforcement in concrete using ground penetrating radar and neural networks. NDT E Int. 38, 203–212 (2005). Structural Faults and RepairCrossRefGoogle Scholar
  6. 6.
    Kaur, P., Dana, K.J., Romero, F.A., Gucunski, N.: Automated GPR rebar analysis for robotic bridge deck evaluation. IEEE Trans. Cybern. 46, 2265–2276 (2016)CrossRefGoogle Scholar
  7. 7.
    Al-Nuaimy, W., Huang, Y., Nakhkash, M., Fang, M., Nguyen, V., Eriksen, A.: Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition. J. Appl. Geophys. 43, 157–165 (2000)CrossRefGoogle Scholar
  8. 8.
    La, H.M., Lim, R.S., Basily, B.B., Gucunski, N., Yi, J., Maher, A., Romero, F.A., Parvardeh, H.: Mechatronic systems design for an autonomous robotic system for high-efficiency bridge deck inspection and evaluation. IEEE/ASME Trans. Mechatron. 18, 1655–1664 (2013)CrossRefGoogle Scholar
  9. 9.
    La, H.M., Gucunski, N., Kee, S.H., Yi, J., Senlet, T., Nguyen, L.: Autonomous robotic system for bridge deck data collection and analysis. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1950–1955 (2014)Google Scholar
  10. 10.
    Xianqi-He, Z.-Z., Guangyin-Lu, Q.-L.: Bridge management with GPR. In: 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, vol. 3, pp. 325–328 (2009)Google Scholar
  11. 11.
    Pasolli, E., Melgani, F., Donelli, M.: Automatic analysis of GPR images: a pattern-recognition approach. IEEE Trans. Geosci. Remote Sens. 47, 2206–2217 (2009)CrossRefGoogle Scholar
  12. 12.
    Zhao, Y., Chen, J., Ge, S.: Maxwell curl equation datuming for GPR test of tunnel grouting based on kirchhoff integral solution. In: 2011 6th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), pp. 1–6 (2011)Google Scholar
  13. 13.
    Shi, H., Liu, Y.: Naïve Bayes vs. support vector machine: resilience to missing data. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds.) AICI 2011. LNCS (LNAI), vol. 7003, pp. 680–687. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23887-1_86 CrossRefGoogle Scholar
  14. 14.
    Wang, Z.W., Zhou, M., Slabaugh, G.G., Zhai, J., Fang, T.: Automatic detection of bridge deck condition from ground penetrating radar images. IEEE Trans. Autom. Sci. Eng. 8, 633–640 (2011)CrossRefGoogle Scholar
  15. 15.
    Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B.T.H., Zimmerman, J.B.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39, 355–368 (1987)CrossRefGoogle Scholar
  16. 16.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893 (2005)Google Scholar
  17. 17.
    Pang, Y., Zhang, K., Yuan, Y., Wang, K.: Distributed object detection with linear SVMs. IEEE Trans. Cybern. 44, 2122–2133 (2014)CrossRefGoogle Scholar
  18. 18.
    Nigam, S., Khare, M., Srivastava, R.K., Khare, A.: An effective local feature descriptor for object detection in real scenes. In: 2013 IEEE Conference on Information Communication Technologies (ICT), pp. 244–248 (2013)Google Scholar
  19. 19.
    Lim, R.S., La, H.M., Shan, Z., Sheng, W.: Developing a crack inspection robot for bridge maintenance. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 6288–6293 (2011)Google Scholar
  20. 20.
    Lim, R.S., La, H.M., Sheng, W.: A robotic crack inspection and mapping system for bridge deck maintenance. IEEE Trans. Autom. Sci. Eng. 11, 367–378 (2014)CrossRefGoogle Scholar
  21. 21.
    La, H.M., Gucunski, N., Kee, S.H., Nguyen, L.V.: Data analysis and visualization for the bridge deck inspection and evaluation robotic system. Visual. Eng. 3, 6 (2015)CrossRefGoogle Scholar
  22. 22.
    La, H.M., Gucunski, N., Lee, S.H., Nguyen, L.V.: Visual and acoustic data analysis for the bridge deck inspection robotic system. In: The 31st International Symposium on Automation and Robotics in Construction and Mining (ISARC), pp. 50–57 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of NevadaRenoUSA

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