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International Symposium on Visual Computing

ISVC 2019: Advances in Visual Computing pp 631–643Cite as

Rebar Detection and Localization for Non-destructive Infrastructure Evaluation of Bridges Using Deep Residual Networks

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

Abstract

Nondestructive Evaluation (NDE) of civil infrastructure has been an active area of research for the past few decades. Traditional inspection of civil infrastructure, mostly relying on visual inspection is time-consuming, labor-intensive and often provides subjective and erroneous results. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated manner. The purpose of this research is to provide a novel Deep Learning-based method for detection of steel rebars in reinforced concrete bridge elements using data from Ground Penetrating Radar (GPR). At the same time, a novel technique is proposed for the localization of rebar in B-scan images. In order to examine the performance of the rebar detection and localization system, results are outlined to demonstrate the feasibility of the proposed system within relevant practical applications.

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References

  1. Penn, A.: The deadliest bridge collapses in the US in the last 50 years. CNN, 15 March 2018

    Google Scholar 

  2. Kirk, R.S., Mallett, W.J.: Highway Bridge Conditions: Issues for Congress. US Congressional Research Service, Washington, D.C. (2013)

    Google Scholar 

  3. Wright, L., Chinowsky, P., Strzepek, K., et al.: Estimated effect of climate change on flood vulnerability of US bridges. Mitig. Adapt. Strat. Glob. Change 17(8), 939–955 (2012)

    Article  Google Scholar 

  4. Briaud, J.-L., Brandimarte, L., Wang, J., D’Odorico, P.: Probability of scour depth exceedance owing to hydrologic uncertainty. Georisk 1(2), 77–88 (2014)

    Google Scholar 

  5. US Department of Transportation Report: 2015 Status of the Nation’s Highways, Bridges, and Transit: Conditions and Performance, Pub. No: FHWA-PL-17-001. US Department of Transportation, Washington, DC (2015)

    Google Scholar 

  6. La, H.M., Gucunski, N., Dana, K.J., Kee, S.-H.: Development of an autonomous bridge deck inspection robotic system. J. Field Robot. 34, 1489–1504 (2017)

    Article  Google Scholar 

  7. DeVault, J.E.: Robotic system for underwater inspection of bridge piers. IEEE Instrum. Meas. Mag. 3(3), 32–37 (2000)

    Article  MathSciNet  Google Scholar 

  8. Al-Nuaimy, W., Huang, Y., et al.: Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition. J. Appl. Geophys. 43(2), 157–165 (2000)

    Article  Google Scholar 

  9. Gibb, S., La, H.M., Le, T., Nguyen, L., Schmid, R., Pham, H.: Nondestructive evaluation sensor fusion with autonomous robotic system for civil infrastructure inspection. J. Field Robot. 35, 988–1004 (2018)

    Article  Google Scholar 

  10. Diamanti, N., Redman, N.: Field observations and numerical models of GPR response from vertical pavement cracks. J. Appl. Geophys. 81, 106–116 (2012)

    Article  Google Scholar 

  11. Gucunski, N., Kee, S.-H., La, H.M., Basily, B., Maher, A.: Delamination and concrete quality assessment of concrete bridge decks using a fully autonomous RABIT platform. Struct. Monit. Maintenance 2(1), 19–34 (2015)

    Article  Google Scholar 

  12. Kaur, P., Dana, K.J., Romero, F.A., Gucunski, N.: Automated GPR rebar analysis for robotic bridge deck evaluation. IEEE Trans. Cybern. 46(10), 2265–2276 (2016)

    Article  Google Scholar 

  13. La, H.M., Lim, R.S., Du, J., Zhang, S., Yan, G., Sheng, W.: Development of a small-scale research platform for intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 13(4), 1753–1762 (2012)

    Article  Google Scholar 

  14. La, H.M., Gucunski, N., Kee, S.-H., Nguyen, L.V.: Data analysis and visualization for the bridge deck inspection and evaluation robotic system. J. Visual. Eng. 3(6), 1–16 (2015)

    Google Scholar 

  15. Mazzeo, B.A., Larsen, J., McElderry, J., Guthrie, W.S.: Rapid multichannel impact-echo scanning of concrete bridge decks from a continuously moving platform. In: Proceedings of 43rd Annual Review of Progress in Quantitative Nondestructive Evaluation, vol. 1806, pp. 1–6 (2017)

    Google Scholar 

  16. Wang, Z.W., Zhou, M., Slabaugh, G.G., Zhai, J., Fang, T.: Automatic detection of bridge deck condition from ground penetrating radar images. IEEE TASE 8(3), 633–640 (2011)

    Google Scholar 

  17. Gamba, P., Lossani, S.: Neural detection of pipe signatures in ground penetrating radar images. IEEE Trans. Geosci. Remote Sens. 38(2), 790–797 (2000)

    Article  Google Scholar 

  18. Protopapadakis, E., Doulamis, N.: Image based approaches for tunnels’ defects recognition via robotic inspectors. In: Bebis, George, et al. (eds.) ISVC 2015. LNCS, vol. 9474, pp. 706–716. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27857-5_63

    Chapter  Google Scholar 

  19. Gibb, S., La, H.M.: Automated rebar detection for ground-penetrating radar. In: Bebis, George, et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 815–824. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-50835-1_73

    Chapter  Google Scholar 

  20. Ahmed, H., La, H.M., Gucunski, N.: Rebar detection using Ground Penetrating Radar with state-of-the-art Convolutional Neural Networks. In: Proceedings of 9th SHMII, St. Louis, Missouri, 4–7 August 2019

    Google Scholar 

  21. 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: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA, 14–18 September 2014

    Google Scholar 

  22. 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(2), 367–378 (2014)

    Article  Google Scholar 

  23. La, H.M., Lim, R.S., Basily, B., Gucunski, N., Yi, J., Maher, A., Romero, F.A., Parvardeh, H.: Mechatronic and control systems design for an autonomous robotic system for high-efficiency bridge deck inspection and evaluation. IEEE Trans. Mechatron. 18(6), 1655–1664 (2013)

    Article  Google Scholar 

  24. Dinh, K., Gucunski, N., Duong, T.H.: An algorithm for automatic localization and detection of rebars from GPR data of concrete bridge decks. Autom. Constr. 89, 292–298 (2018)

    Article  Google Scholar 

  25. Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.-H.: Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans. Med. Imaging 36(4), 994–1015 (2017)

    Article  Google Scholar 

  26. Zhang, K., Sun, M., Han, T.X., Yuan, X., Guo, L., Liu, T.: Residual networks of residual networks: multilevel residual networks. IEEE TCSVT 28(6), 1303–1314 (2018)

    Google Scholar 

  27. Kim, J.-H., Lee, J.-S.: Deep residual network with enhanced upscaling module for super-resolution. In: IEEE CVPR, pp. 913–922 (2018)

    Google Scholar 

  28. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual learning for image recognition. In: Proceedings of ICCV, pp. 770–778 (2016)

    Google Scholar 

  29. Chen, C.W., Luo, J., Parker, K.J.: Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications. ITIP 7(12), 1673–1683 (1998)

    Google Scholar 

  30. Gonzales, R.C., Woods, R.E.: Digital Image Processing, 4th edn. Pearson, New York (2017)

    Google Scholar 

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Acknowledgment

Financial support for this INSPIRE UTC project is provided by the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (USDOT/OST-R) under Grant No. 69A3551747126 through INSPIRE University Transportation Center (http://inspire-utc.mst.edu) at Missouri University of Science and Technology. The views, opinions, findings and conclusions reflected in this publication are solely those of the authors and do not represent the official policy or position of the USDOT/OST-R, or any State or other entity.

Author information

Authors and Affiliations

  1. Advanced Robotics and Automation Lab, Department of Computer Science and Engineering, University of Nevada, Reno, NV, 89557, USA

    Habib Ahmed & Hung Manh La

  2. Department of Civil and Environmental Engineering, University of Nevada, Reno, NV, 89557, USA

    Gokhan Pekcan

Authors
  1. Habib Ahmed
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  2. Hung Manh La
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  3. Gokhan Pekcan
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Corresponding author

Correspondence to Hung Manh La .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

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

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Ahmed, H., La, H.M., Pekcan, G. (2019). Rebar Detection and Localization for Non-destructive Infrastructure Evaluation of Bridges Using Deep Residual Networks. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_49

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  • DOI: https://doi.org/10.1007/978-3-030-33720-9_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33719-3

  • Online ISBN: 978-3-030-33720-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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