Automated Rebar Detection for Ground-Penetrating Radar
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.
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.
- 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.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.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.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
- 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.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
- 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
- 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
- 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.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
- 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