Automated Rebar Detection for Ground-Penetrating Radar

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


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.


Training Image Bridge Deck Histogram Equalization Histogram Localization Adaptive Histogram Equalization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University of NevadaRenoUSA

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