Automated Rebar Detection for Ground-Penetrating Radar
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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.
KeywordsTraining Image Bridge Deck Histogram Equalization Histogram Localization Adaptive Histogram Equalization
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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