Automated Rebar Detection for Ground-Penetrating Radar

Automated Rebar Detection for Ground-Penetrating Radar
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DOI：
10.1007/978-3-319-50835-1_73
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发表时间：
2016-12
期刊：
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影响因子：
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通讯作者：
Spencer Gibb;H. La
Spencer Gibb;H. La
中科院分区：
其他
文献类型：
--
作者：
Spencer Gibb;H. La

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自动钢筋检测的图像从探地雷达（GPR）是一个具有挑战性的问题，并难以实时执行，由于相对较低的对比度图像和图像的大小。本文提出了一种钢筋定位算法，该算法可以准确地定位雷达扫描图像中钢筋的像素位置。该算法使用图像分类和统计方法来定位图像中的双曲线签名。所提出的方法利用自适应直方图均衡化来增加图像中钢筋的视觉特征，尽管对比度较低。一个朴素贝叶斯分类器被用来近似定位图像中的钢筋与直方图的方向梯度特征向量。此外，基于直方图的方法被应用到更精确地定位图像中的单个钢筋，然后使用现有的GPR数据和本文研究过程中收集的数据对所提出的方法进行验证。
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.