Buried pipe localization using an iterative geometric clustering on GPR data

Ground penetrating radar is a non-destructive method to scan the shallow subsurface for detecting buried objects like pipes, cables, ducts and sewers. Such buried objects cause hyperbola shaped reflections in the radargram images achieved by GPR. Originally, those radargram images were interpreted manually by human experts in an expensive and time consuming process. For an acceleration of this process an automatization of the radargram interpretation is desirable. In this paper an efficient approach for hyperbola recognition and pipe localization in radargrams is presented. The core of our approach is an iterative directed shape-based clustering algorithm combined with a sweep line algorithm using geometrical background knowledge. Different to recent state of the art methods, our algorithm is able to ignore background noise and to recognize multiple intersecting or nearby hyperbolas in radargram images without prior knowledge about the number of hyperbolas or buried pipes. The whole approach is able to deliver pipe position estimates with an error of only a few millimeters, as shown in the experiments with two different data sets.