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Buried pipe localization using an iterative geometric clustering on GPR data

Janning, Ruth; Busche, Andre; Horváth, Tomáš; Schmidt-Thieme, Lars

Authors

Ruth Janning

Andre Busche

Tomáš Horváth

Lars Schmidt-Thieme



Abstract

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.

Citation

Janning, R., Busche, A., Horváth, T., & Schmidt-Thieme, L. (2014). Buried pipe localization using an iterative geometric clustering on GPR data. Artificial Intelligence Review, 42(3), 403-425. https://doi.org/10.1007/s10462-013-9410-2

Journal Article Type Article
Online Publication Date Jul 16, 2013
Publication Date 2014-10
Deposit Date Mar 27, 2024
Print ISSN 0269-2821
Electronic ISSN 1573-7462
Publisher Springer
Peer Reviewed Peer Reviewed
Volume 42
Issue 3
Pages 403-425
DOI https://doi.org/10.1007/s10462-013-9410-2
Keywords Ground penetrating radar (GPR), Object detection, Hyperbola recognition, Clustering, Sweep line algorithm
Public URL http://researchrepository.napier.ac.uk/Output/3577737