Efficient Detection of Buried Plastic Pipes by Combining GPR and Electric Field Methods

In this paper, an efficient plastic pipe detecting model is proposed, which combines the ground penetrating radar (GPR) and the electric field method. The model consists of the electric field locating model (EFLM) and the GPR B-scan image interpreting (GBII) model. Synchronized electric field and GPR data are collected through a data acquisition device dedicatedly designed for the swift and accurate estimation of buried plastic pipes. The EFLM estimates the approximate locations of underground plastic pipes from the electric field data quickly, separates a GPR B-scan image into segments, keeps the segments that might contain hyperbolas, and discards the irrelevant ones. Then, the GBII model interprets the depth and radius of the buried pipe in the kept segments. Our numerical simulations and experiments prove that by utilizing the EFLM, the 1-D electric field data could be processed quickly and the GPR B-scan image could be segmented with part of irrelevant pixels discarded, while hyperbolas in the kept image segments could be automatically and accurately fitted. With our proposed model, the depth and radius of the buried pipes could be efficiently obtained.

[1]  Farid Melgani,et al.  Automatic Analysis of GPR Images: A Pattern-Recognition Approach , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Paolo Gamba,et al.  A fuzzy shell clustering approach to recognize hyperbolic signatures in subsurface radar images , 2000, IEEE Trans. Geosci. Remote. Sens..

[4]  F. Francisca,et al.  Complex Dielectric Permittivity of Soil–Organic Mixtures (20 MHz–1.3 GHz) , 2003 .

[5]  S. Shihab,et al.  Radius Estimation for Cylindrical Objects Detected by Ground Penetrating Radar , 2005 .

[6]  P. Hedvig Electrical properties of polymers , 1977, Digest of Literature on Dielectrics Volume 42 1978.

[7]  Claudio Bruschini,et al.  Ground penetrating radar and imaging metal detector for antipersonnel mine detection , 1998 .

[8]  F. Bookstein Fitting conic sections to scattered data , 1979 .

[9]  Chi-Chih Chen,et al.  Automatic GPR target detection and clutter reduction using neural network , 2002, International Conference on Ground Penetrating Radar.

[10]  Salvatore Caorsi,et al.  An electromagnetic approach based on neural networks for the GPR investigation of buried cylinders , 2005, IEEE Geoscience and Remote Sensing Letters.

[11]  Huanhuan Chen,et al.  An Automatic GPR B-Scan Image Interpreting Model , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[12]  N. E. Hill,et al.  Dielectric properties and molecular behaviour , 1969 .

[13]  Jerzy Krupka,et al.  Complex permittivity measurements of common plastics over variable temperatures , 2003 .

[14]  Antonios Giannopoulos,et al.  Modelling ground penetrating radar by GprMax , 2005 .

[15]  P. Falorni,et al.  ESTIMATION OF RELATIVE PERMITTIVITY OF SHALLOW SOILS BY USING THE GROUND PENETRATING RADAR RESPONSE FROM DIFFERENT BURIED TARGETS , 2008 .

[16]  L. S. Edwards,et al.  A modified pseudosection for resistivity and IP , 1977 .

[17]  Lorenzo Capineri,et al.  Advanced image‐processing technique for real‐time interpretation of ground‐penetrating radar images , 1998 .

[18]  George A. McMechan,et al.  GPR characterization of buried tanks and pipes , 1997 .

[19]  Robert D. Grisso,et al.  Precision Farming Tools. Soil Electrical Conductivity , 2005 .

[20]  C. Rogers,et al.  CAPACITIVE-COUPLED ELECTRIC-FIELD SENSING FOR URBAN SUB-SURFACE MAPPING: MOTIVATIONS AND PRACTICAL CHALLENGES , 2010 .

[21]  J. E. Hipp Soil electromagnetic parameters as functions of frequency, soil density, and soil moisture , 1974 .

[22]  Huanhuan Chen,et al.  Probabilistic robust hyperbola mixture model for interpreting ground penetrating radar data , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[23]  Theoretical and experimental examinations of the capacitively coupled resistivity (line antenna) method , 2013 .

[24]  Josef Kittler,et al.  A survey of the hough transform , 1988, Comput. Vis. Graph. Image Process..

[25]  L. Bohm,et al.  High-density polyethylene pipe resins† , 1992 .

[26]  Paolo Gamba,et al.  Neural detection of pipe signatures in ground penetrating radar images , 2000, IEEE Trans. Geosci. Remote. Sens..

[27]  Oliver Kuras,et al.  The Capacitive Resistivity Technique for Electrical Imaging of the Shallow Subsurface , 2002 .

[28]  Anthony G Cohn,et al.  Probabilistic Conic Mixture Model and its Applications to Mining Spatial Ground Penetrating Radar Data , 2010 .

[29]  Z. Chik,et al.  Improved near surface soil characterizations using a multilayer soil resistivity model , 2013 .

[30]  P. Murugavel,et al.  Effect of relative humidity and sea level pressure on electrical conductivity of air over Indian Ocean , 2009 .

[31]  Wenxing Zhou,et al.  Statistical analyses of incidents on onshore gas transmission pipelines based on PHMSA database , 2016 .

[32]  Anthony G. Cohn,et al.  Real-Time Hyperbola Recognition and Fitting in GPR Data , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Serena Matucci,et al.  The Detection of Buried Pipes From Time-of-Flight Radar Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[34]  Emiliano Rustighi,et al.  3D Buried Utility Location Using A Marching-Cross-Section Algorithm for Multi-Sensor Data Fusion , 2016, Sensors.

[35]  Zhiyu Wang,et al.  Investigation on a Novel Capacitive Electrode for Geophysical Surveys , 2016, J. Sensors.

[36]  Colin G. Windsor,et al.  A Data Pair-Labeled Generalized Hough Transform for Radar Location of Buried Objects , 2014, IEEE Geoscience and Remote Sensing Letters.

[37]  Jörg Schmalzl,et al.  Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar , 2013, Comput. Geosci..

[38]  Huanhuan Chen,et al.  Buried Utility Pipeline Mapping based on Street Survey and Ground Penetrating Radar , 2010, ECAI.

[39]  A. LeBlanc,et al.  Capacitive resistivity inversion using effective dipole lengths for line antennas , 2013 .

[40]  Kazushi Nakano,et al.  Dirichlet process crescent-signal mixture model for ground-penetrating radar signals , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[41]  Mansor Nakhkash,et al.  Automatic detection of buried utilities and solid objects with GPR using neural networks and pattern recognition , 2000 .

[42]  Hiroshi Akima,et al.  A Method of Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points , 1978, TOMS.

[43]  John Porrill Fitting ellipses and predicting confidence envelopes using a bias corrected Kalman filter , 1990, Image Vis. Comput..