Verification of algorithm for point extraction from hyperbolic reflections in GPR data

Ristić, Aleksandar; Bugarinović, Željko; Govedarica, Miro; Pajewski, Lara; Dérobert, Xavier

doi:10.1109/iwagpr.2017.7996109

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…It is well known that GPR has been extensively used for non-destructive testing problems [17], [18], [19] with mainstream civil engineering applications that include: mapping of utilities [20], [21], bridge evaluation [22], [23], road inspection [24], [25], [26], pipe location, [27], [28] and crack detection [29], [30]. The mapping of reinforcement in concrete using GPR [31] is particularly attractive due to the high contrast in dielectric properties between concrete and steel, combined with the fact that concrete is a relatively homogeneous medium which further increases the overall signal-to-clutter ratio.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

Giannakis

Giannopoulos

Warren

2019

IEEE Trans. Geosci. Remote Sensing

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The simulation, or forward modeling, of Ground Penetrating Radar (GPR) is becoming a more frequently used approach to facilitate interpretation of complex real GPR data, and as an essential component of full-waveform inversion (FWI). However, general full-wave 3D electromagnetic (EM) solvers, such as ones based on the Finite-Difference Time-Domain (FDTD) method, are still computationally demanding for simulating realistic GPR problems. We have developed a novel near realtime, forward modeling approach for GPR that is based on a machine learning (ML) architecture. The ML framework uses an innovative training method which combines a predictive principal component analysis technique, a detailed model of the GPR transducer, and a large dataset of modeled GPR responses from our FDTD simulation software. The ML-based forward solver is parameterized for a specific GPR application, but the framework can be applied to many different classes of GPR problems. To demonstrate the novelty and computational efficiency of our ML-based GPR forward solver, we used it to carry out FWI for a common infrastructure assessment application-determining the location and diameter of reinforcement bars in concrete. We tested our FWI with synthetic and real data, and found a good level of accuracy in determining the rebar location, size, and surrounding material properties from both datasets. The combination of the near real-time computation, which is orders of magnitude less than what is achievable by traditional full-wave 3D EM solvers, and the accuracy of our ML-based forward model is a significant step towards commercially-viable applications of FWI of GPR.

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Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

Giannakis

Giannopoulos

Warren

2019

IEEE Trans. Geosci. Remote Sensing

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“…APEX (automated point extraction from radargrams) was recently developed with the primary objective of automatically detecting hyperbolic signatures in radargrams [14,[31][32][33].…”

Section: Automated Point Extraction From Radargrams (Apex) Algorithmmentioning

confidence: 99%

“…The next 2 × 2 submatrix will have the found prong pixel in the position previously occupied by the apex pixel, and the process will continue. More details regarding the APEX algorithm are in [13,30,31]. Finding prong pixels is important to estimate the size of a target, and to detect interfered hyperbolas; these tasks are not in the focus of this paper and are therefore not further described.…”

Section: Automated Point Extraction From Radargrams (Apex) Algorithmmentioning

confidence: 99%

“…In previous papers [31][32][33][34], APEX was successfully tested on synthetic and experimental radargrams obtained by using antennas with 200-MHz, 400-MHz and 900-MHz central frequencies. The performed tests showed that the algorithm works very well at different frequencies and also that, with minor modifications, it can be applied to radargrams containing reflections from non-cylindrical objects, such as a concrete channel; furthermore, the algorithm is time efficient and worth developing further for real-time applications.…”

Section: Automated Point Extraction From Radargrams (Apex) Algorithmmentioning

confidence: 99%

“…The recently-proposed algorithm APEX (automated point extraction from radargrams) [14] is capable of finding the coordinates of hyperbola apices and extract the points on hyperbola prongs; it is based on the use of a cascade object detector (COD) to identify segments of interest that contain hyperbolic reflections and on a simple but smart procedure for the local search of apices. APEX has been successfully tested on synthetic data and experimental data recorded in complex urban environments during real GPR utility surveys [31][32][33]. A comparative analysis of APEX and SPOT-GPR is presented in [34].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the Introduction of Canny Operator in an Advanced Imaging Algorithm for Real-Time Detection of Hyperbolas in Ground-Penetrating Radar Data

et al. 2020

Self Cite

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This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB.

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Ground Penetrating Radar Data Acquisition to Detect Imbalances and Underground Pipes

Bachiri

Alsharahi

Khamlichi

et al. 2021

Lecture Notes in Electrical Engineering

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Past research shows that the Ground Penetrating Radar (GPR) can be an effective and efficient way to map buried pipeline systems. This paper presents a GPR data analysis technique, which is first generated by applying the Finite Differences Time Domain (FDTD) method to estimate the thickness of the subsurface layers and characterize the piping systems buried in the underground. In practical investigations, the GPR unit with a 400 MHz antenna was used to detect imbalances and underground pipes. The GPR profiles provided details on the shapes and nature of the target in the underground. These profiles can, therefore, detect water pipes, utility systems up to a depth than 2 m.

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Verification of algorithm for point extraction from hyperbolic reflections in GPR data

Cited by 9 publications

References 10 publications

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion

On the Introduction of Canny Operator in an Advanced Imaging Algorithm for Real-Time Detection of Hyperbolas in Ground-Penetrating Radar Data

Ground Penetrating Radar Data Acquisition to Detect Imbalances and Underground Pipes

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