Peak scatter-based buried object identification using GPR-EMI dual sensor system

Yoldemir, Burak; Sezgin, Mehmet

doi:10.1080/10589759.2019.1623213

Cited by 4 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 12 provides examples of specific scenarios and corresponding object parameter prediction for the proposed approach and the benchmark methods. Two of the benchmark cases include different data sets generated using PCA 26,47,48 . The first one consists of features extracted using PCA similar as in study 26 for the purpose of dimensionality reduction of the B-scan.…”

Section: Design Of Experimentsmentioning

confidence: 99%

“…Two of the benchmark cases include different data sets generated using PCA 26,47,48 . The first one consists of features extracted using PCA similar as in study 26 for the purpose of dimensionality reduction of the B-scan. It www.nature.com/scientificreports/ is mentioned that B-scan data has the size of 319 × 30 (319 time steps and 30 A-scans).…”

Section: Design Of Experimentsmentioning

confidence: 99%

“…A removal of subsurface reflection from the extracted hyperbola in the B-scan images (in the form of the amplitude and time vector) has been used to create inputs in a cascaded NN for characterization of the buried object 12 with the help of the Hilbert transform (HT) to obtain enveloped signals. Another approach has been proposed for identification of a buried object and to obtain the reflected A-scans by using combined MD (Metal Detector) and GPR sensor 26 . The response features extracted from the peaks and their locations within the subsequent 1D time-varying amplitude signals by means of principle component analysis (PCA), are employed to classify the material type into three groups by using k-nearest neighbor supervised learning classification algorithm 26 .…”

Section: Introductionmentioning

confidence: 99%

“…Another approach has been proposed for identification of a buried object and to obtain the reflected A-scans by using combined MD (Metal Detector) and GPR sensor 26 . The response features extracted from the peaks and their locations within the subsequent 1D time-varying amplitude signals by means of principle component analysis (PCA), are employed to classify the material type into three groups by using k-nearest neighbor supervised learning classification algorithm 26 . Other Artificial Intelligence (AI) algorithms have been successfully used for buried target recognition in GPR images include Deep Learning (DL), especially Convolutional Neural Network (CNN) frameworks 14 – 16 , 18 , 19 , 21 .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Yurt

Torpi

Kızılay

et al. 2023

Sci Rep

View full text Add to dashboard Cite

This work addresses artificial-intelligence-based buried object characterization using FDTD-based electromagnetic simulation toolbox of a Ground Penetrating Radar (GPR) to generate B-scan data. In data collection, FDTD-based simulation tool, gprMax is used. The task is to estimate geophysical parameters of a cylindrical shape object of various radii, buried at different positions in the dry soil medium simultaneously and independently of each other. The proposed methodology capitalizes on a fast and accurate data-driven surrogate model developed for object characterization in terms of its vertical and lateral position, and the size. The surrogate is constructed in a computationally efficient manner as compared to methodologies using 2D B-scan image. This is achieved by operating at the level of hyperbolic signatures extracted from the B-scan data through linear regression, which effectively reduces the dimensionality and the size of data. The proposed methodology relies on reducing of 2D B-scan image to 1D data including variation of reflected electric fields’ amplitudes with respect to the scanning aperture. The input of the surrogate model is the extracted hyperbolic signature obtained through linear regression executed on the background subtracted B-scan profiles. The hyperbolic signatures encode information about the geophysical parameters of the buried object, including depth, lateral position, and radius, all of which can be extracted using proposed methodology. Parametric estimation of the object radius and the estimation of the location parameters simultaneously is a challenging problem. Applying the application of processing steps on B-scan profiles incurs high computational costs, which is a limitation of the current methodologies. The metamodel itself is rendered using a novel deep-learning-based modified multilayer perceptron (M2LP) framework. The presented object characterization technique is favourably benchmarked against the state-of-the-art regression techniques, including Multilayer Perceptron (MLP), Support Vector Regression Machine (SVRM), and Convolutional Neural Network (CNN). The verification results demonstrate the average mean absolute error of 10 mm, and the average relative error of 8 percent, both corroborating the relevance of the proposed M2LP framework. In addition, the presented methodology provides a well-structured relation between the geophysical parameters of object and the extracted hyperbolic signatures. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. The environmental and internal noise of the GPR system and their effect is analyzed as well. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources.

show abstract

Section: Design Of Experimentsmentioning

confidence: 99%

Section: Design Of Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Yurt

Torpi

Kızılay

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Other clutter reduction (pre-processing) methods include principal component analysis (PCA) 15 , 27 – 29 , morphological component analysis (MCA) 30 , singular value decomposition (SVD) 15 , 29 , independent component analysis (ICA) 15 , 29 , as well as ICA with multifractal spectrum 31 . In addition to clutter removing, another pre-processing application of PCA is feature extraction, which aims at dimensionality reduction of the B-scan image (2-D data) 32 , 33 . The latter is beneficial from the point of view of representing data using surrogate modeling methods.…”

Section: Introductionmentioning

confidence: 99%

Variable data structures and customized deep learning surrogates for computationally efficient and reliable characterization of buried objects

Yurt,

Torpi,

Kizilay

et al. 2024

Sci Rep

View full text Add to dashboard Cite

In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic parameters of a buried object of diverse radii allocated at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable data structures (raw B-scans, extracted features, consecutive A-scans) with respect to computational cost and accuracy of surrogates. The usage of raw B-scan data and the applications for processing steps on B-scan profiles in the context of object characterization incur high computational cost so it can be a challenging issue. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for time frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the main aim being computationally efficient (about 13 times acceleration) compared to conventional network models using B-scan images (2D data). DRN with TFS is favorably benchmarked to the state-of-the-art regression techniques. The experimental results obtained for the proposed model and second-best model, CNN-1D show mean absolute and relative error rates of 3.6 mm, 11.8 mm and 4.7%, 11.6% respectively. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources.

show abstract

Improved Mask R-CNN with distance guided intersection over union for GPR signature detection and segmentation

Hou

Lei

et al. 2021

Automation in Construction

View full text Add to dashboard Cite

Peak scatter-based buried object identification using GPR-EMI dual sensor system

Cited by 4 publications

References 10 publications

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Buried object characterization by data-driven surrogates and regression-enabled hyperbolic signature extraction

Variable data structures and customized deep learning surrogates for computationally efficient and reliable characterization of buried objects

Improved Mask R-CNN with distance guided intersection over union for GPR signature detection and segmentation

Contact Info

Product

Resources

About