Rapid Detection Methods for Asphalt Pavement Thicknesses and Defects by a Vehicle-Mounted Ground Penetrating Radar (GPR) System

Dong, Zaili; Ye, Shengbo; Gao, Yanfang; Fang, Guangyou; Zhang, Xiaojuan; Zou, Xue; Zhang, Tao

doi:10.3390/s16122067

Cited by 68 publications

(29 citation statements)

References 34 publications

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“…Being capable of sensing discontinuity of electromagnetic properties in the ground, Ground Penetrating Radar (GPR) has been used as nondestructive remote sensing modality in many applications, including tree root detection [1], frozen layer monitoring [2], structural assessment [3], infrastructure inspection [4], and landmine detection [5][6][7][8][9]. However, GPR detection of Antipersonnel Mines (APMs) is always challenging because of clutter contamination, such as antenna coupling, ground reflection, and friendly objects (rocks, voids, etc.)…”

Section: Introductionmentioning

confidence: 99%

GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis

et al. 2019

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The ground Penetrating Radar (GPR) is a promising remote sensing modality for Antipersonnel Mine (APM) detection. However, detection of the buried APMs are impaired by strong clutter, especially the reflection caused by rough ground surfaces. In this paper, we propose a novel clutter suppression method taking advantage of the low-rank and sparse structure in multidimensional data, based on which an efficient target detection can be accomplished. We firstly created a multidimensional image tensor using sub-band GPR images that are computed from the band-pass filtered GPR signals, such that differences of the target response between sub-bands can be captured. Then, exploiting the low-rank and sparse property of the image tensor, we use the recently proposed Tensor Robust Principal Analysis to remove clutter by decomposing the image tensor into three components: a low-rank component containing clutter, a sparse component capturing target response, and noise. Finally, target detection is accomplished by applying thresholds to the extracted target image. Numerical simulations and experiments with different GPR systems are conducted. The results show that the proposed method effectively improves signal-to-clutter ratio by more than 20 dB and yields satisfactory results with high probability of detection and low false alarm rates.

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Section: Introductionmentioning

confidence: 99%

GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis

et al. 2019

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“…In recent years, ground penetrating radar (GPR), which allows for rapid and real-time measurements, has received great attention for nondestructive testing (NDT). This technique has been applied for pavement investigation [ 4 ], geological exploration [ 5 ], civil engineering [ 6 ], subsurface archaeological investigation [ 7 ] and thin-layer thickness estimation [ 8 ]. In the forestry field, GPR has provided an important method of root system architecture reconstruction [ 9 ], root diameter or biomass estimation [ 10 ], and moisture content [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Ground-Penetrating Radar for Detecting Internal Anomalies in Tree Trunks with Irregular Contours

Wen

Zhang

et al. 2018

Sensors

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To assess the health conditions of tree trunks, it is necessary to estimate the layers and anomalies of their internal structure. The main objective of this paper is to investigate the internal part of tree trunks considering their irregular contour. In this respect, we used ground penetrating radar (GPR) for non-invasive detection of defects and deteriorations in living trees trunks. The Hilbert transform algorithm and the reflection amplitudes were used to estimate the relative dielectric constant. The point cloud data technique was applied as well to extract the irregular contours of trunks. The feasibility and accuracy of the methods were examined through numerical simulations, laboratory and field measurements. The results demonstrated that the applied methodology allowed for accurate characterizations of the internal inhomogeneity. Furthermore, the point cloud technique resolved the trunk well by providing high-precision coordinate information. This study also demonstrated that cross-section tomography provided images with high resolution and accuracy. These integrated techniques thus proved to be promising for observing tree trunks and other cylindrical objects. The applied approaches offer a great promise for future 3D reconstruction of tomographic images with radar wave.

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“…In this case, the problem of their electromagnetic compatibility arises. This also complicates the use of so-called georadar lines for simultaneous scanning of the whole lane on the road [11,12]. Earlier, the authors presented a method of assessment the thickness of the road pavement layers, provided that the upper layer of the pavement has a small thickness (the optical path length in the material) [13].…”

Section: Introductionmentioning

confidence: 99%

Combined GPR data analysis technique for diagnostics of structures with thin near-surface layers

Batrakov¹,

Batrakova²,

Antyufeyeva³

2018

Diagnostyka

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A method of processing GPR signals based on previously developed algorithms is proposed. The purpose of the study is to increase the accuracy of thickness measurements of near-surface thin layers of multilayer structures. The main idea of the method is to layer-by-layer restoration of physical and geometric characteristics of plane-layered media, for example, car coverings. As a result, with the help of numerical modelling, the operability and practical effectiveness of the proposed method was established. In addition, schemes for the practical implementation of this approach have been proposed for the processing of sounding data of pavement structures using a pulsed ground-penetrating radar "TRF-1". The use of additional procedures, for example, GPR calibration based on data obtained by core sampling, also improves the reliability and accuracy of assessing the current state of road pavements.

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Rapid Detection Methods for Asphalt Pavement Thicknesses and Defects by a Vehicle-Mounted Ground Penetrating Radar (GPR) System

Cited by 68 publications

References 34 publications

GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis

GPR Antipersonnel Mine Detection Based on Tensor Robust Principal Analysis

Application of Ground-Penetrating Radar for Detecting Internal Anomalies in Tree Trunks with Irregular Contours

Combined GPR data analysis technique for diagnostics of structures with thin near-surface layers

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