Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment

Shao, Wang; Bouzerdoum, A.; Phung, Son Lam; Su, Lijun; Indraratna, Buddhima; Rujikiatkamjorn, Cholachat

doi:10.1109/tgrs.2011.2128328

Cited by 89 publications

(45 citation statements)

References 21 publications

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“…In Australia another approach was studied for GPR signal processing in frequency domain at network level [16]. The authors present an automatic classification for ballast condition based on the extraction of local maximum points in the magnitude spectra that correspond to the salient frequencies.…”

Section: Gpr Signal Processing In the Spectral Domainmentioning

confidence: 99%

Railway Track Condition Assessment at Network Level by Frequency Domain Analysis of GPR Data

et al. 2018

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Abstract:The railway track system is a crucial infrastructure for the transportation of people and goods in modern societies. With the increase in railway traffic, the availability of the track for monitoring and maintenance purposes is becoming significantly reduced. Therefore, continuous non-destructive monitoring tools for track diagnoses take on even greater importance. In this context, Ground Penetrating Radar (GPR) technique results yield valuable information on track condition, mainly in the identification of the degradation of its physical and mechanical characteristics caused by subsurface malfunctions. Nevertheless, the application of GPR to assess the ballast condition is a challenging task because the material electromagnetic properties are sensitive to both the ballast grading and water content. This work presents a novel approach, fast and practical for surveying and analysing long sections of transport infrastructure, based mainly on expedite frequency domain analysis of the GPR signal. Examples are presented with the identification of track events, ballast interventions and potential locations of malfunctions. The approach, developed to identify changes in the track infrastructure, allows for a user-friendly visualisation of the track condition, even for GPR non-professionals such as railways engineers, and may further be used to correlate with track geometric parameters. It aims to automatically detect sudden variations in the GPR signals, obtained with successive surveys over long stretches of railway lines, thus providing valuable information in asset management activities of infrastructure managers.

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Section: Gpr Signal Processing In the Spectral Domainmentioning

confidence: 99%

Railway Track Condition Assessment at Network Level by Frequency Domain Analysis of GPR Data

et al. 2018

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“…The Wollongong railway data set was collected in our project for railway ballast assessment [41]. The aim of the project was to develop an automatic and non-destructive method using GPR for evaluating the conditions of railway ballast.…”

Section: B Experimental Data Setsmentioning

confidence: 99%

Sparse Representation of GPR Traces With Application to Signal Classification

Shao

Bouzerdoum

Phung

2013

IEEE Trans. Geosci. Remote Sensing

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Sparse representation (SR) models a signal with a small number of elementary waves using an overcomplete dictionary. It has been employed for a wide range of signal and image processing applications, including denoising, deblurring, and compression. In this paper, we present an adaptive SR method for modeling and classifying ground penetrating radar (GPR) signals. The proposed method decomposes each GPR trace into elementary waves using an adaptive Gabor dictionary. The sparse decomposition is used to extract salient features for SR and classification of GPR signals. Experimental results on real-world data show that the proposed sparse decomposition achieves efficient signal representation and yields discriminative features for pattern classification. © 1980-2012 IEEE.

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“…The recognition rate can be improved with various signal processing steps. To improve the quality of images, several researchers have demonstrated various signal processing steps like frequency domain processing, spatial filtering, intensity transformation, image restoration and image segmentation at microwave frequency range for hidden target identification [16][17][18][19][20][21] . However, these techniques need to critically analyse for fully utilisation in MMW images for concealed target identification.…”

Section: Introductionmentioning

confidence: 99%

Development of Scale and Rotation Invariant Neural Network based Technique for Detection of Dielectric Contrast Concealed Targets with Millimeter Wave System

2017

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The detection of concealed targets beneath a person's clothing from standoff distance is an important task for protection and the security of a person in a crowded place like shopping malls, airports and playground stadium, etc. The detection capability of the concealed weapon depends on a lot of factors likes, a collection of back scattered data, dielectric property and a thickness of covering cloths, the hidden object, standoff distance and the probability of false alarm owing to objectionable substances. Though active millimeter wave systems have used to detect weapons under cloths, but still more attention is required to detect the target likes a gun, knife, and matchbox. To observe such problems, active V-band (59 GHz-61 GHz) MMW radar with the help of artificial neural network (ANN) has been demonstrated for non-metallic as well as metallic concealed target detection. To validate ANN, the signature of predefined targets is matched with the signature of validated data with the help of the correlation coefficient. The proposed technique has good capability to distinguish concealed targets under various cloths.

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Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment

Cited by 89 publications

References 21 publications

Railway Track Condition Assessment at Network Level by Frequency Domain Analysis of GPR Data

Railway Track Condition Assessment at Network Level by Frequency Domain Analysis of GPR Data

Sparse Representation of GPR Traces With Application to Signal Classification

Development of Scale and Rotation Invariant Neural Network based Technique for Detection of Dielectric Contrast Concealed Targets with Millimeter Wave System

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