Image and Signal Processing Methods

Ground-penetrating radar railroad ballast inspection with an unsupervised algorithm to boost the region of interest detection efficiency

[+] Author Affiliations
Yu Zhang, Anbu Selvam Venkatachalam, Tian Xia

University of Vermont, School of Engineering, 33 Colchester Avenue, Burlington, Vermont 05405, United States

J. Appl. Remote Sens. 9(1), 095058 (Jul 24, 2015). doi:10.1117/1.JRS.9.095058
History: Received March 16, 2015; Accepted June 22, 2015
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Abstract.  Railroad ballast inspection is critical for the safety of both passenger and freight rail. Ground-penetrating radar (GPR) has been utilized as a highly efficient nondestructive evaluation and structural health monitoring technique in bridge and roadway inspection for many years. However, the development of robust GPR technologies for railroad ballast inspection is still at its early stage due to the complex scattering characteristics of ballast and the lack of efficient algorithms to process big GPR data. An efficient unsupervised method for detecting the region of interest in ballast layer based on the Hilbert transform and Renyi entropy analysis is developed. Both laboratory test and field test are set up and conducted. The data interpretation results demonstrate that the developed region of interest detection algorithm is an efficient and valuable tool for GPR data processing.

© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Yu Zhang ; Anbu Selvam Venkatachalam and Tian Xia
"Ground-penetrating radar railroad ballast inspection with an unsupervised algorithm to boost the region of interest detection efficiency", J. Appl. Remote Sens. 9(1), 095058 (Jul 24, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095058


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Automated GPR Rebar Analysis for Robotic Bridge Deck Evaluation. IEEE Trans Cybern Published online Oct 26, 2015;
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