Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring

Alvarez, JK; Kodagoda, S

Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring

Alvarez, JK Kodagoda, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018, 2018, pp. 611 - 616
Issue Date:: 2018-06-26

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Field	Value	Language
dc.contributor.author	Alvarez, JK	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.date.issued	2018-06-26	en_US
dc.identifier.citation	Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018, 2018, pp. 611 - 616	en_US
dc.identifier.isbn	9781538637579	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132292
dc.description.abstract	© 2018 IEEE. The corrosion of reinforced concrete sewer pipes in aging infrastructure is a serious ongoing issue and as such, research into technologies that allow for autonomous site assessments are of major priority to wastewater managing utilities. The use of Ground Penetrating Radar (GPR) is being investigated for providing sub-surface images of sewer crowns. Due to the nature of GPRs, the analysis of a radargram for identifying sub-surface features is non-intuitive and usually require the use of an expert. Traditional methods to help ease analysis involve the use of Synthetic Aperture Radar (SAR) and migration techniques. These techniques refocus dipping and point reflectors to be closer to their true shape but require an accurate velocity model to be effective. This is not always readily available and difficult to estimate especially in regards to sewer conditions. We instead provide an alternative and present a deep learning framework for transforming ground penetrating radargrams into sub-surface permittivity maps. An evaluation of various state-of-the-art deep learning architectures is also conducted, comparing the performance of different objective functions and identifying current limitations. This work provides the base for further exploration of the application of deep learning for use in infrastructure monitoring.	en_US
dc.relation.ispartof	Proceedings of the 13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018	en_US
dc.relation.isbasedon	10.1109/ICIEA.2018.8397788	en_US
dc.title	Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. The corrosion of reinforced concrete sewer pipes in aging infrastructure is a serious ongoing issue and as such, research into technologies that allow for autonomous site assessments are of major priority to wastewater managing utilities. The use of Ground Penetrating Radar (GPR) is being investigated for providing sub-surface images of sewer crowns. Due to the nature of GPRs, the analysis of a radargram for identifying sub-surface features is non-intuitive and usually require the use of an expert. Traditional methods to help ease analysis involve the use of Synthetic Aperture Radar (SAR) and migration techniques. These techniques refocus dipping and point reflectors to be closer to their true shape but require an accurate velocity model to be effective. This is not always readily available and difficult to estimate especially in regards to sewer conditions. We instead provide an alternative and present a deep learning framework for transforming ground penetrating radargrams into sub-surface permittivity maps. An evaluation of various state-of-the-art deep learning architectures is also conducted, comparing the performance of different objective functions and identifying current limitations. This work provides the base for further exploration of the application of deep learning for use in infrastructure monitoring.

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http://hdl.handle.net/10453/132292