Automatic Recognition and Segmentation of Overlapped GPR Target Signatures

Ren, Q; Wang, Y; Ha, QP

Automatic Recognition and Segmentation of Overlapped GPR Target Signatures

Ren, Q Wang, Y Ha, QP

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Publisher:: EDP Sciences
Publication Type:: Journal Article
Citation:: E3S Web of Conferences, 2025, 626, pp. 03003
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Ren, Q
dc.contributor.author	Wang, Y
dc.contributor.author	Ha, QP
dc.contributor.editor	Truong, N-V
dc.contributor.editor	Ha, QP
dc.date.accessioned	2025-05-06T10:27:07Z
dc.date.available	2025-05-06T10:27:07Z
dc.date.issued	2025
dc.identifier.citation	E3S Web of Conferences, 2025, 626, pp. 03003
dc.identifier.issn	2555-0403
dc.identifier.issn	2267-1242
dc.identifier.uri	http://hdl.handle.net/10453/187212
dc.description.abstract	<jats:p>Ground penetrating radar (GPR) has been widely utilized for non-destructive inspection of civil infrastructure systems such as bridges and tunnels. However, the identification of GPR signatures poses significant challenges due to the overlapped multiple objects. To overcome the obstacle, we proposed an innovative Mask R-CNN based network considering spatial relationship between GPR signatures. Firstly, to capture the spatial relationship of overlapping signatures, we introduced an improved intersection over union considering central distance and aspect ratio between GPR signatures. Secondly, we further modified the Non-Maximum Suppression and enhanced the corresponding anchor generative mechanism. To validate the proposed method, we conducted testing on GPR scans obtained from real data from a bridge. The results demonstrate that the proposed method not only accurately detects GPR signatures, but also significantly outperforms existing Mask R-CNN in terms of segmenting overlapped GPR signature. Specially, the proposed method achieved an average accuracy of 46.8% in the segmentation task, marking a substantial advancement in the field.</jats:p>
dc.language	en
dc.publisher	EDP Sciences
dc.relation.ispartof	E3S Web of Conferences
dc.relation.isbasedon	10.1051/e3sconf/202562603003
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Automatic Recognition and Segmentation of Overlapped GPR Target Signatures
dc.type	Journal Article
utslib.citation.volume	626
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Built Infrastructure Resilience (CBIR)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-05-06T10:27:05Z
pubs.publication-status	Published
pubs.volume	626

Abstract:

Ground penetrating radar (GPR) has been widely utilized for non-destructive inspection of civil infrastructure systems such as bridges and tunnels. However, the identification of GPR signatures poses significant challenges due to the overlapped multiple objects. To overcome the obstacle, we proposed an innovative Mask R-CNN based network considering spatial relationship between GPR signatures. Firstly, to capture the spatial relationship of overlapping signatures, we introduced an improved intersection over union considering central distance and aspect ratio between GPR signatures. Secondly, we further modified the Non-Maximum Suppression and enhanced the corresponding anchor generative mechanism. To validate the proposed method, we conducted testing on GPR scans obtained from real data from a bridge. The results demonstrate that the proposed method not only accurately detects GPR signatures, but also significantly outperforms existing Mask R-CNN in terms of segmenting overlapped GPR signature. Specially, the proposed method achieved an average accuracy of 46.8% in the segmentation task, marking a substantial advancement in the field.

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http://hdl.handle.net/10453/187212