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Trend Deviation Analysis for Automated Detection of Defects in GPR Data for Road Condition Surveys

Uus, A., Liatsis, P., Slabaugh, G. G. , Anagnostis, A., Roberts, S. & Twist, S. (2016). Trend Deviation Analysis for Automated Detection of Defects in GPR Data for Road Condition Surveys. Proceedings of the 23rd International Conference on Systems, Signals and Image Processing (IWSSIP 2016), doi: 10.1109/IWSSIP.2016.7502765


This paper presents a novel approach for automated detection of defects and structural changes in GPR data acquired in HMA (Hot Mix Asphalt) road surveys. Unlike the majority of existing approaches for road GPR data processing that are mainly used for extraction of layer profile information, the proposed method focuses on automated identification of significant deviations in subsurface structure and material properties. It is based on the detection of variations in intensity trends of longitudinal lines of interpolated B-scans that are characterized by deviations above a defined threshold. The outputs include mapped defects and deterioration areas together with the locations of detected changes in road structure design.

Publication Type: Article
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Publisher Keywords: Road structural condition monitoring; Nondestructive testing; GPR processing; Automated defect detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Science & Technology > Computer Science
School of Science & Technology > Engineering
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