Trend deviation analysis for automated detection of defects in GPR data for road condition surveys

Alena Uus, Panos Liatsis, Gregory Slabaugh, Athanasios Anagnostis, Sam Roberts, Stephen Twist

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageBritish English
Title of host publicationIWSSIP 2016 - Proceedings of the 23rd International Conference on Systems, Signals and Image Processing
EditorsRenata Rybarova, Gregor Rozinaj, Ivan Minarik, Peter Truchly
PublisherIEEE Computer Society
ISBN (Electronic)9781467395557
DOIs
StatePublished - 30 Jun 2016
Event23rd International Conference on Systems, Signals and Image Processing, IWSSIP 2016 - Bratislava, Slovakia
Duration: 23 May 201625 May 2016

Publication series

NameInternational Conference on Systems, Signals, and Image Processing
Volume2016-June
ISSN (Print)2157-8672
ISSN (Electronic)2157-8702

Conference

Conference23rd International Conference on Systems, Signals and Image Processing, IWSSIP 2016
Country/TerritorySlovakia
CityBratislava
Period23/05/1625/05/16

Keywords

  • Automated defect detection
  • GPR processing
  • Non-destructive testing
  • Road structural condition monitoring

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