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Rebar Detection and Localization for Non-destructive Infrastructure Evaluation of Bridges Using Deep Residual Networks

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11844)

Abstract

Nondestructive Evaluation (NDE) of civil infrastructure has been an active area of research for the past few decades. Traditional inspection of civil infrastructure, mostly relying on visual inspection is time-consuming, labor-intensive and often provides subjective and erroneous results. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated manner. The purpose of this research is to provide a novel Deep Learning-based method for detection of steel rebars in reinforced concrete bridge elements using data from Ground Penetrating Radar (GPR). At the same time, a novel technique is proposed for the localization of rebar in B-scan images. In order to examine the performance of the rebar detection and localization system, results are outlined to demonstrate the feasibility of the proposed system within relevant practical applications.

Keywords

Structural Health Monitoring (SHM) Non-Destructive Evaluation (NDE) Ground Penetrating Radar (GPR) sensor Convolutional Neural Networks (CNNs) Deep Residual Networks (ResNets) 

Notes

Acknowledgment

Financial support for this INSPIRE UTC project is provided by the U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology (USDOT/OST-R) under Grant No. 69A3551747126 through INSPIRE University Transportation Center (http://inspire-utc.mst.edu) at Missouri University of Science and Technology. The views, opinions, findings and conclusions reflected in this publication are solely those of the authors and do not represent the official policy or position of the USDOT/OST-R, or any State or other entity.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Advanced Robotics and Automation Lab, Department of Computer Science and EngineeringUniversity of NevadaRenoUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of NevadaRenoUSA

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