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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera.

Hyuntae Bang1, Jiyoung Min2, Haemin Jeon1

  • 1Department of Civil and Environmental Engineering, Hanbat National University, Dongseodae-ro 125, Daejeon 34158, Korea.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces an automated system for detecting concrete structural damage like cracks and delamination using deep learning and a depth camera. The method achieves high accuracy in identifying and quantifying surface defects, crucial for infrastructure health monitoring.

Keywords:
damage detectiondeep learningdepth cameraquantificationstructured light

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Aging infrastructure necessitates automated structural health monitoring.
  • Surface damage (cracks, delamination, rebar exposure) is key to assessing concrete structure condition.
  • Declining construction workforce drives demand for automated solutions.

Purpose of the Study:

  • To propose a deep learning-based system for detecting and quantifying surface damage on concrete structures.
  • To utilize structured lights and a depth camera for precise damage assessment.
  • To automate the monitoring of structural integrity.

Main Methods:

  • A monitoring system combining four lasers and a depth camera was developed.
  • Image homography was used for calibrating images when the structure and sensor are not parallel.
  • Faster RCNN with Inception Resnet v2 architecture was employed for damage detection (cracks, delamination, rebar exposure).
  • Damage quantification involved analyzing laser beam positions and measured distances.

Main Results:

  • The system successfully detected three types of surface damage.
  • Structural damage was identified with a high F1 score of 0.83.
  • Quantified relative error for damage assessment was below 5% median value.

Conclusions:

  • The proposed deep learning approach effectively automates structural damage monitoring.
  • The system demonstrates high accuracy and reliability in detecting and quantifying concrete surface defects.
  • This technology offers a promising solution for maintaining aging infrastructure.