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Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach.

Eldor Ibragimov1, Yongsoo Kim1, Jung Hee Lee2

  • 1SISTech Co., Ltd., Seoul 05006, Republic of Korea.

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|April 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for calculating the pavement condition index (PCI) using deep learning and image processing. The novel approach accurately detects cracks and measures their width, improving road maintenance efficiency and reliability.

Keywords:
crack detectioncrack segmentationscrack width estimationdeep learningimage processingpavement condition indexpavementsskeleton algorithm

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Infrastructure Management

Background:

  • Road pavement degradation from environmental factors is a significant infrastructure challenge.
  • Manual pavement condition index (PCI) assessment is labor-intensive, subjective, and prone to errors.
  • Accurate PCI evaluation is crucial for infrastructure maintenance, budget allocation, and performance tracking.

Purpose of the Study:

  • To develop and validate an automated, end-to-end method for pavement condition index (PCI) calculation.
  • To overcome the limitations of traditional manual PCI assessment methods.
  • To enhance the efficiency and reliability of pavement distress identification and evaluation.

Main Methods:

  • Integration of deep learning algorithms for automated pavement crack detection.
  • Application of image processing, specifically a segmentation-based skeleton algorithm, for precise crack width estimation.
  • Development of an automated system for PCI rating aligned with established standards.

Main Results:

  • Achieved 95% accuracy in pavement crack detection using deep learning.
  • Demonstrated 90% accuracy in estimating crack width via image processing techniques.
  • Successfully implemented an automated PCI rating system showing significant improvements in efficiency and reliability.

Conclusions:

  • The novel automated method significantly enhances the accuracy and efficiency of pavement condition index (PCI) evaluations.
  • This approach offers a reliable alternative to manual PCI assessment, reducing subjectivity and human error.
  • The developed technology presents advancements for pavement maintenance strategies and broader road infrastructure management.