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Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational

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  • 1Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809-03 Quang Trung, Da Nang, Vietnam.

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Summary
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This study introduces an automated method for classifying asphalt pavement cracks using image processing and artificial intelligence. The hybrid DFP-LSSVM model achieved 93.04% accuracy, improving road condition surveys.

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Periodic surveys of asphalt pavement condition are crucial for infrastructure maintenance.
  • Automating pavement crack pattern classification enhances survey efficiency and accuracy.

Purpose of the Study:

  • To develop an intelligent method for automated classification of pavement crack patterns.
  • To improve the efficiency of asphalt pavement condition surveys.

Main Methods:

  • Utilized Laplacian pyramid and projection integral for numerical feature extraction from pavement images.
  • Employed Least Squares Support Vector Machine (LSSVM) for data classification.
  • Integrated Differential Flower Pollination (DFP) to optimize LSSVM regularization and kernel function parameters, creating the DFP-LSSVM hybrid model.

Main Results:

  • Laplacian pyramid effectively enhanced pavement images, revealing crack patterns.
  • The DFP-LSSVM model, combined with Laplacian pyramid at level 4, achieved a highest classification accuracy of 93.04%.
  • The dataset comprised 500 images across five crack categories: alligator, diagonal, longitudinal, no crack, and transverse.

Conclusions:

  • The hybrid DFP-LSSVM approach demonstrates significant promise for automated pavement crack classification.
  • This intelligent method can assist transportation agencies in efficient pavement condition surveying.
  • The developed technique offers a reliable tool for objective assessment of road surface integrity.