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Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image

Fangyan Nie1, Mengzhu Liu2, Pingfeng Zhang3

  • 1Computer and Information Engineering College, Guizhou University of Commerce, Guiyang, 550014, China. niefyan@vip.163.com.

Scientific Reports
|April 1, 2024
PubMed
Summary

This study introduces a novel multilevel thresholding method for crack image segmentation using minimum arithmetic-geometric divergence. The enhanced particle swarm optimization algorithm improves detection accuracy and efficiency in complex engineering structures.

Keywords:
Crack detectionLocal stochastic perturbationMinimum arithmetic-geometric divergencMultilevel image thresholdingParticle swarm optimization

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Area of Science:

  • Engineering
  • Computer Science
  • Image Processing

Background:

  • Crack formation in engineering structures poses significant safety risks.
  • Prompt crack detection is vital for structural integrity.
  • Image threshold segmentation is a key machine vision technique for crack identification.

Purpose of the Study:

  • To propose a novel multilevel thresholding method for crack image segmentation.
  • To enhance crack detection accuracy in complex scenes.
  • To improve the efficiency of multilevel thresholding algorithms.

Main Methods:

  • A multilevel thresholding method based on minimum arithmetic-geometric divergence for crack image segmentation.
  • An enhanced particle swarm optimization algorithm with local stochastic perturbation to address time complexity.
  • Adaptive threshold determination using pixel value distribution characteristics.

Main Results:

  • The proposed method demonstrates superior performance compared to seven state-of-the-art techniques.
  • Quantitative metrics including RMSE, PSNR, SSIM, and FSIM show significant improvements.
  • The enhanced particle swarm optimization algorithm improved diversity and global convergence.

Conclusions:

  • The proposed minimum arithmetic-geometric divergence multilevel thresholding method is effective for crack image segmentation.
  • The enhanced particle swarm optimization algorithm offers improved efficiency and accuracy.
  • This approach provides a robust solution for detecting cracks in complex engineering structures.