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Hierarchical existential prior based on expanded pseudo-label for crack detection.

Nan Wang1, Jie Fang2, Jianfu Yin3

  • 1School of Information Science and Technology, Hainan Normal University, Haikou 571158, China.

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|December 26, 2024
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Summary
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This study introduces an improved road crack detection method using a hierarchical prior and expanded pseudo-labels. The approach enhances accuracy by progressively refining crack identification, outperforming existing techniques on benchmark datasets.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Image processing for road crack detection is crucial but challenged by complex backgrounds and imbalanced data.
  • Existing methods often struggle to achieve high performance due to these inherent difficulties.

Purpose of the Study:

  • To develop a robust and accurate road crack detection framework.
  • To address the limitations of complex background interference and category imbalance in road images.

Main Methods:

  • A novel hierarchical existential prior based on expanded pseudo-labels is proposed.
  • The framework utilizes three U-Net variants trained hierarchically with decreasing pseudo-label expansion.
  • A hybrid loss function (IoU, SSIM, focal loss) and dynamic hyper-parameter adjustment are employed.

Main Results:

  • The method achieved superior Fβ scores of 11.36% (CrackTree200), 29.76% (Crack Forest), and 26.73% (ALE).
  • The hierarchical approach refines detection results gradually from coarse to fine.
  • The hybrid loss and dynamic adjustment enhance model robustness and structural representation.

Conclusions:

  • The proposed method demonstrates significant effectiveness and superiority in road crack detection.
  • The hierarchical prior and expanded pseudo-label strategy offer a promising direction for improving crack detection accuracy.
  • The technique shows potential for practical applications in road maintenance and inspection.