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April 10, 2026MathematicsOpen Access

Bimodal Image Fusion and Brightness Piecewise Linear Enhancement for Crack Segmentation

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Authors

YLYong LiNJNian JiFZFuzhe Zhao

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Overview

Algorithm improves crack segmentation accuracy in structures, suggesting better monitoring of safety and damage assessment.

Key Points

  • This research aims to improve the segmentation of structural cracks using a novel algorithm.
  • Developed a crack-segmentation algorithm combining bimodal image fusion and brightness piecewise linear enhancement.
  • Utilized visible-light and pseudo-color images for detailed feature enhancement.
  • Employed bottom-hat transform and OTSU algorithm for effective crack region segmentation.
  • Conducted parameter extraction for crack monitoring and safety assessment.
  • Achieved a Dice coefficient of 0.4511 and a Jaccard index of 0.2981 for segmentation performance.
  • Showed improvements of 26.9% and 34.5% over the second-best algorithm.
  • Demonstrated superior computational efficiency and robustness for real-world engineering applications.

Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69d896566c1944d70ce07b45https://doi.org/10.3390/math14071235
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Also Consider

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  1. 1Concrete Crack Detection in Extremely Dark Environments Based on Infrared-Visible Multi-Level Registration Fusion and Frequency Decoupling2026
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  3. 3Pixel-level crack segmentation and quantification enabled by multi-modality cross-fusion of RGB and depth images2025 · 23 citations
  4. 4Crack Segmentation Model for Low-Quality Crack Images Based on Feature Integration and Triple Attention2026 · 1 citations
  5. 5Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation2024 · 11 citations