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Dynamic Partition Gaussian Crack Detection Algorithm Based on Projection Curve Distribution.

Dan Xue1, Weiqi Yuan1

  • 1Computer Vision Group, School of Electronic and Information Engineering, Key Laboratory of Machine Vision, Shenyang University of Technology, Shenyang 110870, China.

Sensors (Basel, Switzerland)
|July 26, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic partitioned Gaussian (DPG) algorithm for effective tunnel crack detection, overcoming uneven illumination challenges. The DPG model significantly improves crack detection recall and precision, ensuring structural integrity monitoring.

Keywords:
dynamic partitioned Gaussiangray projection curve distributiontunnel crack detectionuneven illumination

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Image Processing

Background:

  • Tunnel lining crack detection is challenging due to uneven illumination, varying crack widths, and contrast differences.
  • Existing methods struggle with image variations, impacting accurate crack identification and extraction.
  • Uneven illumination significantly complicates distinguishing crack pixels from background noise.

Purpose of the Study:

  • To propose a novel dynamic partitioned Gaussian (DPG) crack detection algorithm.
  • To address the difficulties in detecting and extracting tunnel lining cracks caused by uneven illumination.
  • To enhance the accuracy and reliability of automated crack detection in tunnel infrastructure.

Main Methods:

  • Utilizing image projection curve distribution for dynamic background pixel partitioning.
  • Developing a new dynamic partitioned Gaussian (DPG) model with defined partition boundary conditions and thresholds.
  • Integrating multi-scale Gaussian factors and crack morphology with a breakpoint connection algorithm for extraction.

Main Results:

  • The DPG algorithm effectively mitigates the impact of uneven illumination on crack detection.
  • Achieved a recall rate exceeding 96% for crack detection.
  • Increased precision by over 70% after crack extraction, validated on real-world tunnel data.

Conclusions:

  • The proposed DPG algorithm offers a robust solution for tunnel lining crack detection under challenging lighting conditions.
  • The method demonstrates significant improvements in both detection recall and extraction precision.
  • This technique enhances the reliability of automated structural health monitoring for tunnels.