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Three-Stage Pavement Crack Localization and Segmentation Algorithm Based on Digital Image Processing and Deep

Zhen Yang1, Changshuang Ni1, Lin Li1,2

  • 1College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350108, China.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel three-stage method for accurately locating and segmenting asphalt pavement cracks. The approach enhances image quality and improves crack detection accuracy, offering a new solution for highway inspection.

Keywords:
RetinexYOLOv7asphalt pavement crackattention mechanismdeep learningdigital image processing technologyguided filter

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Materials Science

Background:

  • Asphalt pavement crack detection is crucial for highway maintenance and safety.
  • Existing methods struggle with image quality issues like uneven illumination, noise, and shadows.
  • Accurate crack location and segmentation are essential for efficient inspection.

Purpose of the Study:

  • To propose a robust three-stage method for asphalt pavement crack location and segmentation.
  • To enhance image preprocessing for improved crack visibility.
  • To improve the accuracy and efficiency of crack detection and contour extraction.

Main Methods:

  • Image preprocessing using guided filtering and Retinex methods to enhance brightness and reduce noise.
  • Crack localization using a novel YOLO-SAMT target detection model, outperforming YOLOv7.
  • Crack extraction via an improved k-means clustering algorithm for precise contour identification.

Main Results:

  • Preprocessed images showed a 63% increase in information entropy.
  • YOLO-SAMT model achieved 5.42 percentage points higher mAP@0.5 than YOLOv7.
  • Improved k-means algorithm increased accuracy by 7.34%, true rate by 6.57%, and reduced false positive rate by 18.32%.

Conclusions:

  • The proposed method significantly improves asphalt pavement crack image quality and detection accuracy.
  • The integrated approach enhances crack identification, location, and contour extraction efficiency.
  • This provides a valuable new solution for automated highway crack inspection.