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Intelligent Crack Detection Method Based on GM-ResNet.

Xinran Li1, Xiangyang Xu1, Xuhui He2

  • 1School of Rail Transportation, Soochow University, Suzhou 215006, China.

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This study introduces GM-ResNet for accurate road crack detection, improving road safety. The method enhances feature extraction and addresses data imbalance for superior performance.

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Area of Science:

  • Civil Engineering
  • Computer Science

Background:

  • Road safety, structural stability, and durability are critical.
  • Effective road crack detection is essential for infrastructure maintenance.

Purpose of the Study:

  • To propose an enhanced deep learning method (GM-ResNet) for precise road crack detection.
  • To improve feature extraction and address class imbalance in crack datasets.

Main Methods:

  • Utilized ResNet-34 for feature extraction, incorporating a global attention mechanism.
  • Replaced the fully connected layer with a multilayer network for complex data relationships.
  • Implemented focal loss to mitigate class imbalance issues.

Main Results:

  • GM-ResNet demonstrated superior crack detection accuracy compared to existing methods.
  • The method showed improved evaluation indicators in detection results.
  • Enhanced feature representation and generalization led to more precise outcomes.

Conclusions:

  • The proposed GM-ResNet effectively enhances road crack detection precision and efficacy.
  • The integration of global attention and focal loss significantly improves model performance.
  • This approach validates the potency of the method for optimal crack detection.