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Lightweight defect detection algorithm of tunnel lining based on knowledge distillation.

Anfu Zhu1, Jiaxiao Xie1, Bin Wang1

  • 1North China University of Water Resources and Electric Power, Zhengzhou, China.

Scientific Reports
|November 7, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight algorithm for detecting tunnel lining defects using knowledge distillation. The improved model significantly reduces size while enhancing accuracy for real-time defect identification.

Keywords:
Deep learningKnowledge distillationModel compression algorithmsTunnel detection

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Area of Science:

  • Civil Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Tunnel lining defects like dehollowing and insufficient compaction arise from construction quality, geology, and hydrology.
  • Current detection methods suffer from complex models, poor real-time performance, and low accuracy.

Purpose of the Study:

  • To develop a lightweight and accurate algorithm for tunnel lining defect detection.
  • To address the limitations of existing complex and slow detection models.

Main Methods:

  • Constructed a high-precision teacher model (YOLOv5s) with C3CSFM, MDFPN, and RWNMS modules.
  • Employed knowledge distillation, fusing feature and output dimensions for accuracy.
  • Learned mask feature relationships in spatial and channel dimensions for real-time detection.

Main Results:

  • Reduced model parameters by 80% (from 16.03 MB to 3.20 MB).
  • Improved average accuracy from 83.4% to 86.5% (a 3.1% increase).
  • Achieved a lightweight model with maintained detection performance.

Conclusions:

  • The proposed lightweight algorithm enables high-precision and real-time detection of tunnel lining defects.
  • Knowledge distillation effectively reduces model complexity while enhancing accuracy and speed.