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Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
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Road surface crack detection based on improved YOLOv5s.

Jiaming Ding1, Peigang Jiao1, Kangning Li1

  • 1Shandong Jiaotong University, Jinan 250357, China.

Mathematical Biosciences and Engineering : MBE
|March 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced YOLOv5s algorithm for road surface crack detection, significantly improving accuracy and speed over traditional methods. The advanced model achieves high precision and fast processing for effective crack identification.

Keywords:
Res2-C3 moduleYOLOv5sattention mechanismdeep learningroad surface crack detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Traditional manual road surface crack detection is inefficient and costly.
  • Automated methods are needed to improve accuracy and speed.

Purpose of the Study:

  • To develop an improved YOLOv5s algorithm for enhanced road surface crack detection.
  • To address limitations of low accuracy and slow speed in existing methods.

Main Methods:

  • An improved YOLOv5s algorithm was developed, incorporating Res2Net and a novel multi-scale Res2-C3 module.
  • Global attention mechanism (GAM) and dynamic snake convolution were integrated to enhance feature extraction and handle irregular shapes.
  • The modified model was trained and evaluated for road surface crack recognition.

Main Results:

  • The enhanced model achieved a mean average precision (mAP) of 93.9%, a 12.6% improvement over YOLOv5s.
  • Detection speed reached 49.97 frames per second (FPS).
  • Significant improvements in both accuracy and speed for road surface crack identification were demonstrated.

Conclusions:

  • The improved YOLOv5s algorithm effectively addresses the challenges of low accuracy and slow speed in road surface crack detection.
  • The enhanced model offers a viable solution for accurate and efficient road surface inspection.
  • The study highlights the potential of deep learning for infrastructure monitoring.