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Track Fastener Defect Detection Model Based on Improved YOLOv5s.

Xue Li1, Quan Wang1, Xinwen Yang2

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Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
Summary

This study introduces an improved YOLOv5s model for efficient rail fastener defect detection. The enhanced model achieves high accuracy and speed, supporting intelligent railroad inspection.

Keywords:
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Area of Science:

  • Railroad Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Traditional manual inspection of rail fasteners is insufficient for modern railroad networks.
  • Automated defect detection is crucial for ensuring railroad safety and operational reliability.

Purpose of the Study:

  • To develop an accurate, fast, and intelligent defect detection model for rail fasteners.
  • To improve upon existing target detection models for railroad infrastructure inspection.

Main Methods:

  • An improved YOLOv5s model incorporating the Convolutional Block Attention Module (CBAM) and Weighted Bidirectional Feature Pyramid Network (BiFPN).
  • Utilized K-means++ algorithm for optimal anchor box selection tailored to the fastener dataset.
  • Implemented multi-scale feature fusion and enhanced feature extraction techniques.

Main Results:

  • Achieved an average mean precision (mAP) of 97.4% for defect detection.
  • Demonstrated a detection speed of 27.3 frames per second (FPS).
  • Maintained a small model memory occupancy of 15.5 MB.

Conclusions:

  • The improved YOLOv5s model offers superior performance in accuracy, speed, and efficiency compared to existing methods.
  • The model provides robust technical support for edge deployment in real-time rail fastener defect detection.
  • This advancement contributes to safer and more reliable railroad operations through intelligent inspection.