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Fasteners quantitative detection and lightweight deployment based on improved YOLOv8.

Tangbo Bai1,2, Jiaming Duan1,2, Ying Wang3

  • 1School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

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|October 8, 2024
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Summary
This summary is machine-generated.

This study introduces an improved YOLOv8 model for real-time, on-board quantitative detection of rail fasteners using edge AI. The enhanced method significantly boosts detection speed and accuracy for improved railway safety.

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Area of Science:

  • Railway Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Limited research exists on real-time, on-board quantitative detection of rail fasteners.
  • Accurate monitoring of rail fastener integrity is crucial for railway safety and maintenance.

Purpose of the Study:

  • To develop and validate an improved YOLOv8 based method for on-board real-time quantitative detection of rail fasteners.
  • To leverage edge artificial intelligence (AI) computing devices for efficient and accurate rail fastener analysis.

Main Methods:

  • Utilized a lightweight MobileNetV3 backbone with a SA attention mechanism for enhanced feature extraction and speed.
  • Integrated deformable convolution in the neck network for accurate fastener segmentation.
  • Optimized the model using TensorRT on a Jetson AGX Xavier edge AI device.
  • Performed pixel-level analysis of segmentation results to quantify fastener breakage and deflection.

Main Results:

  • The improved lightweight network volume was reduced by 28% compared to the original YOLOv8.
  • The detection frame rate on the edge AI device increased by 71.87% to 55 frames per second.
  • Achieved a mean Average Precision (mAP0.5) of 97.0% after refinement based on quantitative analysis.

Conclusions:

  • The proposed method enables real-time, on-board quantitative detection of rail fasteners with high accuracy and efficiency.
  • The optimized model demonstrates significant improvements in speed and size for edge AI applications.
  • This approach offers a viable solution for enhancing railway infrastructure monitoring and maintenance.