Enhancing rail safety through real-time defect detection: A novel lightweight network approach

Yuan Cao ¹, Yue Liu ¹, Yongkui Sun ¹

⁰The School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044, China.

Accident; Analysis and Prevention +

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May 21, 2024

View abstract on PubMed

Summary

This study introduces YOLOv8n-LiteCBAM, a faster AI model for detecting internal rail defects. It achieves high accuracy and real-time speeds, crucial for railway safety and onboard systems.

Area Of Science

Computer Vision
Artificial Intelligence
Railway Engineering

Background

Railway safety relies on rapid detection of internal rail defects.
Onboard detection systems face computational resource limitations.
Existing methods struggle to balance accuracy and speed for real-time applications.

Purpose Of The Study

To develop an efficient and accurate AI model for onboard rail defect detection.
To overcome the computational constraints of current railway safety systems.
To enhance the real-time detection capabilities for high-speed rail inspection vehicles.

Main Methods

Designed YOLOv8n-LiteCBAM with a lightweight DepthStackNet backbone.
Implemented model pruning and a novel Bidirectional Convolutional Block Attention Module (BiCBAM).
Utilized ONNX Runtime for inference acceleration.

Main Results

Achieved 92.9% mean Average Precision (mAP) on a rail defect dataset.
Reached inference speeds of 136.79 FPS (GPU) and 38.36 FPS (CPU).
Outperformed other lightweight models in inference speed, meeting real-time requirements for 80 km/h inspection.

Conclusions

YOLOv8n-LiteCBAM offers a viable solution for efficient and accurate onboard rail defect detection.
The model's performance supports industrial applications in expedited rail flaw detection.
This advancement contributes to improved railway safety through enhanced detection technologies.

Keywords:

Attention mechanisms Internal rail defect detection Lightweight networks Model pruning Real-time inference