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Train-YOLO: An Efficient and Lightweight Network Model for Train Component Damage Detection
Hanqing Zong1, Ying Jiang2, Xinghuai Huang2
1School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|August 28, 2025
Summary
This study introduces Train-YOLO, an optimized YOLOv8 model for efficient train component fault detection. It significantly improves accuracy and reduces computational load, enabling rapid on-site deployment.
Area of Science:
- Engineering
- Computer Science
- Artificial Intelligence
Background:
- Manual inspection of train components is inefficient, error-prone, and poses safety risks.
- Existing automated methods may lack the necessary accuracy and efficiency for real-world deployment.
Purpose of the Study:
- To develop an innovative and efficient fault detection model for train components.
- To enhance the accuracy and reduce the computational demands of automated fault detection systems.
Main Methods:
- Optimization of the YOLOv8 network architecture.
- Integration of novel modules including ADown, C2f-Rep, and DHD.
- Development of the specialized Train-YOLO model for fault detection.
Main Results:
- The Train-YOLO model achieved a peak accuracy of 92.9% in train component fault detection.
- Demonstrated significant improvements in computational efficiency and detection accuracy compared to baseline models.
- The model exhibits a smaller size and reduced computational requirements.
Conclusions:
- The optimized Train-YOLO model offers a superior solution for train component fault detection.
- Its accuracy, efficiency, and lightweight design make it suitable for rapid on-site deployment.
- This approach addresses the limitations of traditional manual inspection methods.
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