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FaultSeg: A Dataset for Train Wheel Defect Detection.

Muhammad Zakir Shaikh^1,2,3, Sahil Jatoi^4,5, Enrique Nava Baro⁶

¹National Center for Robotics, Automation and Artificial Intelligence, Mehran University of Engineering and Technology (MUET), Jamshoro, Pakistan. muhammadzakirshaikh90@gmail.com.

Scientific Data

|February 20, 2025

Summary

A new FaultSeg dataset aids automatic train wheel defect detection, enhancing railway safety. This dataset and deep learning models like YOLOv9 improve inspection and predictive maintenance for critical transport infrastructure.

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Area of Science:

Railway Engineering
Artificial Intelligence
Computer Vision

Background:

Defective train wheels present a significant safety hazard in railway transportation.
Current inspection methods may not be sufficient to detect all types of wheel defects, posing risks to passenger safety.

Purpose of the Study:

To introduce the FaultSeg dataset for automatic train wheel defect detection.
To evaluate the effectiveness of deep learning models for identifying wheel defects using the FaultSeg dataset.

Main Methods:

The FaultSeg dataset was created with 829 manually annotated images of train wheels, identifying Cracks/Scratches, Shelling, and Discoloration.
The YOLOv9 instance segmentation algorithm was trained and evaluated on the FaultSeg dataset.

Main Results:

The YOLOv9 model achieved approximately 87% accuracy in detecting train wheel defects.
The results demonstrate the dataset's utility for training advanced deep learning models.

Conclusions:

The FaultSeg dataset is a valuable resource for developing automatic inspection systems in railway transportation.
This research supports the implementation of data-driven predictive maintenance strategies to enhance railway safety.