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Automatic 3D railroad alignment detection using modified Hough transform
Jaehyuk Lee1, Jeongjun Park2, Hyunoh Shin3
1School of Civil Engineering, Chungbuk National University, Cheongju, 28644, South Korea.
Scientific Reports
|December 13, 2025
Summary
This study introduces an automated method for detecting railroad alignment using deep learning and computer vision on 3D point cloud data. The new approach enhances digital railroad modeling efficiency and accuracy for maintenance.
Area of Science:
- Civil Engineering
- Computer Science
- Geospatial Technology
Background:
- Railroad infrastructure maintenance requires accurate rail alignment for safety and efficiency.
- Traditional rail alignment detection methods are manual, subjective, and error-prone.
- Digital modeling offers potential for improved railroad maintenance processes.
Purpose of the Study:
- To develop an automated method for detecting railroad alignment from 3D point cloud data.
- To improve the efficiency and accuracy of digital railroad model creation.
- To enhance overall railroad maintenance operations.
Main Methods:
- Utilized deep learning and computer vision techniques.
- Processed 3D point cloud data for rail alignment detection.
- Validated the method on the Osong railroad test track.
Main Results:
- Achieved an average Root Mean Square Error (RMSE) of 3.57 mm in rail alignment detection.
- Significantly reduced the time needed for digital railroad model construction.
- Demonstrated enhanced efficiency in railroad maintenance tasks.
Conclusions:
- The proposed automated method offers a more accurate and efficient alternative to manual rail alignment detection.
- Deep learning and computer vision are effective tools for analyzing 3D point cloud data in railroad infrastructure.
- The approach facilitates the creation of reliable digital railroad models, supporting better maintenance strategies.
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