Related Experiment Video
Updated: Jan 8, 2026

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
Published on: February 12, 2011
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.
Abstract:
In railroad infrastructure, timely and accurate maintenance is essential to ensure both safety and operational efficiency. Rail alignment is a critical element for constructing a reliable digital model of railroads. However, conventional rail alignment detection often relies on manual processes that are subjective and prone to error. With the advent of digital modeling technologies, there is an opportunity to improve the efficiency and accuracy of railroad maintenance. In this study, we propose an automated method for detecting railroad alignment directly from 3D point cloud data using deep learning and computer vision. The method was validated on the Osong railroad test track, achieving an average RMSE of 3.57 mm in rail alignment detection. The proposed approach significantly reduces the time required to construct digital railroad models and enhances the efficiency of railroad maintenance.
Related Concept Videos
Design Example: Alignment of a Road Line Using GIS
Relative Motion Analysis using Rotating Axes-Problem Solving
Here, in order to determine the magnitude of velocity and acceleration for point...
Adjusting a Traverse
Relative Motion Analysis using Rotating Axes
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
Horizontal Curve: Problem Solving
Curvilinear Motion: Rectangular Components
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...

