Evidence map›Paper›PMID 41390568›Full record

ArticleScientific reports2025

Automatic 3D railroad alignment detection using modified Hough transform.

Jaehyuk Lee, Jeongjun Park, Hyunoh Shin, Hyungchul Yoon

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jaehyuk LeeSchool of Civil Engineering, Chungbuk National University, Cheongju, 28644, South Korea.
Jeongjun ParkAdvanced Railroad Civil Engineering, Korea Railroad Research Institute, Uiwang, 16105, South Korea.
Hyunoh ShinDepartment of Agricultural and Rural Engineering, Chungnam National University, Daejeon, 34134, South Korea.
Hyungchul YoonSchool of Civil Engineering, Chungbuk National University, Cheongju, 28644, South Korea. hyoon@cbnu.ac.kr.

Funding

National Research Foundation of Korea RS-2022-NR071667
6 · The paper itself

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.

Indexed as

Deep learningHough transformPoint cloud dataRailroad alignmentSemantic segmentation

Identifiers

PMID41390568
PMCPMC12804780

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.