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Robust LiDAR-Based Train Detection via Point Cloud Segmentation for Railway Safety.

Yuxing Yang1,2, Siyue Yu2, Jimin Xiao2

  • 1School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an 710000, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
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This study introduces a new deep learning method for reliable train detection using LiDAR, improving railway safety. The approach enhances accuracy and robustness in critical areas, overcoming limitations of traditional techniques.

Area of Science:

  • Railway Engineering
  • Computer Vision
  • Sensor Technology

Background:

  • Reliable train monitoring is crucial for railway safety in critical zones.
  • Roadside LiDAR offers advantages over cameras for train geometry capture in challenging conditions.
  • Current industrial LiDAR methods struggle with background variations, leading to detection errors.

Purpose of the Study:

  • To reformulate train detection as a point-level semantic segmentation problem.
  • To develop a robust and accurate method for real-time train detection using LiDAR data.
  • To overcome the limitations of traditional background comparison techniques in railway monitoring.

Main Methods:

  • A lightweight 3D semantic segmentation network is designed to process raw LiDAR data.
Keywords:
point cloud segmentationrailway safety monitoringrobust train detection

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  • The network directly predicts train points, enabling real-time detection.
  • Clustering-based post-processing is employed to generate train-level events.

    • Main Results:

    • The proposed deep learning method significantly improves detection accuracy compared to traditional approaches.
    • The system demonstrates greater robustness against varying environmental and operational conditions.
    • Experiments on real railway data validate the effectiveness of the new approach.

    Conclusions:

    • The developed point-level semantic segmentation method offers a more reliable solution for train detection.
    • This approach is suitable for practical railway safety monitoring applications.
    • The study highlights the potential of deep learning for enhancing railway infrastructure safety.