Chinese Journal of Lasers, Volume. 49, Issue 4, 0410002(2022)

Railway Track Detection Based on Vehicle Laser Point Cloud

Weigang Li*, Yang Mei, Xiang Fan, and Yuntao Zhao
Author Affiliations
  • Engineering Research Center of Metallurgical Automation and Measurement Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
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    Figures & Tables(12)
    Flow of track detection algorithm
    Flow of Euclidean clustering based on elevation constraints
    Rail track diagram. (a) Track section; (b) rail section
    Sleeper details. (a) Top view of non-bridge area; (b) side view of non-bridge area; (c) top view of bridge area; (d) side view of bridge area
    Extraction effect of subgrade area
    Detection effect of rail surface point cloud under different grid sizes. (a)(f) Grid size is 0.04 m; (b)(g) grid size is 0.06 m; (c)(h) grid size is 0.08 m; (d)(i) grid size is 0.10 m; (e)(j) grid size is 0.12 m
    Effect of sleeper point cloud detection in different areas. (a) Non-bridge area; (b) bridge area
    Point cloud detection effect of rail surface and sleeper in different areas. (a)(d) Effect of rail surface extraction; (b)(e) effect of sleeper extraction; (c)(f) overall effect
    • Table 1. Accuracy of rail extraction under different grid sizes unit: %

      View table

      Table 1. Accuracy of rail extraction under different grid sizes unit: %

      Evaluation indicatordgrid=0.04 mdgrid=0.06 mdgrid=0.08 mdgrid=0.10 mdgrid=0.12 m
      Area 1Area 2Area 1Area 2Area 1Area 2Area 1Area 2Area 1Area 2
      r75.650.196.296.098.099.298.299.498.261.1
      p98.299.898.699.399.599.399.699.499.799.7
      q75.550.095.095.097.598.497.998.798.061.0
    • Table 2. Comparison of extraction results of rail surface using two methods unit: %

      View table

      Table 2. Comparison of extraction results of rail surface using two methods unit: %

      Evaluation indicatorYang’s methodProposed method
      Area 1Area 2AverageArea 1Area 2Average
      r95.099.197.198.599.599.0
      p99.498.999.298.099.499.1
      q94.598.196.396.698.997.8
    • Table 3. Accuracy of sleeper extraction in different basic thresholds dth of non-bridge area unit: %

      View table

      Table 3. Accuracy of sleeper extraction in different basic thresholds dth of non-bridge area unit: %

      Evaluation indicatordth=0.25 mdth=0.26 mdth=0.27 mdth=0.28 mdth=0.29 mdth=0.30 mdth=0.31 m
      r4.939.588.699.499.899.899.9
      p99.999.598.993.688.161.148.2
      q4.939.587.893.187.861.148.2
    • Table 4. Accuracy of sleeper extraction in different basic thresholds dth of bridge area unit: %

      View table

      Table 4. Accuracy of sleeper extraction in different basic thresholds dth of bridge area unit: %

      Evaluation indicatordth=0.22 mdth=0.23 mdth=0.24 mdth=0.25 mdth=0.26 mdth=0.27 mdth=0.28 m
      r48.278.696.999.399.599.799.9
      p93.994.895.894.791.888.380.9
      q46.875.392.994.191.488.180.8
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    Weigang Li, Yang Mei, Xiang Fan, Yuntao Zhao. Railway Track Detection Based on Vehicle Laser Point Cloud[J]. Chinese Journal of Lasers, 2022, 49(4): 0410002

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    Paper Information

    Received: Jun. 1, 2021

    Accepted: Jul. 30, 2021

    Published Online: Jan. 18, 2022

    The Author Email: Li Weigang (liweigang.luck@foxmail.com)

    DOI:10.3788/CJL202249.0410002

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