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Laser & Optoelectronics Progress, Volume. 62, Issue 5, 0512006(2025)

Application of Deep Learning Algorithm for Automatic Identification of Key Points for Laser Beam Pointing

Yulong Suo1,2、*, Haifeng Zhang1,3, Bao Zhang4, Jiefeng Sun1,2, Si Qin1, and Mingliang Long1
Author Affiliations
  • 1Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
  • 2School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Key Laboratory of Space Object and Debris Observation, Chinese Academy of Sciences, Nanjing 210008, Jiangsu , China
  • 4People's Liberation Army 61711, Kashi 844000, Xinjiang , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
    Equations (2)
    References (23)
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    Figures & Tables(8)
    Two typical kinds of laser beam images. (a) Laser beam image in clear weather; (b) laser beam image when laser beam hitting thin cloud

    Fig. 1. Two typical kinds of laser beam images. (a) Laser beam image in clear weather; (b) laser beam image when laser beam hitting thin cloud

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    Distribution of key points in laser beam dataset

    Fig. 2. Distribution of key points in laser beam dataset

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    Partial images of dataset after data augmentation

    Fig. 3. Partial images of dataset after data augmentation

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    Structure of YOLOv8-Pose model

    Fig. 4. Structure of YOLOv8-Pose model

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    Comparison of key point recognition performance using different methods. (a) Traditional image processing method; (b) YOLOv8-Pose

    Fig. 5. Comparison of key point recognition performance using different methods. (a) Traditional image processing method; (b) YOLOv8-Pose

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    Identification results of laser beam key point at different conditions by proposed algorithm. (a) Identification result with star interference; (b) identification result when laser beam hitting thin cloud

    Fig. 6. Identification results of laser beam key point at different conditions by proposed algorithm. (a) Identification result with star interference; (b) identification result when laser beam hitting thin cloud

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    Identification results of laser beam key point while Galileo207 satellite ranging. (a) Identification result of laser beam key point while real-time ranging; (b) real-time ranging result of Galileo207 satellite

    Fig. 7. Identification results of laser beam key point while Galileo207 satellite ranging. (a) Identification result of laser beam key point while real-time ranging; (b) real-time ranging result of Galileo207 satellite

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    • Table 1. Test results of validation set

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      Table 1. Test results of validation set

      InterferenceAtmosphericTotalDeviation below 2 pixelDeviation range is 2,3 pixel
      StarGood transparency99990
      Poor transparency41374
      Thin cloud36351
      No starGood transparency1761742
      Poor transparency95896
      Thin cloud48462
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    Yulong Suo, Haifeng Zhang, Bao Zhang, Jiefeng Sun, Si Qin, Mingliang Long. Application of Deep Learning Algorithm for Automatic Identification of Key Points for Laser Beam Pointing[J]. Laser & Optoelectronics Progress, 2025, 62(5): 0512006

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: May. 11, 2024

    Accepted: Jul. 12, 2024

    Published Online: Feb. 20, 2025

    The Author Email:

    DOI:10.3788/LOP241257

    CSTR:32186.14.LOP241257

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology