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Laser & Optoelectronics Progress, Volume. 61, Issue 17, 1712005(2024)

Positioning and Decoding of Absolute Grating Rulers Based on End-to-End Deep Learning Framework

Jiabin Zhao1, Weibin Li1, Weicheng Ou2, Nian Cai1、*, Zhouyixiao Wu1, and Han Wang2
Author Affiliations
  • 1School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
  • 2School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
    Equations (9)
    References (21)
    Cited By (1)
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    Figures & Tables(10)
    Code element edges of an absolute grating ruler

    Fig. 1. Code element edges of an absolute grating ruler

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    Architecture of the proposed positioning and decoding method for absolute grating rulers

    Fig. 2. Architecture of the proposed positioning and decoding method for absolute grating rulers

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    The structure of UNet++

    Fig. 3. The structure of UNet++

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    Code edge positioning network

    Fig. 4. Code edge positioning network

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    Code edge extraction network

    Fig. 5. Code edge extraction network

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    Grating image of an absolute grating ruler

    Fig. 6. Grating image of an absolute grating ruler

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    Experiemental system of the absolute grating ruler

    Fig. 7. Experiemental system of the absolute grating ruler

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    Error comparison

    Fig. 8. Error comparison

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    • Table 1. Comparative experiments for different methods

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      Table 1. Comparative experiments for different methods

      MethodRMSE /μmErep /μmVariance /μm2CI /μmTime /ms
      Otsu2.438±0.1736.177（-0.750，9.000）23.79
      TRUNC2.557±0.4126.594（-0.750，9.000）23.77
      Estimate-TS［10］1.269±0.8103.019（-1.750，0.500）29.64
      EMD［12］1.414±0.8373.640（-2.500，2.000）151.33
      LinkNet［14］0.461±0.2060.272（-0.358，0.241）58.52
      Fcnet［15］0.401±0.5200.205（-0.452，0.393）145.33
      FasterNet［16］0.447±0.3060.216（-0.456，0.337）45.24
      UNet［17］0.389±0.5930.165（-0.160，0.385）75.21
      SegNet［18］0.397±0.3580.143（-0.139，0.580）66.26
      UNet++［19］0.358±0.1340.161（-0.421，0.290）80.53
      Proposed method0.265±0.0740.108（-0.206，0.243）90.65

    • Table 2. Ablation experiments

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      Table 2. Ablation experiments

      MethodRMSE /μmVariance /μm2Max /μmMin /μmCI /μmTime /ms
      LX+EX0.2780.14772.147-3.846（-0.129，0.442）81.36
      AX+EX0.2950.13564.483-0.527（-0.205，0.785）90.39
      LX+AX+CX0.4300.20511.250-1.500（-0.500，0.500）197.52
      LX+AX+EX0.2650.10842.135-2.303（-0.206，0.243）90.65
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    Jiabin Zhao, Weibin Li, Weicheng Ou, Nian Cai, Zhouyixiao Wu, Han Wang. Positioning and Decoding of Absolute Grating Rulers Based on End-to-End Deep Learning Framework[J]. Laser & Optoelectronics Progress, 2024, 61(17): 1712005

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Dec. 18, 2023

    Accepted: Feb. 5, 2024

    Published Online: Sep. 14, 2024

    The Author Email: Nian Cai (cainian@gdut.edu.cn)

    DOI:10.3788/LOP232694

    CSTR:32186.14.LOP232694

    Topics

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