Acta Optica Sinica, Volume. 42, Issue 19, 1912006(2022)

Particle Size and Position Measurement of Defocused Particle Based on Convolutional Neural Network

Xiangyun Zhang1,2, Wu Zhou1,2、*, Youxin Jiang1,2, and Xiangxuejie Xia1
Author Affiliations
  • 1School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • 2Shanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering, Shanghai 200093, China
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    Figures & Tables(8)
    Principle diagram of dual-camera defocused imaging
    Faster-RCNN network architecture[19]
    VGG16 network architecture[20]
    Flow chart of simultaneous particle size and position prediction based on convolution neural network
    Schematic diagram of dual-camera system and calibration test bench[17]. (a) Schematic diagram of dual-camera system; (b) calibration test bench
    Partial dot images after cropping
    Comparison of particle size and depth errors under different particle sizes. (a)(b) particle size error; (c)(d) depth error
    Size prediction error of standard particles in circulating sample cell
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    Xiangyun Zhang, Wu Zhou, Youxin Jiang, Xiangxuejie Xia. Particle Size and Position Measurement of Defocused Particle Based on Convolutional Neural Network[J]. Acta Optica Sinica, 2022, 42(19): 1912006

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Feb. 28, 2022

    Accepted: Apr. 16, 2022

    Published Online: Oct. 18, 2022

    The Author Email: Zhou Wu (zhouwu@usst.edu.cn)

    DOI:10.3788/AOS202242.1912006

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