Infrared and Laser Engineering, Volume. 50, Issue 12, 20210856(2021)

Single-pixel fast-moving object classification based on optical-electronical hybrid neural network (Invited)

Shujun Zheng1... Manhong Yao2, Shengping Wang1, Zibang Zhang1,3, Junzheng Peng1,3, and Jingang Zhong13 |Show fewer author(s)
Author Affiliations
  • 1Department of Optoelectronic Engineering, Jinan University, Guangzhou 510632, China
  • 2School of Optoelectronic Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China
  • 3Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Guangzhou 510632, China
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    Figures & Tables(15)
    Optical configuration of structured detected single-pixel imaging
    Framework of the fully convolutional neural network
    Optical-electronical hybrid neural network
    Example of the original training images and corresponding images with random rotation and lateral shift
    Confusion matrix of the classification results on handwritten digit test set (15 kernels)
    2D convolutional kernel images of the first layer in the fully convolutional neural network
    MNIST test set classification accuracy of networks with different number of convolutional kernels
    Optical system. (a) Experimental setup; (b) Layout of the handwritten digits on disk
    A pair of binarized convolutional kernel images
    Snapshots of digit "5" in motion at different speeds captured by using a camera
    Single-pixel measurements of moving handwritten digits. (a) Single-pixel measurements of handwritten digits passing through the field of view successively in 1.5 s; (b) Partially enlarged view of the single-pixel measurements of the digit "5" in (a); (c) Result of the differential measurement from (b)
    The ten classes and example images in Fashion-MINST dataset
    Fashion-MINST test set classification accuracy of networks with different number of convolutional kernels
    • Table 1. Experiment classification results of moving handwritten digits

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      Table 1. Experiment classification results of moving handwritten digits

      Linear velocity/m·s−1Number of kernelsCorrectTotalCorrect/Total
      1.3645785218135.99%
      1052368176.80%
      1558460796.21%
      20339339100.00%
      2532334693.35%
      3018019592.31%
      2.4505737211034.93%
      1039960565.95%
      1546453586.73%
      2024927191.88%
      2520926379.47%
      3019028766.20%
      4.9265892267933.30%
      1054397355.81%
      1542062567.20%
      2019033257.23%
      2514532644.48%
      3011430137.87%
    • Table 2. Results of different models on MNIST datasets

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      Table 2. Results of different models on MNIST datasets

      Classifier nameAccuracy
      Linear classifier [20]88.00%
      SVM [23]98.60%
      6-layer neural network [24]99.65%
      Deep convolutional network [25]99.65%
      Proposed network97.99%
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    Shujun Zheng, Manhong Yao, Shengping Wang, Zibang Zhang, Junzheng Peng, Jingang Zhong. Single-pixel fast-moving object classification based on optical-electronical hybrid neural network (Invited)[J]. Infrared and Laser Engineering, 2021, 50(12): 20210856

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    Paper Information

    Category: Special issue—Single-pixel imaging

    Received: Nov. 16, 2021

    Accepted: --

    Published Online: Feb. 9, 2022

    The Author Email:

    DOI:10.3788/IRLA20210856

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