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Laser & Optoelectronics Progress, Volume. 56, Issue 8, 081007(2019)

Methods for Location and Recognition of Chess Pieces Based on Convolutional Neural Network

Xie Han*, Rong Zhao**, and Fusheng Sun***
Author Affiliations
  • Department of Data Science and Technology, North University of China, Taiyuan, Shanxi 030051, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (15)
    Equations (0)
    References (19)
    Cited By (2)
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    Paper Information
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    Figures & Tables(15)
    Flow chart of whole algorithm

    Fig. 1. Flow chart of whole algorithm

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    Flow chart of algorithm for location of chess pieces

    Fig. 2. Flow chart of algorithm for location of chess pieces

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    Chessboard pretreatment. (a) Perspective transformation picture; (b) ROI picture

    Fig. 3. Chessboard pretreatment. (a) Perspective transformation picture; (b) ROI picture

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    Linear mixture picture

    Fig. 4. Linear mixture picture

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    Location picture of chess pieces

    Fig. 5. Location picture of chess pieces

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    Flow chart of recognition algorithm

    Fig. 6. Flow chart of recognition algorithm

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    Network structure

    Fig. 7. Network structure

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    Examples of chess data

    Fig. 8. Examples of chess data

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    Training and verification results of proposed method. (a) Training accuracy and validation accuracy; (b) training loss and verification loss

    Fig. 9. Training and verification results of proposed method. (a) Training accuracy and validation accuracy; (b) training loss and verification loss

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    Recognition results of chess pieces based on CNN. (a) Partial experimental results 1; (b) partial experimental results 2

    Fig. 10. Recognition results of chess pieces based on CNN. (a) Partial experimental results 1; (b) partial experimental results 2

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    Comparison of experimental results

    Fig. 11. Comparison of experimental results

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    • Table 1. Configuration information and data of network structure (Conv1-Conv4 layout data)

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      Table 1. Configuration information and data of network structure (Conv1-Conv4 layout data)

      ConvConv1Conv2Conv3Conv4
      Data_Size100×100×348×48×3224×24×6424×24×128
      Conv: Num_Filter3264128128
      Conv: padding0211
      Conv: Filter_Size5×5×35×5×323×3×6424×24×128
      Conv: stride1111
      Data_Size after convolution96×96×3248×48×6424×24×12824×24×128
      ActivationReLUReLUReLUReLU
      Data_Size after activation96×96×3248×48×6424×24×12824×24×128
      Pooling: Kernel_Size2×22×22×2
      Pooling: stride222
      Data_Size after pooling48×48×3224×24×6424×24×12812×12×128
      LRN(Data_Size)48×48×3224×24×6424×24×12812×12×128

    • Table 2. Configuration information and data of network structure(FC1-FC3 layout data)

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      Table 2. Configuration information and data of network structure(FC1-FC3 layout data)

      FCFC1FC2FC3
      Data12×12×1281024512
      Data after FC1024102414
      ActivationReLUReLU
      Data after activation10241024
      Dropout Kept_prob0.50.5
      Data after dropout fitting1024512

    • Table 3. Location experiment of chess pieces

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      Table 3. Location experiment of chess pieces

      PieceTime ofsegmentation /sCoordinate of imageCalculated coordinateActual coordinateError /mm
      Col /pixelRow /pixelX /mmY /mmX' /mmY' /mm
      Red_Car0.1731292.88972.66-56.816-101.251-56.7-100.21.09
      Red_House0.1771286.983921.603-57.532-32.759-57.5-61.61.15
      Red_Ele0.1911279.039869.282-57.568-21.625-57.4-21.50.2
      Red_Knight0.2071273.411824.562-57.6515.628-57.515.60.15
      Marshal0.1821266.772777.076-57.10756.284-57.156.40.12
      Red_Gun0.1861189.585919.27320.505-62.27920.5-61.70.58
      Red_Pawn0.181128.461776.13261.96455.66761.755.80.29
      Green_Pawn0.185986.305614.135185.19210.372186.2210.81.1
      Green_Gun0.182943.496979.248219.448-64.996219.6-650.15
      General0.179854.364771.18299.64657.123299.557.50.4
      Green_Knight0.184850.955814.87300.59719.501300.119.20.36
      Green_Ele0.183849.73863.732298.672-21.476299.1-21.60.45
      Green_House0.183845.265918.192298.95-65.306298.8-65.90.61
      Green_Car0.186843.988956.32298.05-101.029297.6-101.30.53
      Total piece0.208------0.51

    • Table 4. Comparison of experimental results

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      Table 4. Comparison of experimental results

      Experimental dataProposed methodRef. [3]Ref. [14]
      Location time /s0.2120.484-
      Location error /mm0.51-0.87
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    Xie Han, Rong Zhao, Fusheng Sun. Methods for Location and Recognition of Chess Pieces Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(8): 081007

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    Paper Information

    Category: Image Processing

    Received: Oct. 17, 2018

    Accepted: Nov. 22, 2018

    Published Online: Jul. 26, 2019

    The Author Email: Xie Han (290949559@qq.com), Rong Zhao (tm_zhaorong@126.com), Fusheng Sun (sfs2699@163.com)

    DOI:10.3788/LOP56.081007

    Topics

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