• Laser & Optoelectronics Progress
  • Vol. 56, Issue 8, 081007 (2019)
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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    DOI: 10.3788/LOP56.081007 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Flow chart of whole algorithm
    Fig. 1. Flow chart of whole algorithm
    Flow chart of algorithm for location of chess pieces
    Fig. 2. Flow chart of algorithm for location of chess pieces
    Chessboard pretreatment. (a) Perspective transformation picture; (b) ROI picture
    Fig. 3. Chessboard pretreatment. (a) Perspective transformation picture; (b) ROI picture
    Linear mixture picture
    Fig. 4. Linear mixture picture
    Location picture of chess pieces
    Fig. 5. Location picture of chess pieces
    Flow chart of recognition algorithm
    Fig. 6. Flow chart of recognition algorithm
    Network structure
    Fig. 7. Network structure
    Examples of chess data
    Fig. 8. Examples of chess data
    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
    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
    Comparison of experimental results
    Fig. 11. Comparison of experimental results
    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 1. Configuration information and data of network structure (Conv1-Conv4 layout data)
    FCFC1FC2FC3
    Data12×12×1281024512
    Data after FC1024102414
    ActivationReLUReLU
    Data after activation10241024
    Dropout Kept_prob0.50.5
    Data after dropout fitting1024512
    Table 2. Configuration information and data of network structure(FC1-FC3 layout data)
    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 3. Location experiment of chess pieces
    Experimental dataProposed methodRef. [3]Ref. [14]
    Location time /s0.2120.484-
    Location error /mm0.51-0.87
    Table 4. Comparison of experimental results
    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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