Author Affiliations
Department of Data Science and Technology, North University of China, Taiyuan, Shanxi 030051, Chinashow less
Fig. 1. Flow chart of whole algorithm
Fig. 2. Flow chart of algorithm for location of chess pieces
Fig. 3. Chessboard pretreatment. (a) Perspective transformation picture; (b) ROI picture
Fig. 4. Linear mixture picture
Fig. 5. Location picture of chess pieces
Fig. 6. Flow chart of recognition algorithm
Fig. 7. Network structure
Fig. 8. Examples of chess data
Fig. 9. Training and verification results of proposed method. (a) Training accuracy and validation accuracy; (b) training loss and verification loss
Fig. 10. Recognition results of chess pieces based on CNN. (a) Partial experimental results 1; (b) partial experimental results 2
Fig. 11. Comparison of experimental results
Conv | Conv1 | Conv2 | Conv3 | Conv4 |
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Data_Size | 100×100×3 | 48×48×32 | 24×24×64 | 24×24×128 | Conv: Num_Filter | 32 | 64 | 128 | 128 | Conv: padding | 0 | 2 | 1 | 1 | Conv: Filter_Size | 5×5×3 | 5×5×32 | 3×3×64 | 24×24×128 | Conv: stride | 1 | 1 | 1 | 1 | Data_Size after convolution | 96×96×32 | 48×48×64 | 24×24×128 | 24×24×128 | Activation | ReLU | ReLU | ReLU | ReLU | Data_Size after activation | 96×96×32 | 48×48×64 | 24×24×128 | 24×24×128 | Pooling: Kernel_Size | 2×2 | 2×2 | | 2×2 | Pooling: stride | 2 | 2 | | 2 | Data_Size after pooling | 48×48×32 | 24×24×64 | 24×24×128 | 12×12×128 | LRN(Data_Size) | 48×48×32 | 24×24×64 | 24×24×128 | 12×12×128 |
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Table 1. Configuration information and data of network structure (Conv1-Conv4 layout data)
FC | FC1 | FC2 | FC3 |
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Data | 12×12×128 | 1024 | 512 | Data after FC | 1024 | 1024 | 14 | Activation | ReLU | ReLU | | Data after activation | 1024 | 1024 | | Dropout Kept_prob | 0.5 | 0.5 | | Data after dropout fitting | 1024 | 512 | |
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Table 2. Configuration information and data of network structure(FC1-FC3 layout data)
Piece | Time ofsegmentation /s | Coordinate of image | Calculated coordinate | Actual coordinate | Error /mm |
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Col /pixel | Row /pixel | | X /mm | Y /mm | X' /mm | Y' /mm |
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Red_Car | 0.173 | 1292.88 | 972.66 | -56.816 | -101.251 | -56.7 | -100.2 | 1.09 | Red_House | 0.177 | 1286.983 | 921.603 | -57.532 | -32.759 | -57.5 | -61.6 | 1.15 | Red_Ele | 0.191 | 1279.039 | 869.282 | -57.568 | -21.625 | -57.4 | -21.5 | 0.2 | Red_Knight | 0.207 | 1273.411 | 824.562 | -57.65 | 15.628 | -57.5 | 15.6 | 0.15 | Marshal | 0.182 | 1266.772 | 777.076 | -57.107 | 56.284 | -57.1 | 56.4 | 0.12 | Red_Gun | 0.186 | 1189.585 | 919.273 | 20.505 | -62.279 | 20.5 | -61.7 | 0.58 | Red_Pawn | 0.18 | 1128.461 | 776.132 | 61.964 | 55.667 | 61.7 | 55.8 | 0.29 | Green_Pawn | 0.185 | 986.305 | 614.135 | 185.19 | 210.372 | 186.2 | 210.8 | 1.1 | Green_Gun | 0.182 | 943.496 | 979.248 | 219.448 | -64.996 | 219.6 | -65 | 0.15 | General | 0.179 | 854.364 | 771.18 | 299.646 | 57.123 | 299.5 | 57.5 | 0.4 | Green_Knight | 0.184 | 850.955 | 814.87 | 300.597 | 19.501 | 300.1 | 19.2 | 0.36 | Green_Ele | 0.183 | 849.73 | 863.732 | 298.672 | -21.476 | 299.1 | -21.6 | 0.45 | Green_House | 0.183 | 845.265 | 918.192 | 298.95 | -65.306 | 298.8 | -65.9 | 0.61 | Green_Car | 0.186 | 843.988 | 956.32 | 298.05 | -101.029 | 297.6 | -101.3 | 0.53 | Total piece | 0.208 | - | - | - | - | - | - | 0.51 |
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Table 3. Location experiment of chess pieces
Experimental data | Proposed method | Ref. [3] | Ref. [14] |
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Location time /s | 0.212 | 0.484 | - | Location error /mm | 0.51 | - | 0.87 |
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Table 4. Comparison of experimental results