• Laser & Optoelectronics Progress
  • Vol. 56, Issue 19, 191505 (2019)
Mengjing Yang, Yunqi Tang*, and Xiaojia Jiang
Author Affiliations
  • Institute of Forensic Science, People's Public Security University of China, Beijing 100038, China
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    DOI: 10.3788/LOP56.191505 Cite this Article Set citation alerts
    Mengjing Yang, Yunqi Tang, Xiaojia Jiang. Novel Shoe Type Recognition Method Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(19): 191505 Copy Citation Text show less
    Initial network model
    Fig. 1. Initial network model
    Schematic of data acquisition route
    Fig. 2. Schematic of data acquisition route
    Schematic of cutting process
    Fig. 3. Schematic of cutting process
    Examples of various types of experimental data
    Fig. 4. Examples of various types of experimental data
    Variation curves of test accuracy and train loss
    Fig. 5. Variation curves of test accuracy and train loss
    Photographs of partial misidentify of shoe type. (a) Example of shoe image with label 4 being incorrectly identified as label 14; (b) example of shoe image with label 3 being incorrectly identified as label 19
    Fig. 6. Photographs of partial misidentify of shoe type. (a) Example of shoe image with label 4 being incorrectly identified as label 14; (b) example of shoe image with label 3 being incorrectly identified as label 19
    Number of output elements3005001000
    Test accuracy /%89.6891.9193.46
    Train time /min485056
    Table 1. Effect of number of output elements in Ip1 layer on performance
    kernel_sizeNumber of layersMemory /MBTrain time /minAccuracy /%Loss
    5×5683.65091.910.2807
    3×38150.47895.810.1601
    Table 2. Effect of network depth on performance
    PoolingMemory /MBTraintime /minAccuracy /%
    Original pooling150.47895.81
    Overlapping pooling145.97296.06
    Table 3. Effect of overlapping pooling on performance
    Mengjing Yang, Yunqi Tang, Xiaojia Jiang. Novel Shoe Type Recognition Method Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(19): 191505
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