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Laser & Optoelectronics Progress, Volume. 59, Issue 2, 0215004(2022)

Shoe Type Recognition Algorithm Based on Attention Mechanism

Jiajun Zhang, Yunqi Tang*, Zhixiong Yang, and Pengzhi Geng
Author Affiliations
  • School of Investigation, People's Public Security University of China, Beijing 100038, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
    Equations (10)
    References (20)
    Cited By (1)
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    Figures & Tables(13)
    Residual structure of the ResNet50

    Fig. 1. Residual structure of the ResNet50

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    Structure of the attention mechanism module

    Fig. 2. Structure of the attention mechanism module

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    Residual structure after introducing attention module

    Fig. 3. Residual structure after introducing attention module

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    Algorithm flow chart after introducing attention module

    Fig. 4. Algorithm flow chart after introducing attention module

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    Equipment construction and walking route

    Fig. 5. Equipment construction and walking route

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    Schematic diagram of capturing shoe images

    Fig. 6. Schematic diagram of capturing shoe images

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    Schematic diagram of correcting shoe images

    Fig. 7. Schematic diagram of correcting shoe images

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    Recognition accuracy curves of different attention mechanism models

    Fig. 8. Recognition accuracy curves of different attention mechanism models

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    Visualization results of different models. (a) Original image; (b) Grad-CAM; (c) Guided Grad-CAM

    Fig. 9. Visualization results of different models. (a) Original image; (b) Grad-CAM; (c) Guided Grad-CAM

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    Comparison of different feature layers and convolution feature aggregation methods

    Fig. 10. Comparison of different feature layers and convolution feature aggregation methods

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    • Table 1. Recognition accuracy of different attention mechanism models

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      Table 1. Recognition accuracy of different attention mechanism models

      ModelRank-1mAP
      ResNet5061.0946.34
      ResNet50+SENet59.1443.79

      ResNet50+CBAM

      ResNet50+ECA-Net

      Ours

      62.65

      59.14

      65.37

      45.86

      45.71

      47.99

    • Table 2. Comparison of different feature layers and convolution feature aggregation methods

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      Table 2. Comparison of different feature layers and convolution feature aggregation methods

      Layer and feature

      aggregation method

      		Rank-1mAP
      FC65.3747.99
      Layer 4+AvgPool69.6552.59

      Layer 4+AvgPool+MaxPool

      Layer 4+MaxPool

      73.15

      73.93

      54.44

      54.87

    • Table 3. Influence of hyper parameter ε on recognition accuracy

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      Table 3. Influence of hyper parameter ε on recognition accuracy

      εRank-1mAP
      Without Label Smoothing73.9354.87
      0.1270.0455.80
      0.1371.2156.65
      0.1474.3256.97
      0.1569.6555.84
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    Jiajun Zhang, Yunqi Tang, Zhixiong Yang, Pengzhi Geng. Shoe Type Recognition Algorithm Based on Attention Mechanism[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0215004

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    Paper Information

    Category: Machine Vision

    Received: Jan. 12, 2021

    Accepted: Mar. 16, 2021

    Published Online: Dec. 29, 2021

    The Author Email: Yunqi Tang (tangyunqi@ppsuc.edu.cn)

    DOI:10.3788/LOP202259.0215004

    Topics

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