• Laser & Optoelectronics Progress
  • Vol. 58, Issue 4, 0415009 (2021)
Yuxin Li, Fan Yang*, Zhao Liu, and Yazhong Si
Author Affiliations
  • School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
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    DOI: 10.3788/LOP202158.0415009 Cite this Article Set citation alerts
    Yuxin Li, Fan Yang, Zhao Liu, Yazhong Si. Classification Method of Crossing Vehicle Based on Improved Residual Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0415009 Copy Citation Text show less
    Flowchart of the proposed method
    Fig. 1. Flowchart of the proposed method
    Improve comparison. (a) Original residual block; (b) improved residual block
    Fig. 2. Improve comparison. (a) Original residual block; (b) improved residual block
    Normal convolution
    Fig. 3. Normal convolution
    Group convolution
    Fig. 4. Group convolution
    Attention model
    Fig. 5. Attention model
    Heat maps processed by different models. (a) Original map; (b) original model ResNet; (c) model with attention mechanism
    Fig. 6. Heat maps processed by different models. (a) Original map; (b) original model ResNet; (c) model with attention mechanism
    Partial images in Stanford Cars dataset
    Fig. 7. Partial images in Stanford Cars dataset
    Partial images in real crossing dataset
    Fig. 8. Partial images in real crossing dataset
    Accuracy of ablation experiment
    Fig. 9. Accuracy of ablation experiment
    Loss of ablation experiment
    Fig. 10. Loss of ablation experiment
    ModelAccuracy /%
    Three-scale Attention[17]81.50
    B-CNN[18]86.50
    Kernel-Pooling[19]85.70
    FA-ResNet86.97
    Table 1. Accuracy of different models on Stanford Cars dataset
    ExperimentNo.Group convolutionAttention modelFocal lossAccuracy /%
    180.44
    281.12
    388.43
    490.15
    588.91
    692.13
    794.19
    894.96
    Table 2. Results of ablation experiment
    Yuxin Li, Fan Yang, Zhao Liu, Yazhong Si. Classification Method of Crossing Vehicle Based on Improved Residual Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0415009
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