Volume 41 Issue 3
Jun.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(3): 111-118
ZHANG Yang, ZHANG Shuaifeng, LIU Weiming. A Small-scale Pedestrian Detection Method Based on Fused Residual Networks and Feature Pyramids[J]. Journal of Transport Information and Safety, 2023, 41(3): 111-118. doi: 10.3963/j.jssn.1674-4861.2023.03.012
Citation: ZHANG Yang, ZHANG Shuaifeng, LIU Weiming. A Small-scale Pedestrian Detection Method Based on Fused Residual Networks and Feature Pyramids[J]. Journal of Transport Information and Safety, 2023, 41(3): 111-118. doi: 10.3963/j.jssn.1674-4861.2023.03.012
shu

A Small-scale Pedestrian Detection Method Based on Fused Residual Networks and Feature Pyramids

doi: 10.3963/j.jssn.1674-4861.2023.03.012
  • 1.

    School of Transportation, Fujian University of Technology, Fuzhou 350118, China

  • 2.

    Intelligent Transportation System Research Center, Fujian University of Technology, Fuzhou 350118, China

  • 3.

    School of Civil Engineering & Transportation, South China University of Technology, Guangzhou 510641, China

  • Received Date: 2022-07-31
    Available Online: 2023-09-16
    Abstract
  • Traditional detection methods for small-scale pedestrians have several issues such as overfitting, misalignment of features, and neglect of multi-scale features. Therefore, a new small-scale pedestrian detection method is proposed by combining residual networks and feature pyramids. To solve the overfitting problem of the residual networks for detecting small-scale pedestrians, a residual block with a dropout layer is applied to replace the standard residual block in the residual network structure. Moreover, the regularization effect of the dropout layer can reduce the computational complexity. The embedding feature selection module and feature alignment module in the lateral connection part of the feature pyramid networks can improve the ability of learning multi-scale features of pedestrians. The feature selection module and feature alignment module make up for the deficiency of misalignment of features and neglect of multi-scale features, which can improve the accuracy of detecting small-scale pedestrians. The proposed model is trained, tested, and validated based on the Caltech Pedestrian dataset. Experiment results show that the detection accuracy for small-scale pedestrians is 73.6% and the AP50 detection accuracy is 95.6%. Compared to the traditional method, the proposed method improves the AP (average precision) by 17.2%, AP50 (average precision when the intersection over union is greater than 0.5) by 7.8%, and detection accuracy for small-scale pedestrians by 21.6% respectively, when the number of layers is set as 50. In addition, the proposed method improves the AP by 24.5%, AP50 by 8.2%, and detection accuracy for small-scale pedestrians by 32.3%, when the number of layers is set as 101. Moreover, compared with RefindDet512 and GHM800 algorithms, the AP is improved by 20.8% and 17.7%, the AP50 is improved by 5.5% and 3.6%, and the detection accuracy for small-scale pedestrians is improved by 26.8% and 20.6%, respectively. Therefore, it can be concluded that the proposed method can effectively improve performance and accuracy of pedestrian detection, when compared to traditional algorithms.

     

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