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SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios

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  • Weiwei Zhang

    (College of Engineering, Huaqiao University, Quanzhou 362021, China
    Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Quanzhou 362021, China)

  • Xin Ma

    (College of Engineering, Huaqiao University, Quanzhou 362021, China
    Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Quanzhou 362021, China)

  • Yuzhao Zhang

    (College of Engineering, Huaqiao University, Quanzhou 362021, China)

  • Ming Ji

    (College of Engineering, Huaqiao University, Quanzhou 362021, China
    Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Quanzhou 362021, China)

  • Chenghui Zhen

    (College of Engineering, Huaqiao University, Quanzhou 362021, China
    Fujian Provincial Academic Engineering Research Centre in Industrial Intellectual Techniques and Systems, Quanzhou 362021, China)

Abstract

Due to the arbitrariness of the drone’s shooting angle of view and camera movement and the limited computing power of the drone platform, pedestrian detection in the drone scene poses a greater challenge. This paper proposes a new convolutional neural network structure, SMYOLO, which achieves the balance of accuracy and speed from three aspects: (1) By combining deep separable convolution and point convolution and replacing the activation function, the calculation amount and parameters of the original network are reduced; (2) by adding a batch normalization (BN) layer, SMYOLO accelerates the convergence and improves the generalization ability; and (3) through scale matching, reduces the feature loss of the original network. Compared with the original network model, SMYOLO reduces the accuracy of the model by only 4.36%, the model size is reduced by 76.90%, the inference speed is increased by 43.29%, and the detection target is accelerated by 33.33%, achieving minimization of the network model volume while ensuring the detection accuracy of the model.

Suggested Citation

  • Weiwei Zhang & Xin Ma & Yuzhao Zhang & Ming Ji & Chenghui Zhen, 2022. "SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios," Future Internet, MDPI, vol. 14(1), pages 1-14, January.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:1:p:21-:d:717711
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    References listed on IDEAS

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    1. Shengbo Chen & Hongchang Zhang & Zhou Lei, 2021. "Person Re-Identification Based on Attention Mechanism and Context Information Fusion," Future Internet, MDPI, vol. 13(3), pages 1-15, March.
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