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

Weiwei Zhang, Xin Ma, Yuzhao Zhang, Ming Ji and Chenghui Zhen
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Weiwei Zhang: College of Engineering, Huaqiao University, Quanzhou 362021, China
Xin Ma: College of Engineering, Huaqiao University, Quanzhou 362021, China
Yuzhao Zhang: College of Engineering, Huaqiao University, Quanzhou 362021, China
Ming Ji: College of Engineering, Huaqiao University, Quanzhou 362021, China
Chenghui Zhen: College of Engineering, Huaqiao University, Quanzhou 362021, China

Future Internet, 2022, vol. 14, issue 1, 1-14

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.

Keywords: model compression; pedestrian detection; deep learning; drone scene (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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