An improved pedestrian detection algorithm based on YOLOv3

Xiaojuan Chen; Zhengyang Yu

doi:10.1117/12.2668070

20 April 2023 An improved pedestrian detection algorithm based on YOLOv3

Xiaojuan Chen, Zhengyang Yu

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 1260235 (2023) https://doi.org/10.1117/12.2668070
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

For the detection needs of small target size and obscured targets in the pedestrian detection human task in complex scenes and the shortcomings of existing deep learning models, this paper proposes an improved pedestrian detection algorithm based on YOLOv3. First, the three-scale detection layer is increased to a four-scale detection layer to improve the accuracy of small target recognition through the retention of high-resolution feature map position information; second, add Spatial Pyramid Pooling layer to improve the generalization ability of the model; finally, the Convolutional Block Attention Module attention mechanism module is added to reinforce the important feature information and ignore irrelevant information.

Citation Download Citation

Xiaojuan Chen and Zhengyang Yu "An improved pedestrian detection algorithm based on YOLOv3", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 1260235 (20 April 2023); https://doi.org/10.1117/12.2668070

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