Research on pedestrian detection based on lightweight YOLOv8

Shihang Luo; Tao He; Chao Xu; Jixiang Su; Shuqin Wang; Lifeng Zhang; Hu Liang; Huipeng Li

doi:10.1117/12.3026335

27 March 2024 Research on pedestrian detection based on lightweight YOLOv8

Shihang Luo, Tao He, Chao Xu, Jixiang Su, Shuqin Wang, Lifeng Zhang, Hu Liang, Huipeng Li

Author Affiliations +

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131051W (2024) https://doi.org/10.1117/12.3026335
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

For the YOLOv8 pedestrian detection issue on embedded devices with high computational complexity and deployment challenges, we propose a novel lightweight pedestrian detection solution. This involves replacing YOLO's backbone network with lightweight models, MobileNetV3 and EfficientNetv2, and modifying the attention mechanism in the model by introducing the CBAM attention mechanism. Additionally, an SPPF module is added to the last layer of the model to enhance feature extraction. To achieve lightweighting in the head segment, we halved its parameter count. Experimental results show that compared to directly replacing the network, our approach successfully reduces the parameter count while achieving higher accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shihang Luo, Tao He, Chao Xu, Jixiang Su, Shuqin Wang, Lifeng Zhang, Hu Liang, and Huipeng Li "Research on pedestrian detection based on lightweight YOLOv8", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131051W (27 March 2024); https://doi.org/10.1117/12.3026335

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KEYWORDS

Object detection

Performance modeling

Convolution

Feature extraction

Design

Education and training

Head

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