Journal of System Simulation
Abstract
Abstract: The YOLOv8n detection algorithm has a large amount of computation and parameters in the driving environment. To address this issue, a lightweight driver facial object detection algorithm YOLOv8-DF was proposed. A lightweight multi-scale convolution module (LMCM) was proposed to replace the Conv module in the network, and the dual-channel design could reduce the computation and parameter quantity of the algorithm; the multi-scale design could enrich the feature information inside the network. The lightweight convolutional GhostConv, Fasterblock module, and C2f module were fused, and a dual-channel lightweight convolution module (DLCM) was fused with the SPPF module. The experimental results show that the average precision of the YOLOv8n-DF algorithm in the test set reaches 99.2%, which is 36.7% lower than that of the YOLOv8n detection algorithm; the amount of computation is reduced by 30.9%, and the frame rate reaches 282 frames/s.
Recommended Citation
Li, Mingyu and Lin, Jiaquan
(2025)
"Lightweight Driver Face Object Detection Algorithm Based on YOLOv8-DF,"
Journal of System Simulation: Vol. 37:
Iss.
8, Article 16.
DOI: 10.16182/j.issn1004731x.joss.24-0320
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss8/16
First Page
2103
Last Page
2114
CLC
TP391.4；TP391.9
Recommended Citation
Li Mingyu, Lin Jiaquan. Lightweight Driver Face Object Detection Algorithm Based on YOLOv8-DF [J]. Journal of System Simulation, 2025, 37(8): 2103-2114.
DOI
10.16182/j.issn1004731x.joss.24-0320
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