Distracted driving behaviour recognition based on transfer learning and model fusion

被引：1

作者：

Luo G. ^{[1
]}

Xiao W. ^{[1
]}

Chen X. ^{[2
]}

Tao J. ^{[3
]}

Zhang C. ^{[3
]}

机构：

[1] Fujian (Quanzhou)-HIT Research Institute of Engineering and Technology, Quanzhou, Fujian

[2] Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, Fujian

[3] Department of Instrumental and Electrical Engineering, Xiamen University, Fujian, Xiamen

来源：

International Journal of Wireless and Mobile Computing | 2023年 / 24卷 / 02期

关键词：

deep learning; distracted driving behaviour; model fusion; pattern recognition; transfer learning;

D O I：

10.1504/IJWMC.2023.130405

中图分类号：

学科分类号：

摘要：

In the recognition of distracted driving behaviour, traditional manual feature extraction is subjective and complex; single deep convolutional network also has problems such as insufficient generalisation performance and stability. To solve the above problems, this paper proposes a distracted driving behaviour recognition method based on transfer learning and model fusion. First, based on the transfer learning method, the deep convolutional neural network models ResNet18 and ResNet34 are used to extract the features of some images, respectively. Furthermore, the pre-trained model is fine-tuned to obtain four deep convolutional neural network models. Finally, the four network models are fused by stacking method, using 5-fold cross-validation method to reduce over-fitting. Experimental results show that the recognition accuracy of distracted driving behaviour after model fusion reaches 95.47%. The fusion model has higher model generalisation performance and recognition accuracy, which can provide certain technical support for the research of distracted driving behaviour recognition. © 2023 Inderscience Enterprises Ltd.

引用

页码：159 / 168

页数：9

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