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

共 27 条

[1]

Da Silva Oliveira F.R., Farias F.C., Comparing transfer learning approaches applied to distracted driver detection, IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1-6, (2018)

[2]

Du Y., Raman C., Black A.W., Et al., Multimodal polynomial fusion for detecting driver distraction, (2018)

[3]

Figueredo G.P., Agrawal U., Mase J.M.M., Et al., Identifying heavy goods vehicle driving styles in the United Kingdom, IEEE Transactions on Intelligent Transportation Systems, 20, 9, pp. 3324-3336, (2018)

[4]

Gumaei A., Al-Rakhami M., Hassan M.M., Et al., A deep learning-based driver distraction identification framework over edge cloud, Neural Computing and Applications, pp. 1-16, (2020)

[5]

Han X.Z., Jin R., A small sample image recognition method based on ResNet and transfer learning, Proceedings of the 5th International Conference on Computational Intelligence and Applications (ICCIA), pp. 6-81, (2020)

[6]

He K., Zhang X., Ren S., Et al., Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, (2016)

[7]

Huang T., Fu R., Chen Y., Deep driver behavior detection model based on human brain consolidated learning for shared autonomy systems, Measurement, 179, (2021)

[8]

Jiao S.J., Liu L.Y., Liu Q., A hybrid deep learning model for recognizing actions of distracted drivers, Sensors, 21, 21, (2021)

[9]

State farm distracted driver detection [EB/OL], (2017)

[10]

Koay H.V., Chuah J.H., Chow C.O., Convolutional neural network or vision transformer?. Benchmarking various machine learning models for distracted driver detection, TENCON 2021-2021 IEEE Region 10 Conference (TENCON), pp. 417-422, (2021)

← 1 2 3 →