Driver behavior detection method based on convolutional neural network and data enhancement

Lisha Yao; Fengqi Li; Simeng Jia; Ziqing Dai

doi:10.1117/12.2671042

28 April 2023 Driver behavior detection method based on convolutional neural network and data enhancement

Lisha Yao, Fengqi Li, Simeng Jia, Ziqing Dai

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126102L (2023) https://doi.org/10.1117/12.2671042
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Nowadays, safe driving has become a very serious problem all over the country and even the world. The consequences of most traffic accidents are human factors, such as tired driving, driving, making phone calls, talking back, etc. In order to reduce traffic accidents caused by human factors, based on the original convolutional neural networks (CNN), a driver behavior detection method based on convolutional neural networks and data enhancement is proposed in this paper. This method is based on the original convolutional neural network, optimizes the original convolutional neural network, and designs a model suitable for the task of this paper. At the same time, this paper introduces the data enhancement transformation method to expand the original data to improve the over fitting problem caused by the lack of data. Experiments show that the accuracy of this model is about 82.01% higher than that of the original model, and the results of using data enhancement are 4.12% higher than that of using original data.

Citation Download Citation

Lisha Yao, Fengqi Li, Simeng Jia, and Ziqing Dai "Driver behavior detection method based on convolutional neural network and data enhancement", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126102L (28 April 2023); https://doi.org/10.1117/12.2671042

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