Monitoring of dangerous driving behavior of drivers based on deep learning

被引：0

作者：

Zhang F. ^{[1
]}

Wang A.J. ^{[1
]}

机构：

[1] College of Information Engineering, Zhengzhou University of Technology, Zhengzhou

来源：

Advances in Transportation Studies | 2023年 / 3卷 / Special issue期

关键词：

critic weighting method; dangerous driving behavior; deep learning; filter redundant box; spectral eigenvalues;

D O I：

10.53136/979122180922013

中图分类号：

学科分类号：

摘要：

This paper proposes a driver dangerous driving behavior monitoring method based on deep learning. Firstly, construct a driving behavior dataset by collecting images and preprocess the driving behavior data; Secondly, the improved filtering redundant box module algorithm in deep learning is used to process the original information features of the image, and ResNet-50 with residual structure in deep learning is introduced to extract feature information; Finally, the CRITIC weighting method in the objective weighting method is used to calculate the weights of various dangerous driving behaviors, and machine vision is combined to achieve driver dangerous driving behavior monitoring. The experimental results show that the accuracy rate of dangerous driving behavior monitoring in this method is 99.2%, and the monitoring time for driving behavior isl.2 seconds. © 2023, Aracne Editrice. All rights reserved.

引用

页码：149 / 160

页数：11

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