基于改进的ResNeSt驾驶员头部状态分类算法

doi:10.13229/j.cnki.jdxbgxb20200767

摘要/Abstract

摘要：

为快速准确地在驾驶行程中获取驾驶员头部状态以识别驾驶员状态，提出了基于改进的ResNeSt分类算法，使用Yolov4作为检测驾驶员头部的算法获取头部图像；同时，针对数据集类不平衡的问题，通过使用PULSE方法将对焦失败的图像还原，将数据补充完整；最终，实验结果达到80.51%的分类准确率，表明本文算法具有一定有效性和准确性。

关键词: 计算机应用技术, 驾驶员头部姿态检测, 图像分类, ResNeSt

Abstract:

In order to quickly and accurately obtain the driver's head status during the driving schedule to identify drivers' status， an improved ResNeSt classification algorithm is proposed in this paper. Yolov4 is used as an algorithm to detect the drivers' head to obtain head images. To solve the problem of imbalance classification of the dataset， the method named PULSE is applied to recover the focus failed image to complete the dataset. Experimental results show that the classification accuracy can reach to 80.51%， indicating that the proposed algorithm has certain validity and accuracy.

Key words: computer application technology, driver's head status detection, image classification, ResNeSt

中图分类号:

TP391.9

许骞艺,秦贵和,孙铭会,孟诚训. 基于改进的ResNeSt驾驶员头部状态分类算法[J]. 吉林大学学报(工学版), 2021, 51(2): 704-711.

Qian-yi XU,Gui-he QIN,Ming-hui SUN,Cheng-xun MENG. Classification of drivers' head status based on improved ResNeSt[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 704-711.

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