Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (2): 704-711.doi: 10.13229/j.cnki.jdxbgxb20200767

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Classification of drivers' head status based on improved ResNeSt

Qian-yi XU1,2(),Gui-he QIN1,2,Ming-hui SUN1,2(),Cheng-xun MENG3   

  1. 1.College of Computer Science and Technology，Jilin University，Changchun 130012，China
    2.Key Laboratory of Symbolic Computing and Knowledge Engineering，Ministry of Education，Jilin University，Changchun 130012，China
    3.Jilin Hangsheng Electronics Co. ，Jilin 132013，China
  • Received:2020-10-19 Online:2021-03-01 Published:2021-02-09
  • Contact: Ming-hui SUN E-mail:xuqynina@163.com;smh@jlu.edu.cn

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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

CLC Number: 

  • TP391.9

Fig.1

Image using PULSE method"

Fig.2

Reorganized dataset"

Fig.3

ResNeSt block structure diagram"

Fig.4

Split-Attention within a cardinal group"

Fig.5

Improved ResNeSt block structure diagram"

Fig.6

Self-Calibrated Conv module structure diagram"

Table 1

Classification algorithm comparison"

算法参数量MTop-1准确率/%

ResNet-50

ResNet-101

ResNext-50

ResNext-101

SENet-50

SENet-101

SKNet-50

SKNet-101

SCNet-50

SCNet-101

ResNeSt-50

ResNeSt-101

25.6

44.5

25.0

44.2

27.9

49.3

27.5

48.4

25.6

44.5

27.5

48.2

76.23

77.42

77.53

78.61

78.04

79.37

79.18

78.91

77.78

78.26

79.93

80.04

改进的ResNest-10148.180.51

Table 1

Comparison of data classification accuracy"

数据准确率/%

原始数据（10分类）

原始数据（13分类）

PULSE数据（单分类）

最终数据（13分类）

77.27

78.42

90.15

80.51

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