<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great opportunity to regularly control the dynamics of the driver with transfer learning methods while driving, and to warn the driver in case of possible drowsiness and to focus their attention in order to prevent traffic accidents due to drowsiness. A classification study was carried out with the aim of detecting the drowsiness of the driver by the position of the eyelids and the presence of yawning movement using the Convolutional Neural Network (CNN) architecture. The dataset used in the study includes the face shapes of drivers of different genders and different ages while driving. Accuracy and F1-score parameters were used for experimental studies. The results achieved are 91 % accuracy for the VGG16 model and an F1-score of over 90 % for each class.</p>
</abstract>

One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great opportunity to regularly control the dynamics of the driver with transfer learning methods while driving, and to warn the driver in case of possible drowsiness and to focus their attention in order to prevent traffic accidents due to drowsiness. A classification study was carried out with the aim of detecting the drowsiness of the driver by the position of the eyelids and the presence of yawning movement using the Convolutional Neural Network (CNN) architecture. The dataset used in the study includes the face shapes of drivers of different genders and different ages while driving. Accuracy and F1-score parameters were used for experimental studies. The results achieved are 91 % accuracy for the VGG16 model and an F1-score of over 90 % for each class.

Detection of Driver Dynamics with VGG16 Model

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Detection of Driver Dynamics with VGG16 Model"}

One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great...

Detection of Driver Dynamics with VGG16 Model

Data publikacji: 23 sie 2022

Zakres stron: 83 - 88

DOI: https://doi.org/10.2478/acss-2022-0009

Słowa kluczowe
Deep learning, drowsiness, transfer learning, VGG16

© 2022 Alper Aytekin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Detection of Driver Dynamics with VGG16 Model

Data publikacji: 23 sie 2022

Zakres stron: 83 - 88

DOI: https://doi.org/10.2478/acss-2022-0009

Słowa kluczoweDeep learning, drowsiness, transfer learning, VGG16

© 2022 Alper Aytekin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Deep learning, drowsiness, transfer learning, VGG16