Detection of Driver Dynamics with VGG16 Model

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Tarih

2022

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Sciendo

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

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.

Açıklama

Anahtar Kelimeler

Deep Learning, Drowsiness, Transfer Learning, Vgg16

Kaynak

Applied Computer Systems

WoS Q Değeri

N/A

Scopus Q Değeri

Cilt

27

Sayı

1

Künye