Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A CNN-Based Approach for the Driver Drowsiness Detection by Real-Time Eye State Identification

Ruben Florez
* ORCID logo ,
Facundo Palomino-Quispe
,
Roger Jesus Coaquira-Castillo
,
Julio Cesar Herrera-Levano
,
Thuanne Paixão
,
Ana Beatriz Alvarez
ORCID logo
Version 1 : Received: 2 June 2023 / Approved: 5 June 2023 / Online: 5 June 2023 (08:26:08 CEST)

A peer-reviewed article of this Preprint also exists.

Florez, R.; Palomino-Quispe, F.; Coaquira-Castillo, R.J.; Herrera-Levano, J.C.; Paixão, T.; Alvarez, A.B. A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification. Appl. Sci. 2023, 13, 7849. Florez, R.; Palomino-Quispe, F.; Coaquira-Castillo, R.J.; Herrera-Levano, J.C.; Paixão, T.; Alvarez, A.B. A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification. Appl. Sci. 2023, 13, 7849.

Abstract

Drowsiness detection is an important task in road safety and other areas that require sustained attention. In this article, an approach to detect drowsiness in drivers is presented, focusing on the eye region, since eye fatigue is one of the first symptoms of drowsiness. The method used for the extraction of the eye region is Mediapipe, chosen for its high accuracy and robustness. Three neural networks were analyzed based on: InceptionV3, VGG16 and ResNet50V2 that implement deep learning. The database used is NITYMED, which contains videos of drivers with different levels of drowsiness. The three networks were evaluated in terms of accuracy, precision and recall in detecting drowsiness in the eye region. The results of the study show that all three convolutional neural networks have high accuracy in detecting drowsiness in the eye region. In particular, the Resnet50V2 network achieved the highest accuracy, with a rate of 99.71% on average. For better visualization of the data, the Grad-CAM technique is used, with which we get a better understanding of the performance of the algorithms in the classification process.

Keywords

Driver Monitoring System; Drowsiness Detection; Convolutional Neural Network; Grad-CAM Visualization

Subject

Computer Science and Mathematics, Computer Vision and Graphics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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