Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Title

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Creator

Prathap B.R.; Kumar K.P.; Hussain J.; Chowdary C.R.

Description

The number of automobiles on the road grows in lock-step with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this growing proliferation of vehicles. Accidents frequently occur in our daily lives, and are the top ten causes of mortality from injuries globally. It is now an important component of the worldwide public health burden. Every year, an estimated 1.2 million people are killed in car ac-cidents. Driver drowsiness and weariness are major con-tributors to traffic accidents this study relies on computer software and photographs, as well as a Convolutional Neural Network (CNN), to assess whether a motorist is tired. The Driver Drowsiness System is built on the Multi-Layer Feed-Forward Network concept CNN was created using around 7,000 photos of eyes in both sleepiness and non-drowsiness phases with various face layouts. These photos were divided into two datasets: training (80% of the images) and testing (20% of the images). For training purposes, the pictures in the training dataset are fed into the network. To decrease information loss as much as feasible, backpropagation techniques and optimizers are applied. We developed an algorithm to calculate ROI as well as track and evaluate motor and visual impacts. 2022, Industrial Research Institute for Automation and Measurements. All rights reserved.

Source

Journal of Automation, Mobile Robotics and Intelligent Systems, Vol-2022, No. 2, pp. 64-73.

Date

2022-01-01

Publisher

Industrial Research Institute for Automation and Measurements

Subject

AI Visuals; Artificial Intelligence; Convolutional Neural Networks; Drowsiness Detection; Image Processing; Machine Learning

Coverage

Prathap B.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Kumar K.P., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Hussain J., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India; Chowdary C.R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560074, India

Rights

All Open Access; Hybrid Gold Open Access

Relation

ISSN: 18978649

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.14313/JAMRIS/2-2022/17

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