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

<p>The number of automobiles on the road grows in lockstep 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 accidents. Driver drowsiness and weariness are major contributors 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.</p>
</abstract>

The number of automobiles on the road grows in lockstep 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 accidents. Driver drowsiness and weariness are major contributors 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.

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Department of Computer Science and Engineering

Journal of Automation, Mobile Robotics and Intelligent Systems

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

The number of automobiles on the road grows in lockstep with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this...

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Online veröffentlicht: 29. Mai 2023

Seitenbereich: 64 - 73

Eingereicht: 09. Feb. 2022

Akzeptiert: 03. Mai 2022

DOI: https://doi.org/10.14313/jamris-2-2022-17

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

© 2022 Boppuru Rudra Prathap et al., published by Sciendo

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

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

Online veröffentlicht: 29. Mai 2023

Seitenbereich: 64 - 73

Eingereicht: 09. Feb. 2022

Akzeptiert: 03. Mai 2022

DOI: https://doi.org/10.14313/jamris-2-2022-17

SchlüsselwörterArtificial Intelligence, Machine Learning, Drowsiness Detection, Image Processing, Convolutional Neural Networks, AI Visuals

© 2022 Boppuru Rudra Prathap et al., published by Sciendo

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

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