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Real-time Driver Drowsiness Detection Using Facial Analysis And Machine Learning Techniques.

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Real-time Driver Drowsiness Detection Using Facial Analysis And Machine Learning Techniques.

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Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques.

Siham Essahraui¹, Ismail Lamaakal¹, Ikhlas El Hamly¹

¹Multidisciplinary Faculty of Nador, Mohammed Premier University, Oujda 60000, Morocco.

Sensors (Basel, Switzerland)

|February 13, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a real-time, non-intrusive driver drowsiness detection system using facial analysis and machine learning. Advanced computer vision models like YOLOv5 and YOLOv8 achieved exceptional performance, enhancing road safety.

Keywords:

computer vision drowsiness detection drowsy driving facial analysis machine learning

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Area of Science:

Computer Science
Artificial Intelligence
Road Safety Engineering

Background:

Drowsy driving is a major global road safety concern, causing numerous accidents and fatalities.
Existing driver drowsiness detection (DDD) systems often suffer from intrusiveness and slow response times.

Purpose of the Study:

To develop and evaluate a real-time, non-intrusive driver drowsiness detection system.
To systematically assess various machine and deep learning algorithms for DDD performance.

Main Methods:

Utilized facial analysis and machine learning techniques for drowsiness detection.
Evaluated K-Nearest Neighbors (KNNs), Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and advanced Computer Vision (CV) models (YOLOv5, YOLOv8, Faster R-CNN).
Tested algorithms on NTHUDDD, YawDD, and UTA-RLDD public datasets.

Main Results:

KNNs achieved 98.89% accuracy, 99.27% precision, and 98.86% F1 score on UTA-RLDD.
YOLOv5 and YOLOv8 demonstrated 100% precision and recall with 99.5% mAP@0.5 on UTA-RLDD.
Faster R-CNN showed lower performance with 81.0% accuracy and 63.4% precision on UTA-RLDD.

Conclusions:

Advanced CV models, particularly YOLOv5 and YOLOv8, show significant promise for highly accurate, real-time driver drowsiness detection.
The developed system has the potential to substantially improve road safety through proactive, real-time alerts.