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Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye State.

Fazliddin Makhmudov¹, Dilmurod Turimov¹, Munis Xamidov²

¹Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea.

Sensors (Basel, Switzerland)

|December 17, 2024

Summary

This study presents a real-time system using convolutional neural networks (CNNs) to detect driver drowsiness by analyzing facial cues. The system achieved 96.54% accuracy, offering a promising solution for reducing traffic accidents caused by fatigue.

Keywords:

Haar cascade classifier VGG16 convolutional neural networks (CNNs)deep learning drowsiness detection eye closure detection facial feature analysis yawning detection

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Area of Science:

Computer Science
Artificial Intelligence
Traffic Safety

Background:

Drowsy driving is a significant cause of traffic accidents, leading to impaired cognitive function and increased risks.
Existing driver monitoring systems often lack real-time, non-intrusive capabilities for fatigue detection.

Purpose of the Study:

To analyze a real-time, non-intrusive sleepiness detection system for drivers based on convolutional neural networks (CNNs).
To evaluate the system's effectiveness in identifying fatigue indicators from in-vehicle video data.

Main Methods:

Utilized a convolutional neural network (CNN) architecture for sleepiness detection.
Employed Haar cascade classifiers for facial area extraction and advanced image processing for fatigue diagnosis.
Trained the system on a diverse dataset considering varying lighting and facial angles.

Main Results:

Achieved a testing accuracy of 96.54% in detecting driver drowsiness.
Demonstrated the effectiveness of behavioral indicators like yawning frequency and eye state detection.
Validated the system's performance under diverse conditions.

Conclusions:

CNN-based architectures are effective in addressing public safety concerns like drowsy driving accidents.
The developed system represents a significant advancement in driver monitoring and road safety.
Future work can incorporate additional behavioral and physiological measurements for enhanced detection.