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Real-time driver drowsiness detection using transformer architectures: a novel deep learning approach.

Osama F Hassan¹, Ahmed F Ibrahim^2,3, Ahmed Gomaa^4,5

¹Information Systems Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia, 41522, Egypt.

Scientific Reports

|May 20, 2025

Summary

This study introduces a deep learning framework for real-time driver drowsiness detection using transformer models, achieving over 99% accuracy. The system enhances road safety by reliably identifying drowsiness and triggering timely alarms.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Driver drowsiness is a major cause of road accidents, leading to significant societal and economic losses.
Existing drowsiness detection methods often lack accuracy and robustness in real-world conditions.

Purpose of the Study:

To develop a novel and robust deep learning framework for real-time driver drowsiness detection.
To leverage state-of-the-art transformer architectures and transfer learning for improved accuracy and reliability.

Main Methods:

Utilized advanced data preprocessing: image normalization, augmentation, and Haar Cascade for region-of-interest selection.
Evaluated Vision Transformer (ViT), Swin Transformer, and various transfer learning models (VGG19, DenseNet169, ResNet50V2, etc.) on the MRL Eye Dataset.
Incorporated Class Activation Mapping (CAM) for model interpretability and real-time drowsiness scoring with alarms.

Main Results:

Vision Transformer (ViT) and Swin Transformer achieved high accuracy rates of 99.15% and 99.03%, respectively.
Transformer models outperformed other evaluated models in precision, recall, and F1-score.
The system demonstrated robustness across diverse datasets (NTHU-DDD, CEW) and challenging conditions (lighting, glasses).

Conclusions:

Transformer-based deep learning architectures offer a significant advancement for driver drowsiness detection.
The proposed contactless system provides a reliable and efficient solution for enhancing road safety.
The framework shows potential for widespread adoption in advanced driver assistance systems.