ArticleScientific reports2026
Dynamic driver drowsiness detection with attention enhanced convolutional neural networks for real time monitoring and road safety applications.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Driver fatigue remains a major contributor to traffic accidents worldwide, underscoring the need for accurate and timely drowsiness detection systems. This work presents a real-time driver drowsiness detection framework utilizing deep learning-based facial analysis. The approach integrates pre-trained models (VGG19, VGG16, ResNet150, and DenseNet201) and a custom convolutional neural network (CNN) with an attention mechanism to classify drowsiness based on eye and mouth behavior. These models were fine-tuned and evaluated on the NTHU-DDD dataset. Experimental results show that the proposed CNN with attention mechanism achieves the highest performance, with an accuracy of 99.63% and perfect precision, recall, and F1-score. The attention mechanism further improves detection by emphasizing relevant facial regions. This framework demonstrates the potential for deployment in real-world driver monitoring systems and contributes to advancing driver-assistance technologies.
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