Dynamic driver drowsiness detection with attention enhanced convolutional neural networks for real time monitoring and road safety applications

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.