Leveraging Vision Transformer-MLP Framework for High-Precision Driver Drowsiness Image Categorization

Road accidents attributed to driver drowsiness impose a severe social and economic burden worldwide. We present a hybrid vision–based framework that combines compact embeddings from a Vision Transformer (ViT, specifically NV-DINOv2) with a lightweight multi-layer perceptron (MLP) classifier to detect driver fatigue from facial cues and eye dynamics. By decoupling high-capacity visual feature extraction (ViT) from a small task-specific head (MLP), the proposed system delivers state-of-the-art accuracy while remaining fast enough for real-time deployment on commodity hardware. The model attains 99.25% test accuracy together with 99% precision, 99% recall, and a 99% F1 score substantially outperforming MobileNetV2, VGG16, and VGG19 baselines. These results demonstrate the effectiveness of ViT embeddings for robust, low-cost driver-monitoring applications.