Deep Learning-Based Drowsiness Detection System for Driver’s Safety
Abstract
Driver drowsiness is a leading contributor to road accidents, accounting for over 100,000 crashes and approximately ~1000 fatalities each year in the United States alone, as per National Safety Council (NSC). To mitigate this urgent public-safety risk, we propose a real-time Driver Drowsiness Detection System that achieves both high accuracy and fast inference on standard hardware. Our key idea is to combine two complementary deep-learning strategies: 1) a custom Convolutional Neural Network (CNN) paired with a Support Vector Machine (SVM) classifier, and 2) a lightweight transfer-learning model built upon a pre-trained convolutional backbone. We evaluate these approaches on two datasets: a four-class Kaggle collection of open/closed eyes and yawn/no-yawn images, and the 37-subject MRL eye dataset. For the custom CNN+SVM pipeline, we optimized the split ratios, dropout rates, and L2 regularization to achieve 100% training accuracy and 99.7% validation accuracy. For the transfer-learning model, we leveraged an existing network to accelerate training, achieving 99.4% training accuracy and 99.1% validation accuracy. Finally, we compare both models using metrics including loss curves, confusion matrices, precision, recall, and F1-score. Our system demonstrates that real-time, highly accurate drowsiness detection is achievable without specialized hardware, paving the way for broader deployment in road-safety applications.
