Towards Safer Roads: A Machine Learning Framework for Driver Fatigue Detection
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The goal of this research is to enhance road safety by delivering an affordable, non-intrusive, and effective drowsiness detection solution suitable for integration into mainstream vehicle systems. Driver drowsiness is a critical factor contributing to road accidents, often resulting in severe injury or death. In Malaysia alone, it is estimated that drowsiness causes between 2,000 and 3,000 traffic accidents annually. Conventional vehicle-based drowsiness detection systems, which rely on steering behavior or lane departure, often fail to detect early physiological signs of fatigue. Recent advancements in artificial intelligence (AI), particularly in computer vision and machine learning, offer new opportunities for developing low-cost, real-time drowsiness monitoring systems. This research proposes a driver drowsiness monitoring system (DDMS) that utilizes deep learning and visual behavior analysis to detect signs of fatigue through real-time monitoring of eye and mouth activity. A customized Convolutional Neural Network (CNN) is developed to classify eye states (open vs. closed), while yawning is detected using facial landmark analysis and Mouth Aspect Ratio (MAR) computations. The system is trained and evaluated using the MRL Eye Dataset, which consists of 4,000 annotated images, with data preprocessing and augmentation applied to enhance robustness. Through systematic experimentation and hyperparameter tuning, the model achieves a peak accuracy of 98%, with equally high precision, recall, and F1 Scores, using the Adam optimizer, a learning rate of 0.001, and 50 training epochs. The system also demonstrated strong real-time performance across varied lighting conditions, though challenges remain in scenarios involving occlusion (e.g., sunglasses) and extreme head positions. The results indicate that the proposed method is not only feasible but highly accurate, marking a significant advancement in proactive traffic accident prevention technologies.
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