Driver fatigue and drowsiness are major causes of road accidents worldwide, emphasizing the need for an efficient and reliable detection system. This paper presents a Drowsiness Detection System based on Convolutional Neural Networks (CNNs), designed to monitor driver alertness in real time. The proposed system captures facial images using a camera and analyzes visual features such as eye closure, yawning frequency, and head position. A CNN-based model is trained to classify the driver’s state as active or drowsy by learning spatial and temporal patterns from the image dataset. The system generates alerts through visual or audio notifications when signs of fatigue are detected, helping to prevent potential accidents. Experimental evaluation demonstrates high accuracy and robustness of the proposed model under varying lighting and environmental conditions. The integration of deep learning with computer vision provides a cost-effective and automated solution for enhancing road safety and driver assistance technologies.
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