Real-Time Driver Drowsiness Detection Using Deep Learning and Computer Vision Techniques

One of the main causes of traffic accidents, driver fatigue poses a serious risk to both public safety and the effectiveness of transportation. This study introduces a real-time system for detecting driver drowsiness that uses computer vision and deep learning methods to track and evaluate driver alertness. For real-time video processing and facial landmark detection, the suggested system makes use of a Convolutional Neural Network (CNN) model created with the Keras framework and integrated with OpenCV. To ascertain the driver's degree of drowsiness, important facial features like eye closure, blink rate, and yawning frequency are examined. To guarantee accuracy and robustness, the model is tested in real time under various lighting and environmental conditions after being trained on benchmark datasets. Results from experiments show that the suggested system produces high detection accuracy. According to experimental results, the suggested system detects drowsy states with high accuracy and low latency, which qualifies it for incorporation into contemporary vehicle safety systems. By improving driver safety through automated, non-intrusive, and real-time fatigue monitoring, this work advances the development of intelligent transportation systems.