Abstract: The Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures represent significant advancements in deep learning, particularly in image recognition and sequential data processing. Traditional drowsiness detection methods primarily rely on biometric measurements such as heart rate, pulse waves, brain waves, and eye movements. . By analyzing real-time visual data from a driver’s face and eyes, the system can detect subtle signs of fatigue, such as changes in eyelid movement, eye closure rates, and facial expressions. Additionally, the system provides real-time voice alerts upon detecting signs of drowsiness, ensuring immediate intervention and enhancing driver safety. The integration of CNN and RNN thus offers a highly efficient, real-time, and scalable solution for preventing fatigue-related accidents on the road.
|
DOI:
10.17148/IJARCCE.2025.14538
