A deep learning-based real-time driver drowsiness detection and alert system using CNN-LSTM architecture. The model analyzes eye movements, mouth openness (yawning), and head pose to accurately ...
Abstract: Driver fatigue is one of the leading causes of accidents worldwide. One of the most reliable methods of measuring driver fatigue is to detect the driver's drowsiness. Drowsiness and fatigue ...
A real-time Driver Drowsiness Detection System built using Python, OpenCV, MediaPipe, and Django. The system monitors the driver's eyes through a webcam and detects drowsiness based on eye movement ...
This research addresses the challenge of monitoring railway driver drowsiness using a real-time, vision-based system powered by convolutional neural networks, specifically the YOLOv8 architecture ...
aNational Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China bState Key Laboratory of Pathogen and Biosecurity, Academy of Military ...
This research presents a Driver Drowsiness Detection System (DDDS) that uses a Convolutional Neural Network (CNN) to improve road safety. The system uses a vast dataset of 97,860 images from the ...
cRoche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann–La Roche Ltd., Basel, Switzerland dCenter for Cognitive and Computational ...
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