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CharanSuggala26 / repository
Drowsy Driver Detection System using OpenCV and CNN . A real-time drowsiness detection system that alerts drivers when signs of drowsiness are detected using computer vision and deep learning. This project leverages OpenCV for video capture and CNN for eye state classification.
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A real-time drowsiness detection system that alerts drivers when signs of fatigue are detected using computer vision and deep learning. This project leverages OpenCV for video capture and Convolutional Neural Networks (CNN) for eye state classification.
Drowsy driving is a major cause of road accidents. This system monitors the driver’s eyes using a webcam and alerts them if their eyes remain closed for a certain period, indicating drowsiness.
git clone https://github.com/your-username/drowsy-driver-detection.git
cd drowsy-driver-detection
pip install -r requirements.txt
python main.py
opencv-python
numpy
keras
tensorflow
pygame
Selected from shared topics, language and repository description—not editorial ratings.
nguyenquangtung /
Apply deeplearning for detecting and warning of driver drowsiness
44/100 healthAHMEDSANA /
In this project we will train our model on open and close eyes dataset then use that with face recognition library to check if the driver is sleeping or not.
34/100 healthcodewithpandey /
Using CNN and Computer Vision to determine if a driver is drowsy.
43/100 healthitsvnvr /
Real-time Driver Drowsiness Detection system using Deep Learning (CNN). Built on a Client-Server architecture (Python/PyTorch backend + C# WinForms Client) to detect eye fatigue and yawning.
43/100 healthgangeshbaskerr /
A project to detect the drowsiness of the driver and to alert the driver using an alarm. Here, we used Python, OpenCV, and Keras(Tensorflow) to build a system that can detect features from the face of the drivers and alert them if they ever fall asleep while driving. The system detects the eyes and prompts if it is closed or open.
32/100 healthChetanG696 /
The YOLO(YOU ONLY LOOK ONCE) model leverages deep CNNs for real-time driver fatigue detection. By analyzing in-car camera footage, it provides instant alerts to the driver when signs of drowsiness are detected, thereby enhancing road safety significantly.
30/100 health