Comparision of algorithms in between CNN and SVM for realtime Drowsiness Detection

Driver Behaviour Analysis System (DBAS) is a ROS-based driver monitoring system utilizing OpenCV, Dlib, and YOLOv5 to detect and alert on drowsiness, device usage, and other behaviors during driving.

A project which helps prevent accidents caused by the driver getting drowsy. The project is built on python using OpenCV library.

Infothon 3.0

Driver Drowsiness Detection using Machine Learning

The drowsiness detection and alarming system utilizes Python and machine learning innovatively to address the pressing problem of driver fatigue.

Prototype of an intelligent safety system for detecting driver drowsiness

Alertness detection inside vehicles or other places with straight eye alignment

This project utilizes the YOLOv7 architecture to develop a drowsiness detection system. The model is designed to identify signs of driver drowsiness, such as closed eyes, yawning, and head movements, using a custom dataset. However, the results indicate that YOLOv7 may not be the best choice for real-time drowsiness detection.

This project utilizes the YOLOv5 architecture to develop an advanced drowsiness detection system. The model is designed to identify signs of driver drowsiness, such as closed eyes, yawning, and head movements, using a custom dataset. The goal is to enhance driver safety by providing timely alerts when drowsiness is detected.

This project leverages TensorFlow's MobileNetV2 architecture to develop a drowsiness detection system. The model is designed to identify signs of driver drowsiness, such as closed eyes, yawning, and head movements, using a custom dataset. The aim is to enhance driver safety by providing timely alerts when drowsiness is detected.

This project focuses on developing a system to detect signs of driver drowsiness using dlib’s landmark detection shape_predictor utility. The system is designed to identify drowsiness indicators such as closed eyes, yawning, and head movements using a custom dataset.

Driver Drowsiness Detection with YOLOv8 and Facial Features Combat driver fatigue with this deep learning-powered system that utilizes YOLOv8 to detect open and closed eyes, accurately assessing drowsiness levels.

Driver Drowsiness Detection System - for College project expo.

Apply deeplearning for detecting and warning of driver drowsiness

Leveraging an intelligent approach to road safety

Driver Drowsiness Detector detects if a driver or a person is drowsy or not, using their eye movements.

Implementation of a system to detect fatigue using EAR (Eye Aspect Ratio).

Machine learning project to detect if a person is drowsy.

Class wrapper of the yasa package to detect drowsiness/sleep phase using EEG data