🚗 Driver Drowsiness Detection with Deep Learning (PoC) 🧠 A personal experiment comparing CNN + MobileNetV2 and YOLOv11 for real-time drowsiness detection. Focused on evaluating accuracy and efficiency to explore AI’s potential in preventing fatigue-related accidents. 😴⚡ A hands-on dive into safety-driven computer vision.

SafeDrive: Drowsiness Detection Web

A real-time drowsiness detection application built using Python, OpenCV, and Streamlit. The project uses facial landmark detection to monitor eye closure and yawning, alerting users when signs of drowsiness are detected. This tool aims to enhance road safety by preventing accidents caused by driver fatigue.

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SafeDrive Vision is a CPU-optimized Driver Drowsiness Detection System that uses facial landmarks, Eye Aspect Ratio (EAR), and a lightweight CNN to detect eye closure in real time. It triggers visual, text, and audio alerts, simulating an ADAS Driver Monitoring System for automotive safety applications.

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SafeDrive++ is a real-time driver drowsiness detection system using facial landmarks (EAR, head pose) with traffic and speed-aware alerting. It adapts alert sensitivity based on live traffic and vehicle speed, using multi-modal feedback (vibrations + audio) to enhance road safety in diverse driving conditions.

SafeDrive is a mobile application designed to promote safe driving practices and enhance road safety through features such as drowsiness detection, navigation, emergency assistance, and nearby attractions discovery.

🚗 SafeDrive - AI Driver Drowsiness Detection System SafeDrive is a life-saving Android application designed to prevent road accidents caused by driver fatigue. It uses Artificial Intelligence (Google ML Kit) to monitor the driver's eyes in real-time. If the driver closes their eyes for more than 1.5 seconds the app triggers a loud alarm.