DRIVER SAFETY SYSTEM: DETECTING INTERRUPTION AND DROWSINESS WITH OBJECT DETECTION USING DEEP LEARNING ALGORITHM

Drowsiness and fatigue significantly impair a driver's ability to manage a vehicle, react quickly, and remain aware-especially during nighttime driving or extended hours on the road-posing a serious threat to road safety.To address this growing concern, especially in the modern automobile industry, there is a strong need for driver assistance systems capable of detecting fatigue and drowsiness in real time.This project proposes a non-intrusive computer vision-based prototype that monitors driver vigilance by tracking eye movements, as eyes reveal critical indicators such as gaze direction, attention level, and fatigue status.Eye tracking is central to future driver assistance systems; however, many existing techniques struggle with changes in lighting conditions, limiting their effectiveness in real-world scenarios.This solution integrates hardware for capturing live images of the driver using a camera and software for real-time eye detection and tracking, designed to function effectively under varying and realistic lighting environments.By doing so, the system aims to reduce accident risks and enhance road safety.