Driver Drowsiness and SeatBelt Detection Using Deep Learning Techniques

Road safety remains a critical concern, with driver fatigue and seatbelt non-compliance contributing significantly to traffic accidents. This work presents a comprehensive system that leverages Deep Learning, Machine Learning, and Computer Vision techniques to detect driver drowsiness and monitor seatbelt usage in real-time. The proposed solution utilizes data from vehicle-installed cameras, employing advanced algorithms to analyze driver eye movements and assess alertness levels. Concurrently, seatbelt detection is achieved through sophisticated image processing methods, ensuring accurate identification of seatbelt presence and proper usage. By providing real-time alerts and notifications to drivers and stakeholders, the system promotes adherence to safe driving practices and reduces the risk of accidents. This work advances intelligent transportation systems and enhances road safety through innovative, technology-driven solutions.