Real-Time Driver Drowsiness Detection System Using Machine Learning

Real-Time Driver Drowsiness Detection System Using Machine Learning

Sharath Kumar S R1, Harshitha B2, Karthik S3, Pranav K N4, Nisarga R Heggod5

1Information Science and Engineering & J N N College of Engineering

2 Information Science and Engineering & J N N College of Engineering

3 Information Science and Engineering & J N N College of Engineering

4 Information Science and Engineering & J N N College of Engineering

5 Information Science and Engineering & J N N College of Engineering

---------------------------------------------------------------------***---------------------------------------------------------------------

Abstract - Driver fatigue is a leading cause of road accidents globally, with significant fatalities and injuries reported annually. This paper presents a real-time Driver Drowsiness Detection System leveraging computer vision and machine learning techniques. Using Convolutional Neural Networks (CNNs), the system monitors key behavioural indicators, such as eye closure and yawning frequency, from live video feeds. Preprocessing techniques like grayscale conversion and normalization ensure robustness under varied conditions. Alerts are triggered immediately upon detecting drowsiness, enhancing road safety. The system’s non-intrusive design ensures scalability and affordability, making it suitable for integration into Advanced Driver Assistance Systems (ADAS). This paper also discusses challenges and potential enhancements, such as IoT integration and edge computing.

Key Words: Driver Drowsiness Detection, Machine Learning, CNNs, Real-Time Monitoring, Road Safety.