IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

This project presents an interface design for a driver fatigue detection system using a Raspberry Pi, camera module, red light, and buzzer. This project utilizes a Haar cascade classifier to detect closed eyes and yawning patterns of the driver, aiming to alert them and prevent potential accidents caused by drowsiness. The interface design involves the integration of hardware components with software classifiers to create an effective and user-friendly system. The Raspberry Pi acts as the central processing unit, responsible for capturing video feed from the camera module and processing it. The Haar cascade classifier is employed to detect specific facial features associated with closed eyes and yawning.

To provide immediate feedback to the driver, the system incorporates a red light and a buzzer. When the controller detects closed eyes or yawning, the red light is triggered, providing a visual warning to the driver. Simultaneously, the buzzer emits an audible alarm to further alert the driver and ensure their attention is regained. The system's ability to detect closed eyes and yawning patterns facilitates timely intervention, preventing accidents and potentially saving lives.

Keywords: Raspberry Pi, Camera Interface, Driver Fatigue Detection, Haar Cascade Classifier, Closed Eyes Detection, Yawning Detection, Monitoring.