Intelligent Drowsiness Detection and Vehicle Control System for Safety Using OpenCV and Hardware Integration

Road accidents are largely caused by drowsy driving, which emphasizes the necessity of an efficient real-time detection system to improve driver safety. The majority of current systems rely on sensor-based techniques, which have several disadvantages, including high prices, discomfort, health hazards associated with prolonged sensor use, and decreased accuracy in practical situations. This study presents an Intelligent Drowsiness Detection and Vehicle Control System that eliminates the need for intrusive sensors by utilizing computer vision-based facial landmark detection with OpenCV and Dlib. The Eye Aspect Ratio is tracked by the system to detect extended eye closure. When drowsiness is identified, the system slows down or stops the car and activates warning lights and a buzzer alert. It increases driver safety and lowers the chance of accidents by integrating hardware components for instantaneous response. The suggested system is more reliable and comfortable than conventional sensor-based solutions since it promises excellent accuracy, real-time performance, and a non-intrusive approach. This system provides a realistic and efficient solution to sleepiness detection, greatly improving road safety through software-driven analysis and automated vehicle control.