Driver Drowsiness Detection for Accident Prevention

Among the main causes of traffic accidents and fatalities worldwide is sleepiness among drivers. Driving safety is seriously compromised by prolonged driving without enough rest because it causes microsleeps, reduces reaction times, and diminished awareness. Steering pattern analysis and physiological sensors are two examples of conventional monitoring methods that are frequently costly, invasive, or unreliable in practical settings. To get around restrictions, these studies propose deep learning and non-intrusive computer vision methods for real-time driver drowsiness detection. The device takes frontal pictures of the driver using a camera and facial landmark identification to locate and extract eye regions. A convolutional neural network model is then used to classify the eyes as either open or closed. When eye closure lasts longer than a certain threshold a indication of drowsiness and alarm is set off to alert the driver. TensorFlow, OpenCV, and Python frameworks have been used to implement the proposed system. Experimental results show that the model is robust against factors like as the presence of spectacles and achieves an overall accuracy of more than 83% under a range of scenarios, including driving during the day and at night. Furthermore, the CNNs lightweight architecture which has a maximum model size of 75 KB ensures efficient deployment on mobile devices and embedded platforms. Compared to existing systems, the suggested approach significantly reduces false alarms while maintaining real-time performance. This study demonstrates the possibilities of CNN-based methods to offer a practical, cost-effective, and scalable solution for integration enter ADAS (Advanced Driver Assistance) to improve road safety and prevent fatigue- related accidents.