A Hybrid CNN–EAR-based Real-Time Driver Drowsiness Detection System
Driver drowsiness is among the main causes of road accidents worldwide and can lead to dreadful injuries. Early fatigue detection significantly helps to avoid crashes and enhance road safety. Conventional driver monitoring devices are typically designed to monitor behavior, such as eye blinking and yawning, or physiological indicators, such as EEG and heart rate. Moreover, single-modal systems usually experience lower accuracy because of environmental fluctuations, including lighting changes, occlusions and variations in camera angles. In this study, a hybrid CNN-EAR-based real-time driver drowsiness detection system that combines machine learning-based analyses of faces with geometrical monitoring using eye movement is presented to establish the reliability of the detection. The network utilized is that of a convolutional neural network (CNN) that identifies and classifies areas of faces and eyes under different environmental conditions. Moreover, the eye aspect ratio (EAR) method is a continuous method for assessing eye openness to identify the frequency of blink and extended eye closure. Information at the decision level of a fusion mechanism is used to combine CNN prediction and EAR threshold values to minimize false positives and enhance detection strength. Through experimental analysis, one can prove that the hybrid system is more accurate, shows better performance in terms of precision–recall, and exhibits a faster inference speed than single CNN or EAR-based methods do. The suggested framework is simple and affordable and can be incorporated into real-time intelligent driver assistance systems (IDASs). The solutions that will be implemented in the future include night vision assistance by using infrared cameras, estimation of head pose, and multimodal fatigue detection to make it more dependable.