Advancing Road Safety through Driver Drowsiness Detection Using Deep Learning Model

Driver drowsiness poses a significant threat to public safety, contributing to numerous road accidents and fatalities annually. Drowsy drivers exhibit characteristic changes in facial expressions and behaviors, including eye closure, head nodding, and yawning. These indicators can be detected through various techniques, including image processing, computer vision, and machine learning. This research investigates a promising approach: utilizing a ResNet-101 deep convolutional neural network (CNN) for driver drowsiness detection based on eye, head, and mouth states. The model was trained on a vast dataset of 2.2 million images, covering diverse driving conditions. Despite achieving a 69% accuracy, suggesting real-world potential, computational limitations restricted training to only a quarter of the data. This necessitates further research with larger datasets and increased resources to enhance accuracy and robustness.