A Novel Approach for Driver Drowsiness Detection using Deep Neural Networks

One major problem that contributes to many traffic accidents that cause injuries and fatalities all over the world is drowsy driving. This research introduces the novel approach for detecting driver drowsiness using deep networks and computer vision techniques aims to solve this issue and improve road safety. The Proposed system employs an InceptionV3-based Convolutional Neural Network (CNN) model to precisely determine whether the driver's eyes are open or closed. A sizable dataset of eye images taken in actual driving situations is used to train the CNN model. Whenever the indicators of drowsiness are detected, the proposed system will identify them in real time and promptly notify the driver. The proposed model not only has strong real-time capabilities but also exhibits superior accuracy based on stringent evaluation metrics, which makes a significant contribution to efforts aimed at promoting road safety. The performance and adaptability of the system are improved and further integrates alarm activation for drowsiness detection in a variety of driving conditions.