An Accurate Detection of Drowsiness Using a Graph-Based Neural Network

According to the findings of this research, a low-cost solution for identifying driver drowsiness might be the use of microsleep patterns. In contrast to the standard method, we collected pictures by putting the camera on the extreme left side of the driver and suggested two algorithms that allow reliable face and eye detections regardless of whether the driver is gazing straight at the camera or has closed his or her eyes. This was accomplished by obtaining images by positioning the camera on the driver's extreme left side. It has been recommended that a Graph Neural Network, often known as a GNN, be used in order to determine whether or not the right eye is open. The whale optimization (WO) approach was developed in order to determine which qualities are considered to be the most desirable. Eye states are utilized to identify patterns of microsleep, and an alert is then transmitted to the driver of the vehicle when one is detected. Our data set consisted of a large number of male and female participants, each of whom had unique physical traits and had been exposed to a range of lighting conditions. The suggested method achieves an accuracy of face detection of 99.9% and an accuracy of eye detection of 98.7%. The overall accuracy and precision levels of WO-GNN are, on average, 96.4 and 95.4 percent, respectively, across the board for all subject areas.