Description

The major aim of this project is to develop a drowsiness detection system by monitoring the eyes; it is believed that the symptoms of driver fatigue can be detected early enough to avoid a car accident We have designed a Driver Drowsiness Detection System using Python and opencv models. We propose a Convolutional Neural Network (CNN) model that is capable of detecting drowsiness based on closing of the eyelids of the driver. We apply Convolutional Neural Network (CNN) model interfaced with webcam to detect the facial images of the driver. Based on the time duration for which the eyes are closed, a score is calculated. When this score crosses a predetermined threshold, it prompts the software to play a beeping alarm and alert the driver. The score remains zero for the duration when the eyes remain open. The assessment results showed that datasets, the proposed prediction model produces better results in terms of Accuracy, precision, F1-score, specificity and false positive rate (FPR) etc.