Microsleep detection using Convolution Neural Network (CNN) in deep learning (DL) for accident prevention.

Abstract

World Health Organization (WHO) states that approximately 1.3 million of people die every year because of road traffic crashes. Microsleep is one of the factors of traffic crashes and the number of accidents caused by microsleep increase rapidly each day. This leads to a major accident resulting to higher number of deaths, injuries, demolition of properties and permanent disabilities. To overcome this situation, microsleep detection approach of smart vehicle needs to be studied in detail. Hence, in this project work, we use Convolution Neural Network (CNN) algorithm for this purpose. This proposed work implies face features as input data to evaluate the accuracy of microsleep traits while driving the smart vehicle. Face features data are capable to predict microsleep. CNN plays a significant role in this project due to its capability in performing both generative and descriptive tasks especially in image and video recognition. Thus, the model will analyze between open, closed, no yawn and yawn expression. This integration can be evaluated by detecting the change in face features from normal expression to microsleep symptoms such as opened eyes degree, closed eyes degree and mouth yawning. Results of the preliminary stages of this research are included and the input dataset is gained from Kaggle platform. The preliminary results show the microsleep model’s prediction with 72% accuracy by using CNN algorithm.