A Comparison of Convolutional Neural Network Models for Driver Fatigue Detection

Road traffic crashes are among the significant risks facing millions of people around the world every day. Driver fatigue is a salient factor in road accidents. However, overcoming this factor has become possible with the use of artificial intelligence. In fact, with the development of technology, industrial companies in the automotive sector are working on intelligent cars capable of identifying the risks and avoiding them. In this work, we propose a method that identifies driver fatigue. First, we established a comparison between 10 models of Convolutional Neural Networks (CNNs), to classify the state of both mouth and eyes. After selecting the best of them, we compute the percentage of eye closure (PERCLOS) and yawning frequency of mouth (FOM) to conclude the driver state. The proposed method obtained an accuracy of 87.5%.