Drowsiness Detection for Drivers Using Convolutional Neural Networks (CNNs)

Abstract

Drowsy driving is a huge risk to road safety, resulting in serious accidents. Drowsiness detection in drivers can assist prevent accidents by providing timely alarms and actions. In this research, we look at how deep learning Convolutional Neural Networks (CNNs) can be used to identify tiredness. We discuss our technique, experimental results, and conclusions about the efficacy of deep learning CNNs in solving this essential road safety issue. In this work, we employed two convolutional neural networks, one is built from scratch and another one that is a pre-trained model (ResNet). Our findings show that the pre-trained outperformed the one built from scratch achieving an accuracy of 95.6%