A Driver Distraction Detection Method Based on Convolutional Neural Network

Abstract

The growth of the economy and technology is increasing the popularity of automotive, but it also increases the number of traffic accidents. The driver's factor is a major cause of traffic accidents and ensuring the driver's concentration while driving is an essential research topic along with the development of autonomous cars. Recent developments in artificial intelligence and advanced hardware systems have made convolutional neural networks increasingly useful in computer vision. The purpose of this article is to explore the use of ResNet-50 neural networks in detecting driver distractions. In this article, the performance of ResNet-50 neural network is studied and analyzed and the possibility of its use for distraction detection is explored. In addition, it is found that this neural network is more capable of classifying whether a driver is distracted than of classifying their specific distracted behavior.