An Attention Deep Learning Framework-Based Drowsiness Detection Model for Intelligent Transportation System

Abstract

Drivers’ drowsiness has been considered one of the prime reasons for accidents and road fatalities. Drowsiness may be caused by sleep disorders resulting in unusual mental and health conditions that have detrimental effects on human lives. This article aims to present an attention deep learning (DL) framework for drivers’ drowsiness monitoring for an intelligent transportation system. The proposed imaging system, comprising an Infrared-Cut camera embedded in a microcomputer, has been employed for capturing both day and night mode images for automated detection of drivers’ drowsiness. The frames captured are preprocessed and fed to the proposed attention DL framework based on “you only look once” version 3 (YOLOv3) for eye region detection followed by eye state classification and interpretation. Feature extraction has been carried out via a convolutional neural network module, and multiscale fusion along with the non-maximum suppression method has been applied to detect and classify the eye region of the drivers for monitoring drowsiness. Moreover, the eye region has been interpreted via a classification activation map using the proposed attention module. Experimental evaluations reveal the efficacy of the proposed system on our acquired dataset and two benchmark datasets. The proposed drowsiness detection device and system can possess good potential by increasing safety in an advanced driver assistance system.