Yawning Detection for Cognitive Distraction in Drivers Using AlexNet: A Deep Learning Approach

Abstract

The usage of cars has surged dramatically, with individuals preferring personal or rental vehicles for daily commuting and vacations over public transportation due to comfort and convenience. However, this preference has led to an increased risk of drowsy driving, which often results in severe accidents and fatalities, contributing to a rise in mortality rates. To address this recognizing yawning in drivers is a critical safety measure, which poses a unique challenge because of the action’s subtlety and variability. This paper proposes a yawning recognition technique utilizing AlexNet, a convolutional neural network recognized for its efficacy in image classification. The approach integrates AlexNet with image pre-processing techniques to enhance the recognition of facial gestures from static, frontal profile-view color images. It also employs various neuron-wise and layer-wise visualization methods on an AlexNet model trained with a publicly available dataset. The results underscore the effectiveness of neural networks in accurately detecting the distinctions of yawning, showcasing the potential of deep learning in improving driver safety.