Accuracy Improvement of Real-Time Driver Drowsiness Detection Using Transformer Model

Drowsiness remains a significant challenge for drivers, often resulting from extended working hours, inadequate sleep, and accumulated fatigue. This condition not only impairs reaction time and decision-making but also contributes to a substantial number of road accidents globally. Therefore, reliable and timely detection of driver drowsiness is essential for enhancing transportation safety and reducing the risk of traffic-related fatalities. With the rapid progress in deep learning, numerous models have been developed to detect driver drowsiness with high accuracy. However, the real-world performance of these models can deteriorate under varying environmental conditions, such as changes in cabin illumination, facial occlusions, and dynamic shadows on the driver’s face. To address these limitations, this paper proposes a robust, real-time driver drowsiness detection model that leverages facial behavioral features and a Transformer-based neural network architecture. The Mediapipe framework is utilized to extract a comprehensive set of facial keypoints, capturing subtle facial movements and expressions indicative of drowsiness. These keypoints are then encoded to form feature vectors that serve as input to the Transformer network, enabling effective temporal modeling of facial dynamics. The proposed model is trained and evaluated on the National Tsing Hua University (NTHU) Driver Drowsiness Detection dataset, achieving a state-of-the-art accuracy of ۹۹.۷۱%, demonstrating its potential for deployment in real-world in-vehicle systems.

کلیدواژه ها:

Transformer Network ، Driver Drowsiness Detection ، Facial keypoints ، Mediapipe ، Real-time