Advanced CNN Architecture for Intelligent Driver Drowsiness Detection

ABSTRACT A Drowsiness‐Detection‐Network (DDN) is proposed that combines a Convolutional Neural Network (CNN) for spatial feature extraction with a Long Short‐Term Memory (LSTM) module for temporal dependencies. The 68‐point model approach of Dlib is used to identify and extract facial features to detect drowsiness. Further, for enhancing the interpretability of predictions, Shapley Additive Explanations (SHAP) along with the gradient‐weighted class activation mapping (Grad‐CAM) technique are incorporated. Experimental evaluations using 5‐fold cross validation on two benchmark datasets, Yawning Detection Dataset (YawDD) and University of Texas at Arlington Real‐Life Drowsiness Dataset (UTA RLDD), demonstrate that the proposed DDN consistently outperforms Inception‐V3, VGG‐16, and VGG‐19 in terms of accuracy, precision, recall, and F1‐score.