A CNN- based Improved Walrus Optimization Algorithm to Detect Driver Drowsiness

Driver drowsiness is among the main causes of fatal traffic accidents or injuries of varying severity around the world. Every year, numerous studies and research projects address this topic. Considering the enormous potential of artificial intelligence especially the deep learning technologies, the current focus is on developing deep convolutional neural networks (CNNs) specifically designed to detect driver drowsiness. Designing these networks is difficult because most methods rely on experimentation and optimization to determine hyperparameter values. This article presents a method to enhance the hyperparameters of a convolutional neural network by using a modified version of the Walrus Optimization Algorithm (WaOA). The algorithm (M-WaOA) incorporates a logistic map to prepare the primary generation and the Mantegna’s algorithm accelerating access to the optimal solution. to Improve drowsiness detection system accuracy. The YAWDD dataset is used to obtain driving-related videos to achieve this goal. The footage is converted into individual frames, and a facial recognition algorithm is used to recognize the driver’s face. Furthermore, employing CNN to classify individuals as alert, sleepy, or asleep. The proposed technique achieved an accuracy of up to $\mathbf{9 8 \%}$. The proposed approach achieved better results compared to previous models