Abstract:
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 fatigue. 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 (CNN) 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 enhance the accuracy and reliability of drowsiness detection. 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. The algorithm also identifies the driver's eyes and mouth. Furthermore, employing CNN to classify individuals as
alert, sleepy, or asleep. The proposed technique achieved an accuracy of up to 98%. The proposed approach achieved better results
compared to previous models.