Driver Drowsiness Detection Based on Convolutional Neural Network Architecture Optimization Using Genetic Algorithm

In today’s analysis of traffic accident reports it becomes evident that most driving accidents result from driver drowsiness, fatigue, and lack of alertness. At these moments, drivers cannot react quickly to such changes in their state. Characteristics of drowsy driving include closed eyes, semi-open eyes, and yawing. In this research, a network architecture search mechanism has been used to obtain an optimal structure for a convolutional neural network that can effectively detect driver drowsiness. The contributions in this research include the following: Firstly, to enrich the dataset related to detecting driver drowsiness and address shortcomings in existing datasets, several videos showcasing various drowsy states have been extracted, and their frames have been labeled as either drowsy or natural. Secondly, an optimal convolutional neural network structure, including the number of layers, type of objective function, etc., has been obtained using a genetic algorithm, with FER-2013 used for achieving the optimal structure. Thirdly, transfer learning is employed, where the optimized network obtained from the genetic algorithm is considered a feature extraction component, and its fully connected layer is trained for the drowsy dataset. The proposed method outperforms other approaches in accuracy and precision, achieving an accuracy rate of approximately 99.8%.