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Research on Driver Fatigue Detection Method Based on Parallel Convolution Neural Network

With the complexity of traffic avement and the increase of vehicles, the traffic accidents of fatigue driving still account for a large proportion of the total traffic accidents. Drivers will wear glasses that meet their needs in order to reduce the incidence of accidents, but the occlusion of glasses and the changes and jitter of light during driving can significantly affect the accuracy of facial information detection. Aiming at the above problems, in order to extract the facial features accurately, an adaptive compensation infrared acquisition system is used, and a fatigue state detection model of parallel convolution neural network is proposed. Based on the different detection characteristics of the same image, the convolution neural network is used to automatically complete the feature learning, so as to obtain a more comprehensive description of the fatigue driving characteristics. The support vector machine is used to train the characteristics and establish the classifier to judge whether the driver is tired or not. Compared with other existing algorithms, this method obtains higher accuracy, meets the requirements of real-time detection and has high robustness to complex driving environment.