Fatigued driving detection in complex environments is a challenging problem. This paper proposes a fatigued driving detection algorithm based on multi-index fusion and a state recognition network, for further analysis of driver fatigue states. This study uses a multi-task cascade convolutional neural network for face detection and facial key point detection, corrects the face according to the key points of the eye, intercepts a binoculus image to recognize the eye state, and intercepts a mouth image according to the left and right corner points to recognize the mouth state. This can improve the detection accuracy of the driver's head tilt, deflection, and so on. Next, an eye state recognition network is constructed for the binoculus image to identify the eye closure state, and a mouth state recognition network is used to identify the mouth state. Finally, a fatigue judgment model is established by combining the two characteristics of the eye state and the mouth state to further analyze the driver fatigue state. The algorithm achieved 98.42% detection accuracy on a public eye dataset and achieved 97.93% detection accuracy on an open mouth dataset. As compared with other existing algorithms, the proposed algorithm has the advantages of high accuracy and simple implementation.

, "Eye and mouth state detection algorithm based on contour feature extraction," J. Electron. Imaging 27(5), 051205 (2018), doi: 10.1117/1.JEI.27.5.051205. Abstract. Eye and mouth state analysis is an important step in fatigue detection. An algorithm that analyzes the state of the eye and mouth by extracting contour features is proposed. First, the face area is detected in the acquired image database. Then, the eyes are located by an EyeMap algorithm through a clustering method to extract the sclera-fitting eye contour and calculate the contour aspect ratio. In addition, an effective algorithm is proposed to solve the problem of contour fitting when the human eye is affected by strabismus. Meanwhile, the value of chromatism s is defined in the RGB space, and the mouth is accurately located through lip segmentation. Based on the color difference of the lip, skin, and internal mouth, the internal mouth contour can be fitted to analyze the opening state of mouth; at the same time, another unique and effective yawning judgment mechanism is considered to determine whether the driver is tired. This paper is based on the three different databases to evaluate the performance of the proposed algorithm, and it does not need training with high calculation efficiency. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

The transient performance of centrifugal pumps during the startup period has drawn more and more attention in recent years due to urgent engineering needs. In order to make certain the transient startup characteristics of a high specific-speed prototype centrifugal pump delivering the gas-liquid two-phase flow, the transient flows inside the pump are numerically simulated during the startup period using the dynamic slip region method in this paper. The results show that the difference in heads mainly focuses on the later stage of the startup period when the pump is used to transmit the pure water and the gas-liquid two-phase flow, respectively. The existence of the gas phase makes the head less than that of delivering pure water. The nondimensional head coefficient is very high at the very beginning of the startup period and then quickly drops to a stable value. The continuous variation of the attack angle at the leading edges of blades is the main reason for evolution of the internal flow field during the startup period.

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