Abstract
Driver fatigue is the culprit of most traffic accidents. Visual technology can intuitively judge whether the driver is in the state of fatigue. A driver fatigue detection system based on the residual channel attention network (RCAN) and head pose estimation is proposed. In the proposed system, Retinaface is employed for face location and outputs five face landmarks. Then the RCAN is proposed to classify the state of eyes and the mouth. The RCAN includes a channel attention module, which can adaptively extract key feature vectors from the feature map, which significantly improves the classification accuracy of the RCAN. In the self-built dataset, the classification accuracy of the eye state of the RCAN reaches 98.962% and that of the mouth state reaches 98.561%, exceeding other classical convolutional neural networks. The percentage of eyelid closure over the pupil over time (PERCLOS) and the mouth opening degree (POM) are used for fatigue detection based on the state of eyes and the mouth. In addition, this article proposes to use a Perspective-n-Point (PnP) method to estimate the head pose as an essential supplement for driving fatigue detection and proposes over-angle to evaluate whether the head pose is excessively deflected. On the whole, the proposed driver fatigue system integrates 3D head pose estimation and fatigue detection based on deep learning. This system is evaluated by the four datasets and shows success of the proposed method with their high performance.
Highlights
According to the statistics of the AAA Foundation for Traffic Safety, road accidents caused by fatigue driving comprise one-eighth of total accidents
In an residual channel attention blocks (RCABs), the channel attention module is used for feature re-extraction of the current feature layer
In the channel attention module, we assume that global average-pooling (GAP) and global max-pooling (GMP) have the same impact on the classification effect of the residual channel attention network (RCAN) in the initial training state
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. This method needs a large number of sensors, even wearable sensors, to measure the driver’s physiological signal, which may cause driver discomfort. This study proposes a novel driver fatigue detection framework This framework consists of three parts: facial state recognition, head pose estimation, and fatigue assessment. Compared with the traditional visual method, this framework adds head pose estimation and takes it as an indicator of driver fatigue, which effectively improves the accuracy and robustness of this framework. According to the self-built dataset, the rationality of head pose estimation as a supplement to driver fatigue detection is verified.
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