Driver fatigue detection (DFD) is an effective method to prevent traffic accidents. The existing research on DFD using facial features is an effective and noninvasive fatigue detection method. However, this approach is affected by facial occlusions (glasses, sunglasses, masks, etc.) and the large facial pose deformations in the extraction of effective fatigue features. In this paper, we introduce a novel DFD method using human pose information entropy. The method first estimates human pose from video sequences and then uses them as clues to extract multiple fatigue-related features which can reduce the influence of facial occlusion and head pose deformation. Information entropy and sliding window algorithm are applied to analyse and calculate sufficient consecutive video frames to obtain more robust and accurate fatigue-related values than by using a single frame. These information entropy values are combined resorting to the support vector machine (SVM) to recognize the driver fatigue state. Experimental results show that the method can achieve much higher accuracy and robustness, and the detection speed meets the requirements of real time.

Driver distraction behavior causes a large number of traffic accidents every year, resulting in economic losses and injuries. Currently, the driver still plays an important role in the driving and control of the vehicle due to the low level of vehicle automation and the immature development of autonomous driving. Therefore, it is vital to research distraction detection for drivers. However, in realistic driving scenarios with uncertain information, they are still some challenges in efficient and accurate driver distraction detection. In this paper, an improved deep learning model based on attention mechanisms and bi-directional feature pyramid networks (BiFPN) is proposed to identify driver distractions. Firstly, an improved data augmentation strategy is introduced to increase the data diversity to enhance the generalization capability of the model. Secondly, the squeeze-and-excitation (SE) attention mechanism layer is used after the C3 module of the original backbone network to enhance the important feature information and suppress the minor feature information. Finally, the BiFPN module is introduced into the neck network to better achieve multi-scale feature fusion without increasing the calculation amount too much. The experimental results show that the method proposed in this paper has an average mean accuracy rate (mAP) of 0.956 on the test set. Compared to the original model the mAP has improved by 13.2%. The detection speed of the model is 71 frames per second, and the memory occupation is 15.9 MB. This method has the advantages of high recognition accuracy, fast detection speed, and small memory occupation of the model, which are important for achieving engineering deployment.

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Fatigue driving is one of the main causes of traffic accidents. The eye features are the important cues of fatigue detection. In order to improve the accuracy and robustness of detection based on a single eye feature, we propose a fatigue detection algorithm based on the eye feature (EFV) vector. Firstly, the coordinates of the eye region were localized with facial landmarks detector and the landmarks geometric relation (LGR) was calculated as a feature value. Secondly, a deep transfer learning network was designed to classify the driver eye state on a small dataset. The probability value of the eyes being open state was calculated. Then an eye feature vector was constructed to overcome the limitations of a single fixed threshold and a support vector machine (SVM) model was trained for eye state classification recognition. Finally, the performance of the proposed detection model was evaluated by the percentage of eyelid closure over time (PERCLOS) criterion. The results show that the accuracy of this model can reach 91.67% on the test database, which is higher than the single-feature-based method. This work lays a foundation for the online fatigue detection of train drivers and the deployment of the train driving monitoring system.