Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network

Ed-Doughmi, Younes; Idrissi, Najlae; Hbali, Youssef

doi:10.3390/jimaging6030008

Cited by 71 publications

(29 citation statements)

References 58 publications

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“…The trained classifier results give a classification accuracy equal to 92.33%. Likewise, in [ 49 ], the authors used an RNNs architecture to detect driver fatigue in real-time. The experimental part presents good results (92.19%).…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

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Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

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“…Ed-Doughmi et al proposed a method to analyze and predict driver drowsiness by applying a recurrent neural network on the driver's face in sequence frames. ey used a 3D convolutional network based on a repetitive neural network architecture of a multilayer model to detect the driver's drowsiness [18].…”

Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Based on Facial Key Points and LSTM

Chen

Xin

Liu

et al. 2021

Security and Communication Networks

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In recent years, fatigue driving has been a serious threat to the traffic safety, which makes the research of fatigue detection a hotspot field. Research on fatigue recognition has a great significance to improve the traffic safety. However, the existing fatigue detection methods still have room for improvement in detection accuracy and efficiency. In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software library) is used to locate facial key points to extract the fatigue feature vector of each frame. The fatigue feature vectors of multiple frames are spliced into a temporal feature sequence and sent to the LSTM (long short-term memory) network to obtain a final fatigue feature value. Experiments show that compared with other methods, the fatigue state recognition algorithm proposed in this paper has achieved better results in accuracy. The average accuracy of the proposed method in detecting key points of the face is as high as 93%, and the running time is less than half of the ordinary DLIB method.

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“…Recently, the authors provided online data sets (see Table 4 ) to extract PERCLOS and facial features for the training of the machine learning classifier. From this table, it noticed that some authors provided the NTHU-DDD [ 11 ], UTA-RLDD [ 180 ], MultiPIE [ 181 ], 3MDAD [ 182 ], MiraclHB [ 183 ], and BU-3DFE [ 184 ] datasets, based on computer vision technology to define visual features for driver fatigue. Moreover, Figure 4 and Figure 5 , we can observe that these RGB images with 65-landmark points can be used to train the network classifier for defining the features.…”

Section: Architectural Comparisonsmentioning

confidence: 99%

“…The PERCLOS had three drowsiness metrics—PER-70: the proportion of time the eyes were closed, at least 70 percent; PER-80: the proportion of time the eyes were closed, at least 80 percent; and EYE-MS: the mean square percentage of the eyelid closure rating. These features are real-time extracted from our developed simulator at IMSIU, but based on our trained CNN model from scratch, based on two popular datasets, UTA-RLDD [ 180 ] and MultiPIE [ 181 ] on a cloud server. All of these features are aggregated into one feature vector

and calculated from Equation (1).…”

Section: Architectural Comparisonsmentioning

confidence: 99%

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Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Abbas

Alsheddy

2020

Sensors

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Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

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Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network

Cited by 71 publications

References 58 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Driver Fatigue Detection Based on Facial Key Points and LSTM

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

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