2021
DOI: 10.1049/ipr2.12373
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Real time detection of driver fatigue based on CNN‐LSTM

Abstract: Fatigue driving is one of the main causes of traffic accidents. In order to solve this problem, a new Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) based real-time driver fatigue detection method is proposed. First of all, using simple linear clustering algorithm (SLIC), the driver's image is divided into super pixels of uniform size, which are used as input of CNN, and CNN is trained to automatically learn the features of eyes and mouth contained in the image, and then the location and ar… Show more

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Cited by 23 publications
(6 citation statements)
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References 47 publications
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“…It may have a detrimental effect among practical applications for nighttime detection. Meanwhile Ming-Zhou Liu et al used LSTM model to detect driver's fatigue state and obtained an accuracy of 99.78% with the fatigue detection algorithm of LSTM through a homemade dataset, i.e., 600 driver images [12].…”
Section: Long Term Memory Network (Lstm)mentioning
confidence: 99%
“…It may have a detrimental effect among practical applications for nighttime detection. Meanwhile Ming-Zhou Liu et al used LSTM model to detect driver's fatigue state and obtained an accuracy of 99.78% with the fatigue detection algorithm of LSTM through a homemade dataset, i.e., 600 driver images [12].…”
Section: Long Term Memory Network (Lstm)mentioning
confidence: 99%
“…Moreover, in their study, LSTM eliminated the need for extensive data preprocessing and feature extraction which could have resulted in loss of useful information in EEG data. Also, Liu et al (2022) used LSTM for detecting fatigue of drivers. In construction, Qin and Bulbul (2023) used LSTM for predicting the mental workload of workers while using augmented reality head-mounted display for construction assembly.…”
Section: Machine Learning For Mental Workload Predictionmentioning
confidence: 99%
“…The above studies are only some typical application cases. There are many successful application cases of RNN in the construction of the driving behavior recognition model [116][117][118][119][120][121][122][123][124]. It can be seen that RNN can be used for all kinds of driving behavior recognition, is suitable for highdimensional and big data sample learning, and can extract deep temporal and spatial features with an accuracy rate of more than 90%.…”
Section: Recurrent Neuralmentioning
confidence: 99%