Driver Drowsiness Detection using MobileNets and Long Short-term Memory

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dc.authorid0000-0001-9213-576Xen_US
dc.authorscopusid57204824870en_US
dc.authorscopusid57221820293en_US
dc.authorscopusid57210946041en_US
dc.contributor.authorAydemir, Gürkan
dc.contributor.authorKurnaz, Oguzhan
dc.contributor.authorBekiryazıcı, Tahir
dc.contributor.authorAvcı, Adem
dc.contributor.authorKocakulak, Mustafa
dc.date.accessioned2022-05-16T07:44:30Z
dc.date.available2022-05-16T07:44:30Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractDeep learning has been studied extensively for driver drowsiness detection using video data. However, since the proposed deep learning methods are computationally cumbersome, the commercial driver drowsiness detection methods are still using hand-crafted features such as lane deviation and percentage of eye closure. This study investigates a deep learning model that provides a fair drowsiness detection performance with a lightweight architecture. In the proposed method, Dlib library was used to detect the driver's face in individual frames of video data. The detected faces are fed into a pre-defined convolutional neural network architecture. Then, a long short-term memory network was used to capture the temporal information between the frame sequences to assess the state of drowsiness. The proposed model achieves a detection accuracy of 80% in a popular benchmark dataset. It was also verified that the model could be implemented on a commercial and inexpensive development board with a frame rate of 5 frames per second.en_US
dc.identifier.doi10.23919/ELECO54474.2021.9677724en_US
dc.identifier.endpage223en_US
dc.identifier.isbn978-605011437-9
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage220en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1973
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAydemir, Gürkan
dc.institutionauthorBekiryazıcı, Tahir
dc.institutionauthorAvcı, Adem
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof13th International Conference on Electrical and Electronics Engineeringen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectNetwork architectureen_US
dc.subjectLong short-term memoryen_US
dc.subjectDetection performanceen_US
dc.subjectLearning modelsen_US
dc.titleDriver Drowsiness Detection using MobileNets and Long Short-term Memoryen_US
dc.typeConference Objecten_US

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