Driver drowsiness detection by employing CNN and DLIB

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dc.authorid0000-0002-2529-5533
dc.authorid0000-0001-6657-9738
dc.contributor.authorNawazish, Ali
dc.contributor.authorImran, Hassan
dc.contributor.authorÖzyer, Tansel
dc.contributor.authorAlhajj, Reda
dc.date.accessioned2022-03-17T09:51:03Z
dc.date.available2022-03-17T09:51:03Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractEvery year thousands of people lose their life due to road accidents. One of the main reasons for these accidents is driver drowsiness. In driver drowsiness, the driver slept while driving, which causes the road accident, especially on the long routes. Driver fatigue and micro sleep while driving caused the fatal accident and death of human beings. To overcome this problem, we are implementing a technique in which we capture the image of the driver. After capturing the image of the driver, we process driver images to detect driver drowsiness. For the processing of the driver image, we are using two different techniques with each other. In the first technique, we are using the Dlib for image drowsiness detection by detecting that driver’s eyes are closed and the driver is yawning. In the second technique, we used CNN for the detection of yawning and the eyes of the driver are closed or not and predict driver drowsiness. After implementing the two techniques we combine the output of both techniques. After combining both techniques we test the system, and it gives us very good results.
dc.identifier.citationNawazish, A., Imran, H., Özyer, T. ve Alhajj, R. (2021). Driver drowsiness detection by employing CNN and DLIB. 22nd International Arab Conference on Information Technology (ACIT), Muscat, Oman, 21-23 December 2021. https://doi.org/10.1109/ACIT53391.2021.9677197
dc.identifier.doi10.1109/ACIT53391.2021.9677197
dc.identifier.isbn9781665419956
dc.identifier.scopus2-s2.0-85125335901
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ACIT53391.2021.9677197
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9136
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorNawazish, Ali
dc.institutionauthorImran, Hassan
dc.institutionauthorÖzyer, Tansel
dc.institutionauthorAlhajj, Reda
dc.language.isoen
dc.publisherIEEE (Institute of Electrical and Electronics Engineers)
dc.relation.ispartof22nd International Arab Conference on Information Technology (ACIT)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectCNN
dc.subjectDlib
dc.subjectDriver Drowsiness
dc.subjectPytorch
dc.subjectResnet
dc.titleDriver drowsiness detection by employing CNN and DLIB
dc.typeConference Object

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