Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/25376
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dc.contributor.authorSafie S.I-
dc.contributor.authorRusmawarni Ramli-
dc.contributor.authorM Amirul Azri-
dc.contributor.authorM Aliff-
dc.contributor.authorZulhaimi Mohammad-
dc.contributor.author(UniKL MITEC)-
dc.date.accessioned2022-06-08T02:02:25Z-
dc.date.available2022-06-08T02:02:25Z-
dc.date.issued2022-06-08-
dc.identifier.urihttp://hdl.handle.net/123456789/25376-
dc.description.abstractThis paper presents the implementation of a drowsiness driving detection system using Raspberry Pi. In this work, the Convolutional Neural Network (CNN) has been used to classify drowsiness symptoms such as blinking and yawning. A total of 1310 images were used to train the CNN architecture. A 4 -layer convolution filter has been added as a layer in this CNN architecture. Adam optimization algorithm was then used to train the CNN. A real time study on the effectiveness of this prototype was conducted on 10 individuals. This proposed system successfully demonstrates a classification accuracy rate between 80% and 98%. Other factors that can affect the rate of classification accuracy, such as camera distance from the driver and lighting factors, are also studied in this paper.en_US
dc.subjectRaspberry Pien_US
dc.subjectConvolutional Neural Networken_US
dc.subjectDrowsiness detectionen_US
dc.subjectyawningen_US
dc.titleRaspberry Pi Based Driver Drowsiness Detection System Using Convolutional Neural Network (CNN)en_US
dc.conference.name18th IEEE International Colloquium on Signal Processing & Applicationsen_US
dc.conference.year2022en_US
Appears in Collections:Conference Paper



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