Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/25376
Title: Raspberry Pi Based Driver Drowsiness Detection System Using Convolutional Neural Network (CNN)
Authors: Safie S.I
Rusmawarni Ramli
M Amirul Azri
M Aliff
Zulhaimi Mohammad
(UniKL MITEC)
Keywords: Raspberry Pi
Convolutional Neural Network
Drowsiness detection
yawning
Issue Date: 8-Jun-2022
Abstract: This 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.
URI: http://hdl.handle.net/123456789/25376
Appears in Collections:Conference Papers



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