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 Paper |
Files in This Item:
File | Description | Size | Format | |
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Raspberry Pi Based Driver Drowsiness Detection System Using Convolutional Neural Network (CNN).pdf | 109.85 kB | Adobe PDF | View/Open Request a copy |
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