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http://hdl.handle.net/123456789/25376
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DC Field | Value | Language |
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dc.contributor.author | Safie S.I | - |
dc.contributor.author | Rusmawarni Ramli | - |
dc.contributor.author | M Amirul Azri | - |
dc.contributor.author | M Aliff | - |
dc.contributor.author | Zulhaimi Mohammad | - |
dc.contributor.author | (UniKL MITEC) | - |
dc.date.accessioned | 2022-06-08T02:02:25Z | - |
dc.date.available | 2022-06-08T02:02:25Z | - |
dc.date.issued | 2022-06-08 | - |
dc.identifier.uri | http://hdl.handle.net/123456789/25376 | - |
dc.description.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. | en_US |
dc.subject | Raspberry Pi | en_US |
dc.subject | Convolutional Neural Network | en_US |
dc.subject | Drowsiness detection | en_US |
dc.subject | yawning | en_US |
dc.title | Raspberry Pi Based Driver Drowsiness Detection System Using Convolutional Neural Network (CNN) | en_US |
dc.conference.name | 18th IEEE International Colloquium on Signal Processing & Applications | en_US |
dc.conference.year | 2022 | en_US |
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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