4D : A real-time driver drowsiness detector using deep learning
There are a variety of potential uses for the classification of eye conditions, including tiredness detection, psychological condition evaluation, etc. Because of its significance, many studies utilizing typical neural network algorithms have already been published in the literature, with good resul...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38190/ http://umpir.ump.edu.my/id/eprint/38190/1/4D_A%20real-time%20driver%20drowsiness%20detector%20using%20deep%20learning.pdf |
| _version_ | 1848825444796727296 |
|---|---|
| author | Jahan, Israt Uddin, K. M. Aslam Murad, Saydul Akbar Miah, Md Saef Ullah Khan, Tanvir Zaman Masud, Mehedi Aljahdali, Sultan Bairagi, Anupam Kumar |
| author_facet | Jahan, Israt Uddin, K. M. Aslam Murad, Saydul Akbar Miah, Md Saef Ullah Khan, Tanvir Zaman Masud, Mehedi Aljahdali, Sultan Bairagi, Anupam Kumar |
| author_sort | Jahan, Israt |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | There are a variety of potential uses for the classification of eye conditions, including tiredness detection, psychological condition evaluation, etc. Because of its significance, many studies utilizing typical neural network algorithms have already been published in the literature, with good results. Convolutional neural networks (CNNs) are employed in real-time applications to achieve two goals: high accuracy and speed. However, identifying drowsiness at an early stage significantly improves the chances of being saved from accidents. Drowsiness detection can be automated by using the potential of artificial intelligence (AI), which allows us to assess more cases in less time and with a lower cost. With the help of modern deep learning (DL) and digital image processing (DIP) techniques, in this paper, we suggest a CNN model for eye state categorization, and we tested it on three CNN models (VGG16, VGG19, and 4D). A novel CNN model named the 4D model was designed to detect drowsiness based on eye state. The MRL Eye dataset was used to train the model. When trained with training samples from the same dataset, the 4D model performed very well (around 97.53% accuracy for predicting the eye state in the test dataset). The 4D model outperformed the performance of two other pretrained models (VGG16, VGG19). This paper explains how to create a complete drowsiness detection system that predicts the state of a driver’s eyes to further determine the driver’s drowsy state and alerts the driver before any severe threats to road safety. |
| first_indexed | 2025-11-15T03:29:02Z |
| format | Article |
| id | ump-38190 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:29:02Z |
| publishDate | 2023 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-381902023-09-04T07:24:23Z http://umpir.ump.edu.my/id/eprint/38190/ 4D : A real-time driver drowsiness detector using deep learning Jahan, Israt Uddin, K. M. Aslam Murad, Saydul Akbar Miah, Md Saef Ullah Khan, Tanvir Zaman Masud, Mehedi Aljahdali, Sultan Bairagi, Anupam Kumar QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) There are a variety of potential uses for the classification of eye conditions, including tiredness detection, psychological condition evaluation, etc. Because of its significance, many studies utilizing typical neural network algorithms have already been published in the literature, with good results. Convolutional neural networks (CNNs) are employed in real-time applications to achieve two goals: high accuracy and speed. However, identifying drowsiness at an early stage significantly improves the chances of being saved from accidents. Drowsiness detection can be automated by using the potential of artificial intelligence (AI), which allows us to assess more cases in less time and with a lower cost. With the help of modern deep learning (DL) and digital image processing (DIP) techniques, in this paper, we suggest a CNN model for eye state categorization, and we tested it on three CNN models (VGG16, VGG19, and 4D). A novel CNN model named the 4D model was designed to detect drowsiness based on eye state. The MRL Eye dataset was used to train the model. When trained with training samples from the same dataset, the 4D model performed very well (around 97.53% accuracy for predicting the eye state in the test dataset). The 4D model outperformed the performance of two other pretrained models (VGG16, VGG19). This paper explains how to create a complete drowsiness detection system that predicts the state of a driver’s eyes to further determine the driver’s drowsy state and alerts the driver before any severe threats to road safety. MDPI 2023-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/38190/1/4D_A%20real-time%20driver%20drowsiness%20detector%20using%20deep%20learning.pdf Jahan, Israt and Uddin, K. M. Aslam and Murad, Saydul Akbar and Miah, Md Saef Ullah and Khan, Tanvir Zaman and Masud, Mehedi and Aljahdali, Sultan and Bairagi, Anupam Kumar (2023) 4D : A real-time driver drowsiness detector using deep learning. Electronics (Switzerland), 12 (235). pp. 1-17. ISSN 2079-9292. (Published) https://doi.org/10.3390/electronics12010235 https://doi.org/10.3390/electronics12010235 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Jahan, Israt Uddin, K. M. Aslam Murad, Saydul Akbar Miah, Md Saef Ullah Khan, Tanvir Zaman Masud, Mehedi Aljahdali, Sultan Bairagi, Anupam Kumar 4D : A real-time driver drowsiness detector using deep learning |
| title | 4D : A real-time driver drowsiness detector using deep learning |
| title_full | 4D : A real-time driver drowsiness detector using deep learning |
| title_fullStr | 4D : A real-time driver drowsiness detector using deep learning |
| title_full_unstemmed | 4D : A real-time driver drowsiness detector using deep learning |
| title_short | 4D : A real-time driver drowsiness detector using deep learning |
| title_sort | 4d : a real-time driver drowsiness detector using deep learning |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/38190/ http://umpir.ump.edu.my/id/eprint/38190/ http://umpir.ump.edu.my/id/eprint/38190/ http://umpir.ump.edu.my/id/eprint/38190/1/4D_A%20real-time%20driver%20drowsiness%20detector%20using%20deep%20learning.pdf |