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 AG
2023-01-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/1/235 |
| _version_ | 1797625955632545792 |
|---|---|
| author | Israt Jahan K. M. Aslam Uddin Saydul Akbar Murad M. Saef Ullah Miah Tanvir Zaman Khan Mehedi Masud Sultan Aljahdali Anupam Kumar Bairagi |
| author_facet | Israt Jahan K. M. Aslam Uddin Saydul Akbar Murad M. Saef Ullah Miah Tanvir Zaman Khan Mehedi Masud Sultan Aljahdali Anupam Kumar Bairagi |
| author_sort | Israt Jahan |
| collection | DOAJ |
| 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 | 2024-03-11T10:03:49Z |
| format | Article |
| id | doaj.art-4e690859f2314b2a96d8a6f635e03e48 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T10:03:49Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-4e690859f2314b2a96d8a6f635e03e482023-11-16T15:12:56ZengMDPI AGElectronics2079-92922023-01-0112123510.3390/electronics120102354D: A Real-Time Driver Drowsiness Detector Using Deep LearningIsrat Jahan0K. M. Aslam Uddin1Saydul Akbar Murad2M. Saef Ullah Miah3Tanvir Zaman Khan4Mehedi Masud5Sultan Aljahdali6Anupam Kumar Bairagi7Department of Information & Communication Engineering, Noakhali Science and Technology University, Noakhali 3814, BangladeshDepartment of Information & Communication Engineering, Noakhali Science and Technology University, Noakhali 3814, BangladeshFaculty of Computing, College of Computing & Applied Sciences, Universiti Malaysia Pahang, Pekan Pahang 26600, MalaysiaFaculty of Computing, College of Computing & Applied Sciences, Universiti Malaysia Pahang, Pekan Pahang 26600, MalaysiaDepartment of Information & Communication Engineering, Noakhali Science and Technology University, Noakhali 3814, BangladeshDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaComputer Science and Engineering Discipline, Khulna University, Khulna 9208, BangladeshThere 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.https://www.mdpi.com/2079-9292/12/1/235CNNdrowsiness detectionVGG16VGG194D |
| spellingShingle | Israt Jahan K. M. Aslam Uddin Saydul Akbar Murad M. Saef Ullah Miah Tanvir Zaman Khan Mehedi Masud Sultan Aljahdali Anupam Kumar Bairagi 4D: A Real-Time Driver Drowsiness Detector Using Deep Learning Electronics CNN drowsiness detection VGG16 VGG19 4D |
| 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 | CNN drowsiness detection VGG16 VGG19 4D |
| url | https://www.mdpi.com/2079-9292/12/1/235 |
| work_keys_str_mv | AT isratjahan 4darealtimedriverdrowsinessdetectorusingdeeplearning AT kmaslamuddin 4darealtimedriverdrowsinessdetectorusingdeeplearning AT saydulakbarmurad 4darealtimedriverdrowsinessdetectorusingdeeplearning AT msaefullahmiah 4darealtimedriverdrowsinessdetectorusingdeeplearning AT tanvirzamankhan 4darealtimedriverdrowsinessdetectorusingdeeplearning AT mehedimasud 4darealtimedriverdrowsinessdetectorusingdeeplearning AT sultanaljahdali 4darealtimedriverdrowsinessdetectorusingdeeplearning AT anupamkumarbairagi 4darealtimedriverdrowsinessdetectorusingdeeplearning |