| Yazarlar (2) |
Alper Aytekin
Yildiz Technical University, Türkiye |
Öğr. Gör. Vasfiye AYTEKİN
Batman Üniversitesi, Türkiye |
| Özet |
| One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great opportunity to regularly control the dynamics of the driver with transfer learning methods while driving, and to warn the driver in case of possible drowsiness and to focus their attention in order to prevent traffic accidents due to drowsiness. A classification study was carried out with the aim of detecting the drowsiness of the driver by the position of the eyelids and the presence of yawning movement using the Convolutional Neural Network (CNN) architecture. The dataset used in the study includes the face shapes of drivers of different genders and different ages while driving. Accuracy and F1-score parameters were used for experimental studies. The results achieved are 91 % accuracy for the VGG16 model and an F1-score of over 90 % for each class. |
| Anahtar Kelimeler |
| Deep learning | drowsiness | transfer learning | VGG16 |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | ESCI dergilerinde yayınlanan tam makale |
| Dergi Adı | APPLIED COMPUTER SYSTEMS |
| Dergi ISSN | 2255-8683 Wos Dergi |
| Dergi Tarandığı Indeksler | Emerging Sources Citation Index |
| Makale Dili | İngilizce |
| Basım Tarihi | 08-2022 |
| Cilt No | 27 |
| Sayı | 1 |
| Sayfalar | 83 / 88 |
| DOI Numarası | 10.2478/acss-2022-0009 |
| Makale Linki | https://sciendo.com/article/10.2478/acss-2022-0009 |
| Atıf Sayıları | |
| WoS | 2 |
