Detection of driver drowsiness in driving environment using deep learning methods - Tumen, Vedat; Yildirim, Ozal; Ergen, Burhan (Institute of Electrical and Electronics Engineers Inc., 2018)
In this study, a deep learning method was used to detect sleep states of the drivers in the driving environment. A convolutional neural network (CNN) model has been proposed to determine whether the eyes of certain constant face images of drivers are closed. The proposed model has a wide potential application area such as human, computer interface design, facial expression recognition, driver fatigue-sleepiness determination. This method, which was developed on driver sleepiness data, has been applied on 4,846 real eye images in the Closed Eyes In The Wild (CEW) database. Commonly used CNN models are used on the same data to compare performances of the prepared model. According to the classification results obtained, 96.5% and 92.99% of the designed model achieved success and it is seen that this structure can be used in this problem area. © 2018 IEEE.
Sürüş Ortaminda Sürücü Uykuluk Tespitinin Derin Ö?renme Yöntemleri Kullanarak Gerçekleştirilmesi
4th Electric Electronics, Computer Science, Biomedical Engineerings' Meeting, EBBT 2018 -- 18 April 2018 through 19 April 2018 -- Istanbul -- 137380
Keywords: convolutional neural networks; driver drowsiness detection; image processing