Driver Distraction Detection Using CNN

Abstract: The ultimate aim of this project is to reduce the road accidents and there are several reasons for occurrence of large number of accidents. One of the main and biggest cause is the driver’s distraction while driving, so by detecting the driver’s distraction and giving an alert to maintain the driver’s focus can ultimately reduce the number of accidents. The technology we worked with in this study follows deep neural networks and image processing to prevent accidents brought on by distracted driving. In order to categorize distracted drivers into distinct groups, a CNN-based approach with vgg16 technique is employed to extract the activities of the driver from the driver picture collection. The dataset of all images, which comprises of 10 activities in 26 distinct subjects including texting, using a phone while driving, looking into the mirror, safe driving, drinking, turning behind etc., is used to create a deep learning model. When a video input is given detection is done frame by frame and as soon as the distraction is detected an alarm is given to the driver. Results from 10 epochs demonstrate that all experiments had accuracy levels more than 75%, with the greatest result being 97%.