Distracted Driving Detection with Machine Learning Methods by CNN Based Feature Extraction

Abstract

Millions of people lose their lives due to accidents caused by various reasons. As the number of vehicles increases, the number of accidents also increases. When driver errors caused by technological devices are added to this, the rate of accidents is increasing more and more. Generally, the vast majority of accidents occur as a result of distractions from drivers. For this reason, there is a need for a system based on the detection of driver errors and warning the driver in modern vehicles. For this purpose, the analysis of the convolutional neural network (CNN) feature extraction based classification models was carried out in this study. The SequeezeNet CNN architecture is trained with the transfer learning method and the image features are taken before the classification layer. The images were classified by giving the obtained features as input to k-nearest neighbor (k-NN), support vector machine (SVM) and random forest (RF) machine learning algorithms. A 10-class dataset containing 22,424 driver error images was used in the training of the models. Classification successes of k-NN, SVM, RF models trained with images are 98.1%, 95.8%, and 88.7%, respectively. The highest classification success was obtained from the k-NN model. Other performance measurement metrics were also used for the detailed analysis of the classification models. It is aimed to find the most suitable model by comparing the training and testing times of the models. It is aimed that the obtained models can be used to detect driver errors over the image.