Image Problem Classification for Dashboard Cameras

Abstract

This paper aimed to develop a prediction model to classify problems arising in images obtained from dashboard camera video by using machine-learning algorithms. The authors generated a dataset, called the DS dataset, which contained 900 images. The dataset was divided into three groups of problems comprised of lightness problems, a combination of lightness and blur problems, and a combination of lightness and noise problems. In this study, five features on the dataset were utilised, including mean, standard deviation, entropy, histogram, and variance of the images. Classification was performed on 3 machine-learning algorithms, which were Decision Tree, Naïve Bayes and Support Vector Machines on images and partitions of the images. The experimental results showed that decision tree algorithm yielded the best performance in comparison with the two other algorithms, with the optimal prediction model obtaining an accuracy rate of up to 97.88 percent.