期刊名称：International Journal of Advances in Soft Computing and Its Applications
印刷版ISSN：2074-8523
出版年度：2019
卷号：11
期号：2
页码：15-27
出版社：International Center for Scientific Research and Studies
摘要：The automatic capability of determining the road surface type is essentialinformation for autonomous vehicle navigation such as wheelchair and smartcar. This factor is crucial because determining the type of road surface canincrease security for auto vehicle users. This study used texture information toextract features from pictures using Gray Level Co-occurrence Matrix (GLCM),and combine K-Nearest Neighbor classifier (KNN) and Naïve Bayes classifier(NB) to characterize surface objects into three road classes, i.e., asphalt, gravel,and pavement. The combination of 2 classification methods is then written asKNB. The classification performance of KNB will compare with anotherclassifier. In this study, there were 750 images of original roads (asphalt, gravel,and Pavement) that were arranged into a dataset. The results show that theclassification accuracy using KNB is higher than the comparison classificationmethods.
