Road surface types classification using combination of K-nearest neighbor and Naïve Bayes based on GLCM

Susi Marianingsih; Fitri Utaminingrum; Fitra Abdurrachman Bachtiar

Road surface types classification using combination of K-nearest neighbor and Naïve Bayes based on GLCM

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Susi Marianingsih, Fitri Utaminingrum, Fitra Abdurrachman Bachtiar

2019 International Journal of Advances in Soft Computing and its Applications Vol. 11 Issue 2 Article Cited by 17 SDG 11

Abstract

The automatic capability of determining the road surface type is essential information for autonomous vehicle navigation such as wheelchair and smart car. This factor is crucial because determining the type of road surface can increase security for auto vehicle users. This study used texture information to extract 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 as KNB. The classification performance of KNB will compare with another classifier. In this study, there were 750 images of original roads (asphalt, gravel, and Pavement) that were arranged into a dataset. The results show that the classification accuracy using KNB is higher than the comparison classification methods. © 2019 International Center for Scientific Research and Studies.

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Faculty of Computer Science, Brawijaya University, Malang, Indonesia

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