Three Combination Value of Extraction Feature on GLCM for Detecting Pothole and Asphalt Road

*Yoke Kusuma Arbawa  -  Faculty of Computer Science, Brawijaya University, Indonesia
Fitri Utaminingrum  -  Faculty of Computer Science, Brawijaya University, Indonesia
Eko Setiawan  -  Faculty of Computer Science, Brawijaya University, Indonesia
Received: 20 Jul 2020; Revised: 13 Nov 2020; Accepted: 27 Nov 2020; Published: 31 Jan 2021; Available online: 7 Dec 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer under

Citation Format:
The rate of vehicle accidents in various regions is still high—accidents caused by many factors, such as driver negligence, vehicle damage, road damage, etc. However, transportation technology developed very rapidly, for example, a smart car. The smart car is land transportation that does not use humans as drivers but uses machines automatically. However, vehicle accidents are still possible because automatic machines do not have intelligence like humans to see all the obstacles in front of the vehicle. Obstacles can take many forms, one of them is road potholes. We propose a method for detecting road potholes using the Gray-Level Cooccurrence Matrix with three features and using the Support Vector Machine as a classification method. We analyze the combination of GLCM Contrast, Correlation, and Dissimilarity features. The results showed that the combination of Contrast and Dissimilarity features had the best accuracy of 92.033% with a computing time of 0.0704 seconds per frame.
Keywords: Pothole; Detection; GLCM; SVM

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