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Three combination value of extraction features on GLCM for detecting pothole and asphalt road

Faculty of Computer Science, Brawijaya University. Veteran Road, Malang 65145, Indonesia

Received: 20 Jul 2020; Revised: 13 Nov 2020; Accepted: 27 Nov 2020; Available online: 7 Dec 2020; Published: 31 Jan 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
The rate of vehicle accidents in various regions is still high accidents caused by many factors, such as driver negligence, vehicle damage, and road damage. 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 the intelligence like humans to see all the vehicle's obstacles. 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; transportation
Funding: Brawijaya University

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