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

Abstract

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.

Pothole Detection Using Deep Learning: A Real‐Time and AI‐on‐the‐Edge Perspective

“…Altogether, machine learning and deep learning techniques have reduced complexity and cost for pothole detection. Arbawa et al [20] proposed a method for detecting road potholes using the gray-level co-occurrence matrix (GLCM) feature extractor and support vector machine (SVM) as a classifier. ey analyzed three features such as contrast, correlation, and dissimilarity.…”