Detection and Classification of Road Damage Using Camera with GLCM and SVM

Abstract

Road damage is a common issue in large cities, caused by factors such as heavy traffic, rainfall, and inadequate road maintenance. Detecting road damage, such as potholes, cracks, distortion, fatness, and polished aggregate, is crucial to ensure the safety and comfort of road users. This study proposes a method that uses the Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) algorithms to detect road damage. The proposed method involves processing road images using the GLCM algorithm to extract texture features, such as dissimilarity, correlation, contrast, energy, and Angular Second Moment. GLCM is an effective approach for extracting texture information and generating a matrix that illustrates the relationship between image pixels. These extracted features are then fed as input to the SVM model. The SVM model is trained to classify road images into several categories, including potholes, cracks, distortion, fatness, and polished aggregate. SVM is a machine learning method that can classify data into predetermined categories based on the extracted features. The test results show that the proposed method can detect road damage with high accuracy, as indicated by the F1 score for potholes of 0.95, cracks of 0.89, distortion of 0.8, fatness of 0.89, and polished aggregate of 0.95, with an overall accuracy of 80%. By improving the dataset and reducing the number of existing damage categories, it is likely that the accuracy of the method can be increased to around 90%. This approach can serve as a tool for continuously monitoring road conditions and assisting road authorities in making decisions regarding timely road improvements.

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