Comparative Analysis of Texture Feature Extraction-Based Machine Learning Algorithms for Road Surface Condition Classification

Abstract

Road surface conditions play a crucial role in ensuring transportation comfort and safety. Conventional road inspection methods that rely on manual observation are often time-consuming, expensive, and prone to subjectivity. This study proposes an automated approach to classify road surface conditions using texture-based feature extraction and machine learning algorithms. A total of 802 road images were independently collected, representing three classes: good, fair, and damaged. The images were preprocessed through resizing, grayscale conversion, Contrast Limited Adaptive Histogram Equalization (CLAHE), and pixel normalization to improve image quality. Texture features were then extracted using Gray Level Co-occurrence Matrix (GLCM), including contrast, homogeneity, energy, and correlation. The extracted features were used as input to four classification algorithms: Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Random Forest, and Naive Bayes. Experimental results show that KNN achieved the best performance with 96.27% accuracy, followed by SVM and Random Forest with comparable results. Naive Bayes performed the lowest due to its detrimental assumption of feature independence. These findings demonstrate that texture-based features combined with appropriate machine learning algorithms can effectively classify road surface conditions. This approach has strong potential for implementation in automated, real-time road monitoring systems, especially on devices with limited computing resources, contributing to more efficient and objective infrastructure management.