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Raveling is a widely encountered defect found in asphalt pavements. Raveling deteriorates riding safety and accelerates the development of other pavement defects. Therefore, timely and accurate classification of raveling severity is required to establish cost-effective pavement maintenance plans. This paper compares the performances of the Histogram-Based Gradient Boosting Classification Machine (HGBCM), Random Forest (RF), and Deep Convolutional Neural Network (DCNN) in recognizing raveling severity. Texture analysis based on the Gray-Level Co-Occurrence Matrix and statistical measurements of color channels is used for feature extraction. An image dataset including 3600 samples and three categories (non-raveling, minor raveling, and severe raveling) has been collected to train and verify the proposed methods. Experimental results supported by statistical hypothesis tests point out that HGBCM has achieved an outstanding performance with an accuracy rate > 0.96, F1 score > 0.94, and Cohen's Kappa coefficient > 0.92 for all class labels. • Propose computer vision approaches for classifying pavement raveling severity. • Employ histogram-based gradient boosting machine. • Image texture analysis is used for feature extraction. • Random forest and convolutional neural network are benchmark models. • The gradient boosting machine achieves an accuracy rate > 0.96. [ABSTRACT FROM AUTHOR]