Segmentation and feature extraction-based classification of pavement damages using hybrid computer vision and machine learning approaches

Abstract Ensuring the safety, durability, and cost-effectiveness of road infrastructure maintenance requires accurate and efficient damage assessment. However, the heterogeneous nature of pavement deterioration, ranging from cracks and potholes to exposed aggregate areas, poses significant challenges for its analysis and classification. This study introduces a novel hybrid methodology that integrates advanced segmentation, feature extraction, and classification techniques to enhance the accuracy and robustness of road-damage detection. Unlike conventional approaches, the proposed method leverages Enhanced Fuzzy C-Means and vessel segmentation to achieve precise damage detection, thereby overcoming the limitations of traditional threshold-based techniques. For feature extraction, the method combines Mel-frequency cepstral coefficients and Local Binary Patterns to capture both frequency and textural characteristics and improve feature discriminability. Furthermore, Linear Discriminant Analysis optimizes dimensionality reduction, ensuring a compact yet highly informative representation of pavement conditions. The classification stage evaluates multiple approaches, including a Support Vector Machine (SVM) and Artificial Neural Network (ANN) for supervised learning, K-Means for unsupervised learning, and a Convolutional Neural Network (CNN) based on EfficientNetB7. 6464 road images were processed using the proposed methodology. The results show that SVM and ANN achieved F1-Scores above 90% owing to the quality of the extracted features. Additionally, K-Means obtained an F1-Score of 88%, outperforming EfficientNetB7, which achieved 87%, demonstrating the effectiveness of the proposed approach for segmentation and feature extraction. These findings highlight the advantages of integrating frequency-based and texture-based descriptors with advanced segmentation and classification strategies, ultimately contributing to more reliable and scalable pavement damage assessment systems.