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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.<\/jats:p>","DOI":"10.1007\/s10994-025-06803-3","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T12:59:07Z","timestamp":1750251547000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Segmentation and feature extraction-based classification of pavement damages using hybrid computer vision and machine learning approaches"],"prefix":"10.1007","volume":"114","author":[{"given":"Lizette","family":"Tello-Cifuentes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannio","family":"Marulanda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Thomson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"6803_CR1","doi-asserted-by":"publisher","first-page":"24452","DOI":"10.1109\/ACCESS.2018.2829347","volume":"6","author":"D Ai","year":"2018","unstructured":"Ai, D., Jiang, G., Siew Kei, L., & Li, C. 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The incorrect figures were the result of an oversight during the final production process. Correction to Fig. 9 The figure originally published as Fig. 9 (Mutual information for features obtained) was not the final version intended for publication. The correct figure is provided below. Correction to Fig. 10 Similarly, Fig. 10 (F-values for Feature Selection) was inadvertently replaced with an outdated version. The correct figure appears below. These corrections do not affect the results, interpretation, or conclusions reported in the article. 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