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English Information

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Authors
# Name
1 Nicole Batista(nicolesouza@alu.ufc.br)
2 Victor Oliveira(victoroliveira@det.ufc.br)
3 Thiago Souza(thiagoiachiley@ufc.br)
4 Mariana Castro(maarytdc@alu.ufc.br)
5 Danielo Gomes(danielo@ufc.br)
6 Bruno Bertoncini(bruviber@det.ufc.br)
7 Verônica Branco(veronica@det.ufc.br)

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Reference
# Reference
1 Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., and Shoaib, M. A. (2022). Crack segmentation network using additive attention gate—csn-ii. Engineering Applications of Artificial Intelligence, 114:105130.
2 Barbosa, J. C. P., Rosa, F. D., and Rieder, R. (2024). Pothole detection web app: uma abordagem para detecção de buracos em pavimentos asfálticos utilizando yolo. Revista Brasileira de Computação Aplicada, 16:1–12.
3 Khare, O. M., Gandhi, S., Rahalkar, A. M., and Mane, S. (2023). Yolov8 based visual detection of road hazards: Potholes, sewer covers, and manholes. DBLP.
4 Kumar, K. and Pande, B. P. (2022). Air pollution prediction with machine learning: a case study of indian cities. International Journal of Environmental Science and Technology, 20.
5 Liu, H., Yang, J., Miao, X., Mertz, C., and Kong, H. (2023). Crackformer network for pavement crack segmentation. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 24:9240–9252.
6 Luan, Y. C., Zhang, W. G., Shen, S. H., Wu, S. H., Ma, T., Zhou, X. Y., Mohammad, L. N., and Fu, Y. (2024). Effect of aged material properties on transverse crack performance with two-round field observations. Road Materials and Pavement Design, 25:1037–1053
7 Matarneh, S., Elghaish, F., Rahimian, F. P., Abdellatef, E., and Abrishami, S. (2024). Evaluation and optimisation of pre-trained cnn models for asphalt pavement crack detection and classification. Automation in Construction, 160:105297.
8 Ou, J., Zhang, J., Li, H., and Duan, B. (2025). An improved yolov10-based lightweight multi-scale feature fusion model for road defect detection and its applications. Advances in Engineering Software, 2028:103976.
9 Souza, M., Oliveira, C. E., and Decker, P. H. B. (2025). Defect detection using yolov8 for determining the condition of asphalt pavements. Revista ALCONPAT, 15:79–91.
10 Xiong, C., Zayed, T., and Abdelkader, E. M. (2024). A novel yolov8-gam wise-iou model for automated detection of bridge surface cracks. Construction and Building Materials, 414:135025.
11 Yeung, C. and Lam, K. (2024). Contrastive decoupling global and local features for pavement crack detection. Engineering Applications of Artificial Intelligence, 133:108632.
12 Zhang, Z., Wu, J., Song, W., Zhuang, Y., Xu, Y., Ye, X., Shi, G., and Zhang, H. (2025). Ards-yolo: Intelligent detection of asphalt road damages and evaluation of pavement condition in complex scenarios. Measurement, 242:115946.