Defect detection using YOLOv8 for determining the condition of asphalt pavements
Abstract
This study aimed to evaluate the capacity of the YOLOv8 algorithm to detect potholes, patches, and cracks. To achieve this, a section of a highway was recorded, manually evaluated in the field, and compared with a semi-automatic evaluation based on video processing by the model. The model yielded different results from those obtained through field assessment. Although only a portion of the Maintenance Condition Index is used in the assessment, this marks the first use of an index integrated with YOLOv8. Thus, it is concluded that the model requires further improvements to become viable for definitive application.
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References
Chunlong, Z., Peile, H., Shenghuai, W., Chen, W., Hongxia, W. (2024). Pavement Defect Detection Algorithm Based on Improved YOLOv7 Complex Background. IEEE Access, 12, 32870–32880. https://doi.org/10.1109/ACCESS.2024.3370604 DOI: https://doi.org/10.1109/ACCESS.2024.3370604
Confederação Nacional dos Transportes (CNT). (2023). Pesquisa CNT de Rodovias 2023. Disponível em: https://agenciadenoticias.ms.gov.br/wp-content/uploads/2024/01/pesquisa_cnt_rodovias_2024_relatorio_gerencial.pdf. Accessed on: May 6, 2024.
Departamento Nacional de Infraestrutura de Transportes. (2024). Condições do Pavimento em março/2024. Disponível em: https://servicos.dnit.gov.br/dadosabertos/dataset/condicoes-do-pavimento/resource/030e374d-a505-4d16-a283-610a29008746. Accessed on: May 6, 2024.
Departamento Nacional de Infraestrutura de Transportes. (2024). Resolução nº 5/2022, de 27 de abril de 2022. Disponível em: https://www.gov.br/dnit/pt-br/central-de-conteudos/atos-normativos/tipo/resolucoes/resolucao-5-2022-dir-ba-080-de-29-04-2022.pdf. Accessed on: April 8, 2024.
Du, F. J., Jiao, S. J. (2022). Improvement of Lightweight Convolutional Neural Network Model Based on YOLO Algorithm and Its Research in Pavement Defect Detection. Sensors, 22(9). https://doi.org/10.3390/s22093537 DOI: https://doi.org/10.3390/s22093537
Ju, R. Y., Cai, W. (2023). Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-47460-7 DOI: https://doi.org/10.1038/s41598-023-47460-7
El Hakea, A. H., Fakhr, M. W. (2023). Recent computer vision applications for pavement distress and condition assessment. In Automation in Construction (Vol. 146). Elsevier B.V. https://doi.org/10.1016/j.autcon.2022.104664 DOI: https://doi.org/10.1016/j.autcon.2022.104664
Gonçalves, M., Marques, T., Gaspar, P. D., Soares, V. N. G. J., Caldeira, J. M. L. P. (2023). Road Pavement Damage Detection using Computer Vision Techniques: Approaches, Challenges and Opportunities. Revista de Informatica Teorica e Aplicada, 30(2), 22–35. https://doi.org/10.22456/2175-2745.129787 DOI: https://doi.org/10.22456/2175-2745.129787
Copyright (c) 2025 Souza, A.M., Oliveira, C.E., Decker, P.H.B., Amorim, G.E.R., Correa, A.L.S.C, Fontenele, H.B.
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