Defect detection using YOLOv8 for determining the condition of asphalt pavements

  • Átila Marconcine de Souza UEL
  • Carlos Eduardo de Oliveira Universidade Estadual de Londrina
  • Pedro Henrique Bruder Decker Universidade Estadual de Londrina
  • Ana Lídia da Silva Cascales Corrêa Universidade Estadual de Londrina
  • Giorgie Eduardo Rodrigues Amorim Universidade Estadual de Londrina
  • Heliana Barbosa Fontenele Universidade Estadual de Londrina
DOI: https://doi.org/10.21041/ra.v15i1.781
Keywords: automation, algorithm, image, computer vision, object detection

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

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Published
2025-01-01
How to Cite
Marconcine de Souza, Átila, Eduardo de Oliveira, C., Henrique Bruder Decker, P., da Silva Cascales Corrêa, A. L., Eduardo Rodrigues Amorim, G., & Barbosa Fontenele, H. (2025). Defect detection using YOLOv8 for determining the condition of asphalt pavements. Revista ALCONPAT, 15(1), 79 - 91. https://doi.org/10.21041/ra.v15i1.781
Issue
Vol. 15 No. 1 (2025): Volume 15, Issue 1 (2025)
Section
Study Case

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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