Modelo basado en YOLOv8 para la detección automática de daños en tejados residenciales

  • Alisson Souza Silva Federal University of Bahia
  • Arthur Rios de Azevedo Federal University of Bahia
  • Fernando Humberto de Almeida Moraes Neto Federal University of Bahia
  • Paulo Henrique Ferreira da Silva Federal University of Bahia
DOI: https://doi.org/10.21041/ra.v15i1.783
Keywords: maintenance management, machine learning, You Only Look Once (YOLO), roof inspection, rooftop assessment

Abstract

This study developed an automated image recognition model for inspecting residential roofs using the YOLOv8 architecture to identify three types of damage. The methodology involved images from 167 buildings captured by drones and annotated in CVAT, which were used to train and test the model. YOLOv8 was applied for anomaly detection and classification, achieving 79% precision. The limitations were the small dataset and the limited variety of capture angles. The originality of the work lies in the innovative use of YOLOv8 for roof inspection. Future research will focus on developing the YOLOv9 and YOLOv10 architectures and expanding the dataset and damage classes.

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References

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Published
2025-01-01
How to Cite
Silva, A. S., Arthur Rios de Azevedo, Fernando Humberto de Almeida Moraes Neto, & Paulo Henrique Ferreira da Silva. (2025). Modelo basado en YOLOv8 para la detección automática de daños en tejados residenciales. Revista ALCONPAT, 15(1), 50 -. https://doi.org/10.21041/ra.v15i1.783
Issue
Vol. 15 No. 1 (2025): Volume 15, Issue 1 (2025)
Section
Study Case

Copyright (c) 2025 Silva, A. S., Azevedo, A. R., Neto, F. H. A. M., Ferreira, P. H.

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