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

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

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. YOLOv...

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Bibliographic Details
Main Authors: Alisson Souza Silva, Arthur Rios de Azevedo, Fernando Humberto de Almeida Moraes Neto, Paulo Henrique Ferreira da Silva
Format: Article
Language:English
Published: Asociación Latinoamericana de Control de Calidad, Patología y Recuperación de la Construcción 2025-01-01
Series:Revista ALCONPAT
Subjects:
maintenance management
machine learning
You Only Look Once (YOLO)
roof inspection
rooftop assessment
Online Access:https://mail.revistaalconpat.org/index.php/RA/article/view/783
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Summary: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.
ISSN:2007-6835

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