YOLOv8-based model for automatic detection of residential roof damage.
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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Copyright (c) 2025 Silva, A. S., Azevedo, A. R., Neto, F. H. A. M., Ferreira, P. H.
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