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DOI:
https://doi.org/10.46421/sbtic.v5i00.7510Palabras clave:
YOLO, Aprendizado profundo, Gestão da manutenção, Inspeção automatizada, Inteligência ArtificialResumen
Automatic detection of roof defects using drones faces challenges related to accuracy, computational efficiency, and feasibility of implementation in embedded systems. In this sense, with the advancement of Deep Learning architectures, such as YOLO, it has become possible to perform building inspections faster and more accurately, reducing costs and risks associated with traditional methods. This work aimed to evaluate the performance of different versions of YOLO (v5 to v11) in detecting roof defects, comparing accuracy, mAP50, and inference time metrics to identify the most suitable model for real-time applications. In this context, 7,210 images captured by drones were used, preprocessed, and divided into training, validation, and test sets. Six YOLO models were trained and evaluated, with hyperparameter adjustments to ensure comparability. YOLOv9 stood out with the highest accuracy (66.6%) and mAP50 (53.9%), while YOLOv5 presented the fastest inference time (6.6 ms). The study demonstrates that YOLOv8 is the most viable model for embedded device deployment, offering an ideal balance between performance and computational efficiency for real-time building inspections in support of maintenance management.
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Derechos de autor 2025 SIMPOSIO BRASILEÑO SOBRE TECNOLOGÍAS DE LA INFORMACIÓN Y LA COMUNICACIÓN EN LA CONSTRUCCIÓN
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