Classificação de patologias em estruturas usando redes neurais convolucionais: diferenciação em trincas, fissuras e rachaduras
DOI:
https://doi.org/10.5902/2448190485429Keywords:
Classificação de patologias em edificaçõesAbstract
This study proposes a convolutional neural networks (CNNs) based approach for classifying cracks, fissures, and fractures through image analysis. The methodology encompasses preprocessing, data balancing, and employs the ResNet50 architecture with pooling, dropout, and regularization layers. Advanced data augmentation techniques are applied to overcome image scarcity. The model achieves approximately 96% accuracy, highlighting its effectiveness. However, areas for improvement are identified, such as continuous expansion of the realistic dataset. In summary, this study provides valuable insights into structural inspection using CNNs, with practical implications for infrastructure security and maintenance.
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