| Popis: |
The detection of physical damage in buildings is a critical task in ensuring the safety and integrity of structures. In this study, we investigate the effectiveness of deep learning methods for detecting physical damage in buildings, specifically focusing on cracks, defect, moisture, and undamaged classes. We use transfer learning methods, including VGG16, GoogLeNet, and ResNet50, to classify a dataset of 7200 images. We split the dataset into training, validation, and testing sets, and evaluate the performance of the models using metrics such as accuracy, precision, recall, and F1-score. Our results show that all three models achieve high accuracy on the test set, with VGG16 and ResNet50 outperforming GoogLeNet. Additionally, precision, recall, and F1-score metrics indicate strong performance across all classes, with VGG16 and ResNet50 achieving particularly high scores. Our study demonstrates the effectiveness of deep learning methods for physical damage detection in buildings and provides insights into the comparative performance of transfer learning methods. |
