Deep Learning for Physical Damage Detection in Buildings: A Comparison of Transfer Learning Methods

Authors : Betül BEKTAŞ EKİCİ, Saltuk Taha USTAOĞLU

Pages : 291-299

Doi:10.55525/tjst.1291814

View : 172 | Download : 245

Publication Date : 2023-09-01

Article Type : Research Paper

Abstract :The detection of physical damage in buildings is a critical task in ensuring the safety and integrity of structures. In this study, the effectiveness of deep learning methods for detecting physical damage in buildings, specifically focusing on cracks, defects, moisture, and undamaged classes was investigated. Transfer learning methods, including VGG16, GoogLeNet, and ResNet50, were used to classify a dataset of 7200 images. The dataset was split into training, validation, and testing sets, and the performance of the models was evaluated by using metrics such as accuracy, precision, recall, and F1-score. Results show that all three models achieved 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. It is demonstrated the effectiveness of deep learning methods for physical damage detection in buildings and provides insights into the comparative performance of transfer learning methods.
Keywords : Yapısal hasar sınıflandırması, derin öğrenme, evrişimli sinir ağları, transfer öğrenme