| Title |
Automatic Crack Detection in Concrete Structures Using a VGG-T Image Classification Model |
| Authors |
백영건(Beak, Young-Gun) ; 김현승(Kim, Hyen-Seung) ; 홍영록(Hong, Rong-Lu) ; 김주형(Kim, Ju-Hyung) |
| DOI |
https://doi.org/10.5659/JAIK.2025.41.8.369 |
| Keywords |
Crack; Convolutional Neural Network (CNN); Vision Transformer |
| Abstract |
Crack detection plays a crucial role in monitoring and inspecting the condition of construction structures. Traditional Convolutional Neural
Network (CNN) methods, which focus mainly on local feature extraction, face limitations in accuracy. In contrast, Vision Transformer (ViT)
models effectively capture global features but require large-scale datasets for training. To overcome these challenges, the VGG-T Image
Classification model is proposed. This model combines the local feature extraction strength of the CNN-based VGG-16 with the global feature
learning capabilities of ViT. Incorporating transfer learning and data augmentation techniques allows effective training even with small
datasets. The model was evaluated using binary classification metrics and compared against VGG-16, VGG-19, ResNet-101, and ViT models.
Results showed an accuracy of 99.6%, demonstrating that integrating these two architectures significantly improves detection accuracy. This
advancement is expected to contribute to the development of structural safety diagnosis, automated safety maintenance, and crack detection
technologies. |