Enhancement of Multi-Class Structural Defect Recognition Using Generative Adversarial Network
- Title
- Enhancement of Multi-Class Structural Defect Recognition Using Generative Adversarial Network
- Author
- 태성호
- Keywords
- generative adversarial network; data augmentation; defect recognition; deep learning; convolutional neural network
- Issue Date
- 2021-11
- Publisher
- MDPI
- Citation
- SUSTAINABILITY; NOV 2021, 13 22, p12682 13p.
- Abstract
- Recently, in the building and infrastructure fields, studies on defect detection methods
using deep learning have been widely implemented. For robust automatic recognition of defects
in buildings, a sufficiently large training dataset is required for the target defects. However, it is
challenging to collect sufficient data from degrading building structures. To address the data shortage
and imbalance problem, in this study, a data augmentation method was developed using a generative
adversarial network (GAN). To confirm the effect of data augmentation in the defect dataset of old
structures, two scenarios were compared and experiments were conducted. As a result, in the models
that applied the GAN-based data augmentation experimentally, the average performance increased
by approximately 0.16 compared to the model trained using a small dataset. Based on the results of
the experiments, the GAN-based data augmentation strategy is expected to be a reliable alternative
to complement defect datasets with an unbalanced number of objects.
- URI
- https://www.proquest.com/docview/2602263594?accountid=11283https://repository.hanyang.ac.kr/handle/20.500.11754/169819
- ISSN
- 20711050
- DOI
- 10.3390/su132212682
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ARCHITECTURE(건축학부) > Articles
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