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http://dx.doi.org/10.14190/JRCR.2021.9.3.303

Crack Detection on Bridge Deck Using Generative Adversarial Networks and Deep Learning  

Ji, Bongjun (Department of Industrial and Management Engineering, Pohang University of Science and Technology)
Publication Information
Journal of the Korean Recycled Construction Resources Institute / v.9, no.3, 2021 , pp. 303-310 More about this Journal
Abstract
Cracks in bridges are important factors that indicate the condition of bridges and should be monitored periodically. However, a visual inspection conducted by a human expert has problems in cost, time, and reliability. Therefore, in recent years, researches to apply a deep learning model are started to be conducted. Deep learning requires sufficient data on the situations to be predicted, but bridge crack data is relatively difficult to obtain. In particular, it is difficult to collect a large amount of crack data in a specific situation because the shape of bridge cracks may vary depending on the bridge's design, location, and construction method. This study developed a crack detection model that generates and trains insufficient crack data through a Generative Adversarial Network. GAN successfully generated data statistically similar to the given crack data, and accordingly, crack detection was possible with about 3% higher accuracy when using the generated image than when the generated image was not used. This approach is expected to effectively improve the performance of the detection model as it is applied when crack detection on bridges is required, though there is not enough data, also when there is relatively little or much data f or one class.
Keywords
Deep learning; Image generative model; Crack detection; Bridge deck monitoring; Concrete bridge;
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