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http://dx.doi.org/10.9711/KTAJ.2020.22.5.515

Adversarial learning for underground structure concrete crack detection based on semi­supervised semantic segmentation  

Shim, Seungbo (Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology)
Choi, Sang-Il (Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology)
Kong, Suk-Min (Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology)
Lee, Seong-Won (Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology)
Publication Information
Journal of Korean Tunnelling and Underground Space Association / v.22, no.5, 2020 , pp. 515-528 More about this Journal
Abstract
Underground concrete structures are usually designed to be used for decades, but in recent years, many of them are nearing their original life expectancy. As a result, it is necessary to promptly inspect and repair the structure, since it can cause lost of fundamental functions and bring unexpected problems. Therefore, personnel-based inspections and repairs have been underway for maintenance of underground structures, but nowadays, objective inspection technologies have been actively developed through the fusion of deep learning and image process. In particular, various researches have been conducted on developing a concrete crack detection algorithm based on supervised learning. Most of these studies requires a large amount of image data, especially, label images. In order to secure those images, it takes a lot of time and labor in reality. To resolve this problem, we introduce a method to increase the accuracy of crack area detection, improved by 0.25% on average by applying adversarial learning in this paper. The adversarial learning consists of a segmentation neural network and a discriminator neural network, and it is an algorithm that improves recognition performance by generating a virtual label image in a competitive structure. In this study, an efficient deep neural network learning method was proposed using this method, and it is expected to be used for accurate crack detection in the future.
Keywords
Adversarial learning; Crack detection; Semantic segmentation; Health monitoring; Image processing;
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Times Cited By KSCI : 2  (Citation Analysis)
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