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http://dx.doi.org/10.12652/Ksce.2022.42.1.0117

Development of Open Set Recognition-based Multiple Damage Recognition Model for Bridge Structure Damage Detection  

Kim, Young-Nam (Department of Electrical and Computer Engineering, Sungkyunkwan University, Advanced Institute of Convergence Technology)
Cho, Jun-Sang (Korea Expressway Corporation)
Kim, Jun-Kyeong (Advanced Institute of Convergence Technology)
Kim, Moon-Hyun (Sungkyunkwan University)
Kim, Jin-Pyung (Advanced Institute of Convergence Technology)
Publication Information
KSCE Journal of Civil and Environmental Engineering Research / v.42, no.1, 2022 , pp. 117-126 More about this Journal
Abstract
Currently, the number of bridge structures in Korea is continuously increasing and enlarged, and the number of old bridges that have been in service for more than 30 years is also steadily increasing. Bridge aging is being treated as a serious social problem not only in Korea but also around the world, and the existing manpower-centered inspection method is revealing its limitations. Recently, various bridge damage detection studies using deep learning-based image processing algorithms have been conducted, but due to the limitations of the bridge damage data set, most of the bridge damage detection studies are mainly limited to one type of crack, which is also based on a close set classification model. As a detection method, when applied to an actual bridge image, a serious misrecognition problem may occur due to input images of an unknown class such as a background or other objects. In this study, five types of bridge damage including crack were defined and a data set was built, trained as a deep learning model, and an open set recognition-based bridge multiple damage recognition model applied with OpenMax algorithm was constructed. And after performing classification and recognition performance evaluation on the open set including untrained images, the results were analyzed.
Keywords
Bridge; Bridge damage type; Open set recognition; OpenMax;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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