| Title |
Development of Open Set Recognition-based Multiple Damage Recognition Model for Bridge Structure Damage Detection |
| Authors |
김영남(Kim, Young-Nam) ; 조준상(Cho, Jun-Sang) ; 김준경(Kim, Jun-Kyeong) ; 김문현(Kim, Moon-Hyun) ; 김진평(Kim, Jin-Pyung) |
| DOI |
https://doi.org/10.12652/Ksce.2022.42.1.0117 |
| Keywords |
교량; 교량 손상유형; Open set 인식; OpenMax Bridge; Bridge damage type; Open set recognition; OpenMax |
| 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. |