| Title |
Automatic Classification of Bridge Component based on Deep Learning |
| Authors |
이재혁(Lee, Jae Hyuk) ; 박정준(Park, Jeong Jun) ; 윤형철(Yoon, Hyungchul) |
| DOI |
https://doi.org/10.12652/Ksce.2020.40.2.0239 |
| Keywords |
교량 구성요소 분류;딥러닝 BIM;Bridge component classification;Deep Learning;CNN |
| Abstract |
Recently, BIM (Building Information Modeling) are widely being utilized in Construction industry. However, most structures that have been constructed in the past do not have BIM. For structures without BIM, the use of SfM (Structure from Motion) techniques in the 2D image obtained from the camera allows the generation of 3D model point cloud data and BIM to be established. However, since these generated point cloud data do not contain semantic information, it is necessary to manually classify what elements of the structure. Therefore, in this study, deep learning was applied to automate the process of classifying structural components. In the establishment of deep learning network, Inception-ResNet-v2 of CNN (Convolutional Neural Network) structure was used, and the components of bridge structure were learned through transfer learning. As a result of classifying components using the data collected to verify the developed system, the components of the bridge were classified with an accuracy of 96.13 %. |