| Title |
Image-Based Automatic Bridge Component Classification Using Deep Learning |
| Authors |
조문원(Cho, Munwon) ; 이재혁(Lee, Jae Hyuk) ; 유영무(Ryu, Young-Moo) ; 박정준(Park, Jeongjun) ; 윤형철(Yoon, Hyungchul) |
| DOI |
https://doi.org/10.12652/Ksce.2021.41.6.0751 |
| Keywords |
BIM; 딥러닝; CNN; 교량 구성요소 분류 BIM; Deep learning; CNN; Bridge component classification |
| Abstract |
Most bridges in Korea are over 20 years old, and many problems linked to their deterioration are being reported. The current practice for bridge inspection mainly depends on expert evaluation, which can be subjective. Recent studies have introduced data-driven methods using building information modeling, which can be more efficient and objective, but these methods require manual procedures that consume time and money. To overcome this, this study developed an image-based automaticbridge component classification network to reduce the time and cost required for converting the visual information of bridges to a digital model. The proposed method comprises two convolutional neural networks. The first network estimates the type of the bridge based on the superstructure, and the
second network classifies the bridge components. In avalidation test, the proposed system automatically classified the components of 461 bridge images with 96.6 % of accuracy. The proposed approach is expected to contribute toward current bridge maintenance practice. |