Semantic Segmentation 기반 딥러닝을 활용한 건축 Building Information Modeling 부재 분류성능 개선 방안

• 영문명: A Proposal of Deep Learning Based Semantic Segmentation to Improve Performance of Building Information Models Classification
• 발행기관: 한국BIM학회
• 저자명: 이고은(Lee, Ko-Eun) 유영수(Yu, Young-Su) 하대목(Ha, Dae-Mok) 구본상(Koo, Bon-Sang) 이관훈(Lee, Kwan-Hoon)
• 간행물 정보: 『KIBIM Magazine』11권 3호, 22~33쪽, 전체 12쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2021.08.31

영문 초록

In order to maximize the use of BIM, all data related to individual elements in the model must be correctly assigned, and it is essential to check whether it corresponds to the IFC entity classification. However, as the BIM modeling process is performed by a large number of participants, it is difficult to achieve complete integrity. To solve this problem, studies on semantic integrity verification are being conducted to examine whether elements are correctly classified or IFC mapped in the BIM model by applying an artificial intelligence algorithm to the 2D image of each element. Existing studies had a limitation in that they could not correctly classify some elements even though the geometrical differences in the images were clear. This was found to be due to the fact that the geometrical characteristics were not properly reflected in the learning process because the range of the region to be learned in the image was not clearly defined. In this study, the CRF-RNN-based semantic segmentation was applied to increase the clarity of element region within each image, and then applied to the MVCNN algorithm to improve the classification performance. As a result of applying semantic segmentation in the MVCNN learning process to 889 data composed of a total of 8 BIM element types, the classification accuracy was found to be 0.92, which is improved by 0.06 compared to the conventional MVCNN.

목차

1. 서론

2. 연구 배경

3. 연구 방법

4. 연구 결과 및 비교

5. 결론

감사의 글

References

키워드

IFC 시멘틱 세그멘테이션 MVCNN CRF-RNN BIM IFC Semantic Segmentation Multi-View CNN CRF-RNN