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

이고은; 유영수; 하대목; 구본상; 이관훈

doi:10.13161/kibim.2021.11.3.022

Semantic Segmentation 기반 딥러닝을 활용한 건축 Building Information Modeling 부재 분류성능 개선 방안
A Proposal of Deep Learning Based Semantic Segmentation to Improve Performance of Building Information Models Classification 원문보기

Journal of KIBIM = 한국BIM학회논문집, v.11 no.3, 2021년, pp.22 - 33

이고은 (서울과학기술대학교 건설시스템공학과) , 유영수 (서울과학기술대학교 건설시스템공학과) , 하대목 (서울과학기술대학교 건설시스템공학과) , 구본상 (서울과학기술대학교 건설시스템공학과) , 이관훈 (고려대학교 컴퓨터학과)

Abstract ▼ AI-Helper

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.

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표/그림 (16)

그림 Figure 1. Structural comparison between basic CRF and fully connected CRF (Krähenbühl and Koltun, 2011)
그림 Figure 2. CRF-RNN architecture (Zheng et al., 2015)
그림 Figure 3. Research flow chart
그림 Figure 4. Deep network architecture for MVCNN
그림 Figure 5. IfcWindow semantic segmentation using CRF-RNN
표 Table 1. Status of collected BIM elements
표 Table 2. 12 directions images for MVCNN training
그림 Figure 6. Deep network architecture for CRF-RNN + MVCNN
표 Table 3. Elements divided into training and test set (7:3)
그림 Figure 7. Precision-recall curve for MVCNN
그림 Figure 8. Precision-recall curve for CRF-RNN + MVCNN
표 Table 4. Validation results for MVCNN
표 Table 5. MVCNN classification results (confusion matrix)
표 Table 6. Validation results for CRF-RNN + MVCNN
표 Table 7. CRF-RNN + MVCNN classification results (confusion matrix)
표 Table 8. Delta values between MVCNN and CRF-RNN + MVCNN

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