A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Semantic Segmentation of Synthetic Images into Building Components for Automated Quality Assurance
Quality assurance (QA) plays an essential role in the construction project life cycle. During the construction phase, discrepancies between as-built structures and as-designed models can lead to schedule delays and cost overruns. Currently, QA for buildings is primarily conducted by inspectors physically touring the building to visually inspect and manually measure discrepancies between the design model and the finished structure. This manual approach is time-consuming, costly, and error prone. In this study, we proposed a vision-based approach toward automated QA using images collected via virtual cameras in a game engine. Specifically, our approach aimed to address the problem of the lack of large-scale labeled open datasets for training reliable machine learning models for the task of semantic segmentation of building components (i.e., labeling of each pixel to a specific class of building component). The approach leveraged Building Information Modeling (BIM) authoring tools and a game engine to automatically generate images of virtual buildings with pixel-wise labels. Given the labeled images, a convolutional neural network (CNN)-based model can be implemented for accurate segmentation of the images. To validate the approach, we used one building information model as the testbed. In total, 20,700 images (18,000 for training, 2200 for validation and 500 for testing) were generated from the BIM. Performance of the CNN segmentation model was measured by the mean Intersection over Union (MIoU), which achieved 0.89. The result is significant since it rivals the current state-of-the-art from the Architecture Engineering and Construction (AEC) domain. The approach proposed in this study lays a concrete step toward automated QA, where inspectors can leverage the trained CNN model to automatically label images collected onsite during or after construction to avoid labor-intensive manual inspections.
Semantic Segmentation of Synthetic Images into Building Components for Automated Quality Assurance
Quality assurance (QA) plays an essential role in the construction project life cycle. During the construction phase, discrepancies between as-built structures and as-designed models can lead to schedule delays and cost overruns. Currently, QA for buildings is primarily conducted by inspectors physically touring the building to visually inspect and manually measure discrepancies between the design model and the finished structure. This manual approach is time-consuming, costly, and error prone. In this study, we proposed a vision-based approach toward automated QA using images collected via virtual cameras in a game engine. Specifically, our approach aimed to address the problem of the lack of large-scale labeled open datasets for training reliable machine learning models for the task of semantic segmentation of building components (i.e., labeling of each pixel to a specific class of building component). The approach leveraged Building Information Modeling (BIM) authoring tools and a game engine to automatically generate images of virtual buildings with pixel-wise labels. Given the labeled images, a convolutional neural network (CNN)-based model can be implemented for accurate segmentation of the images. To validate the approach, we used one building information model as the testbed. In total, 20,700 images (18,000 for training, 2200 for validation and 500 for testing) were generated from the BIM. Performance of the CNN segmentation model was measured by the mean Intersection over Union (MIoU), which achieved 0.89. The result is significant since it rivals the current state-of-the-art from the Architecture Engineering and Construction (AEC) domain. The approach proposed in this study lays a concrete step toward automated QA, where inspectors can leverage the trained CNN model to automatically label images collected onsite during or after construction to avoid labor-intensive manual inspections.
Semantic Segmentation of Synthetic Images into Building Components for Automated Quality Assurance
Lecture Notes in Civil Engineering
Gupta, Rishi (editor) / Sun, Min (editor) / Brzev, Svetlana (editor) / Alam, M. Shahria (editor) / Ng, Kelvin Tsun Wai (editor) / Li, Jianbing (editor) / El Damatty, Ashraf (editor) / Lim, Clark (editor) / Zhang, H. X. (author) / Huang, L. (author)
Canadian Society of Civil Engineering Annual Conference ; 2022 ; Whistler, BC, BC, Canada
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022 ; Chapter: 14 ; 215-228
2023-08-17
14 pages
Article/Chapter (Book)
Electronic Resource
English
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