Abstract
Visual facility inspections performed manually are tasks that can be automated. Segmentation of facility image data is one of the automated methods of identifying problems in facilities. However, the machine learning methodology that is mainly used to train the segmentation model requires a large amount of training dataset. Preparing training dataset accompanies laborious manual labeling. To address this issue, we present a new method for generating synthetic data that do not require manual labeling. The method is to create photograph-style images from the BIM images; a generative adversarial network called CycleGAN is used to enable style transfer between the two different domains.
| Original language | English |
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| Title of host publication | Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020 |
| Subtitle of host publication | From Demonstration to Practical Use - To New Stage of Construction Robot |
| Publisher | International Association on Automation and Robotics in Construction (IAARC) |
| Pages | 334-338 |
| Number of pages | 5 |
| ISBN (Electronic) | 9789529436347 |
| Publication status | Published - 2020 |
| Event | 37th International Symposium on Automation and Robotics in Construction: From Demonstration to Practical Use - To New Stage of Construction Robot, ISARC 2020 - Kitakyushu, Online, Japan Duration: 2020 Oct 27 → 2020 Oct 28 |
Publication series
| Name | Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot |
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Conference
| Conference | 37th International Symposium on Automation and Robotics in Construction: From Demonstration to Practical Use - To New Stage of Construction Robot, ISARC 2020 |
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| Country/Territory | Japan |
| City | Kitakyushu, Online |
| Period | 20/10/27 → 20/10/28 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (No. 2018R1A2B2008600) and the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20SMIP-A156488-01).
Publisher Copyright:
© 2020 Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot. All rights reserved.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Civil and Structural Engineering
- Human-Computer Interaction
- Geotechnical Engineering and Engineering Geology