| Title: | OS17-6 An Effective Method for Minimizing Domain Gap in Sim2Real Object Recognition Using Domain Randomization |
|---|---|
| Publication: | ICAROB2023 |
| Volume: | 28 |
| Pages: | 420-424 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2023.OS17-6 |
| Author(s): | Tomohiro Ono, Akihiro Suzuki, Hakaru Tamukoh |
| Publication Date: | February 9, 2023 |
| Keywords: | Data-centric Deep Learning, Object Recognition, Sim2Real, Dataset Generation |
| Abstract: | Manual annotation is common, but problems occur, such as oversight and mislabeling via human error. These problems are known to affect the quality of datasets significantly. To resolve these problems, we propose a method to automatically generate high-quality and large datasets in a short time using a simulator. Our proposed method uses domain randomization to minimize domain gaps without faithfully reproducing real scenes. The generated dataset achieved more than 80% recognition accuracy against the real image dataset. |
| PDF File: | https://alife-robotics.co.jp/members2023/icarob/data/html/data/OS/OS17/OS17-6.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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