Synthetic‐to‐realistic domain adaptation for cold‐start of rail inspection systems
| Autor(en): |
Qilong Huang
(Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing China)
Jianzhu Wang (Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing China) Yixiao Song (Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing China) Wenkai Cui (Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing China) Hailang Li (Infrastructure Inspection Research Institute, China Academy of Railway China Academy of Railway Sciences LTD Beijing China) Shengchun Wang (Infrastructure Inspection Research Institute, China Academy of Railway China Academy of Railway Sciences LTD Beijing China) Peng Dai (Infrastructure Inspection Research Institute, China Academy of Railway China Academy of Railway Sciences LTD Beijing China) Xinxin Zhao (Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing China) Qingyong Li (Key Laboratory of Big Data & Artificial Intelligence in Transportation (Beijing Jiaotong University) Ministry of Education Beijing China) |
|---|---|
| Medium: | Fachartikel |
| Sprache(n): | Englisch |
| Veröffentlicht in: | Computer-Aided Civil and Infrastructure Engineering, August 2023, n. 3, v. 39 |
| Seite(n): | 424-437 |
| DOI: | 10.1111/mice.13087 |
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Datenseite - Reference-ID
10735018 - Veröffentlicht am:
03.09.2023 - Geändert am:
10.02.2024
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