| Autor: |
Lu Han, JiPing Zhai, Zhibin Yu, Bing Zheng |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Frontiers in Marine Science, Vol 10 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2296-7745 |
| DOI: |
10.3389/fmars.2023.1151112 |
| Popis: |
The current data-driven underwater object detection methods have significantly progressed. However, there are millions of marine creatures in the oceans, and collecting a corresponding database for each species for similar tasks (such as object detection)is expensive. Besides, marine environments are more complex than in-air cases. Water quality, illuminations, and seafloor topography may lead to domain shifting with visual instability features of underwater objects. To tackle these problems, we propose a few-shot adaptive object detection framework with a novel two-stage training approach and a lightweight feature correction module to accommodate both image-level and instance-level domain shifting on multiple datasets. Our method can be trained in a source domain and quickly adapt to an unfamiliar target domain with only a few labeled samples. Extensive experimental results have demonstrated the knowledge transfer capability of the proposed method in detecting two similar marine species. The code will be available at: https://github.com/roadhan/FSCW |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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