FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images
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Abstract (arXiv)
Ship detection using high-resolution remote sensing images is an important task, which contribute to sea surface regulation. The complex background and special visual angle make ship detection relies in high quality datasets to a certain extent. However, there is few works on giving both precise classification and accurate location of ships in existing ship detection datasets. To further promote the research of ship detection, we introduced a new fine-grained ship detection datasets, which is named as FGSD. The dataset collects high-resolution remote sensing images that containing ship samples from multiple large ports around the world. Ship samples were fine categorized and annotated with both horizontal and rotating bounding boxes. To further detailed the information of the dataset, we put forward a new representation method of ships' orientation. For future research, the dock as a new class was annotated in the dataset. Besides, rich information of images were provided in FGSD, including the source port, resolution and corresponding GoogleEarth' s resolution level of each image. As far as we know, FGSD is the most comprehensive ship detection dataset currently and it'll be available soon. Some baselines for FGSD are also provided in this paper.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 3; 2024: 0; 2025: 1; 2026: 2.
9 papers.
- 2021Object Detection in Aerial Images: A Large-Scale Benchmark and Challengescited by 202
- 2021FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagerycited by 143
- 2023Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advancescited by 20
- 2022A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillancecited by 14
- 2023Oriented object detection in optical remote sensing images using deep learning: a surveycited by 7
- 2025More Clear, More Flexible, More Precise: A Comprehensive Oriented Object Detection benchmark for UAVcited by 5
- 2023Fast Region of Interest Proposals on Maritime UAVscited by 3
- 2026MARINER: A 3E-Driven Benchmark for Fine-Grained Perception and Complex Reasoning in Open-Water Environmentscited by 0
- 2026Hierarchical Fine-Grained Aerial Object Detectioncited by 0
- 2026Hierarchical Fine-Grained Aerial Object Detection15 Jun 2026
- 2026MARINER: A 3E-Driven Benchmark for Fine-Grained Perception and Complex Reasoning in Open-Water Environments9 Apr 2026
- 2025More Clear, More Flexible, More Precise: A Comprehensive Oriented Object Detection benchmark for UAV28 Apr 2025
- 2023Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances13 Sep 2023
- 2023Oriented object detection in optical remote sensing images using deep learning: a survey21 Feb 2023
- 2023Fast Region of Interest Proposals on Maritime UAVs27 Jan 2023
- 2022A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance26 Jul 2022
- 2021FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery9 Mar 2021
- 2021Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges24 Feb 2021