Fine-Grained Ship Recognition in Complex Scenarios.
Ship recognition in remote sensing imagery is crucial for numerous applications such as monitoring maritime security, preventing illegal activities and implementing environmental protection. However, most existing research rarely focuses on both the complexity of the scene and the fine-grained recognition of targets, which limits the accuracy and applicability of ship recognition technology. To propel advancements in ship recognition methodology, we propose a new dataset named Fine-Grained Ship Recognition in Complex Scenarios (FGSRCS), which contains 17 typical categories of large and medium-sized ships from 280 widely distributed ports. For the richness of the image characteristics, the dataset images are collected from multiple satellite platforms, such as Ikonos, Jilin-1, OrbView, Pleiades and WorldView, and the time span of the data covers the past 20 years. To ensure the diversity of scenarios, the dataset mainly considers five complex scenarios, including thick clouds, mists, shadows, sea clutter and ground facilities, which helps to train and improve the algorithm applicability to practical application scenarios. Furthermore, we conduct experiments with nine state-of-the-art recognition algorithms on FGSRCS dataset, providing a benchmark for algorithm application. The research result can furnish both theoretical insights and practical guidance for the development of future ship recognition models.
The dataset is available on the following links:
Baidu Driver: https://pan.baidu.com/s/1mppz46IwIH3iJI6oeo-YQw?pwd=g1vt (extraction password: g1vt)
Google Driver: https://drive.google.com/file/d/1HHUsGGQOpJh9ozHSVfmKIuj6niUsK9HF/view?usp=drive_link
If you want use our dataset, please follows these rules:
• Use of Google Earth images must respect the "Google Earth" terms of use.
• All images and their associated annotations in FGSRCS can be used for academic purposes only, but any commercial use is prohibited.
To facilitate usage, we have also provided configuration files for MMrotate 1.0.0rc1 and FGSRCS-class files, enabling straightforward benchmarking of the dataset's performance metrics.
If you have any problem or feedback in using FGSRCS dataset, please contact me at 22S021006@stu.hit.edu.cn.