TSSWD 🚢

Taiwan SAR-based Ship and Weather Dataset (TSSWD) is the latest SAR ship dataset combined with weather data. Link to the buoy stations map. Link to the research paper.

Here's why you should use our dataset:

• Dataset build up with buoy weather data, i.e. every image has various weather data including wave height, wind speed ... etc. So not only the dataset can be use in Ship detection, it also got the potential to do sea weather prediction through SAR images!
• We only use Sentinel-1 as our image source, no mixed-source satellite, which is more common practice in Remote Sensing Field.
• The ship size in our dataset is smaller than previous dataset, which means it's more challenging 😄

Dataset Details 📗

We now only provide Mask-RCNN-X152 and YOLOV5 model's weight due to the storage limitation.
If you need other model weight, please contact us.

Satellite	Mode	Images	Ships	Size	Resolution
Sentinel-1	IW	2904	8255	800x800	10 m
Annotation Type	Training	Validation	Testing	Weights	Dataset
COCO / VOC / YOLOV4 / YOLOV5	2324	290	290	Click Me	Click Me

Training & Testing Details 💪

The Object Detection / Instance Segmentation Models are list bellow. Bold represent Instance Segmentation.
Training and testing params set in log file.
Mask R-CNN use Detectron2 platform.
YOLOV5 and CenterMask use their official github repo.
All model trained by GTX 1060 use the ppdet platform

Models	GPU	Notes
Mask R-CNN, YOLOV5, CenterMask	Nvidia Tesla V100 32G Nvida RTX 2080Ti 12G	Provide by NTUCE-NCREE AI Research Center
Rest of them	Nvidia GTX 1060 6G	Home PC

Model	Bbox mAP	Bbox mAP50	Seg mAP	Seg mAP50	log
Mask R-CNN-r101	49.475	90.672	43.753	90.581	log
Mask R-CNN-x101	48.732	90.835	45.247	92.047	log
Mask R-CNN-x152	51.211	93.061	46.512	93.04	log
CenterMask-V99	47.024	86.21	43.726	87.736	log
CenterMask-V57	47.168	85.929	43.468	86.861	log
YOLO V5-l	54.8	94.9	x	x	log
YOLO V5-l6	55.4	93.7	x	x	log
YOLO V5-x	55.5	94.8	x	x	log
YOLO V5-x6	52.9	95.1	x	x	log
PPYOLO	49	91.9	x	x	log
HRNET	49	91	x	x	log
TOOD	42.9	79.3	x	x	log
Cascade R-CNN	50	90.5	x	x	log
Sparse R-CNN	44.9	86.3	x	x	log
Res2Net	49.6	91.1	x	x	log
FCOS	43.6	80.3	x	x	log

License & Citations

Distributed under the MIT License. See LICENSE for more information.

If our work is helpful to your research, use this bibtex to cite this repository:

@misc{TSSWD,
  title={TSSWD: Taiwan SAR-based Ship Detection and Weather Dataset},
  author={Shang-Fong, Yang and YaLun, Tsai},
  year={2023},
  publisher={Github},
  journal={GitHub repository},
  howpublished={\url{https://github.com/GMfatcat/TSSWD}}
}

use this citation if the article is published (not yet):

@article{TSSWD,
  title={TSSWD: Taiwan SAR-based Ship Detection and Weather Dataset},
  author={Shang-Fong, Yang and YaLun, Tsai},
  keywords={SAR,Ship Detection,Object Detection,Instance Segmentation,Buoy}
  year={2023},
  publisher={},
  journal={},
  volume={},
  number={},
  pages={},
}

Contact

Email - r10521801@ntu.edu.tw

Email - yalunstsai@ntu.edu.tw

LAB Website: EORSLAB