Sea Situational Awareness (SeaSAw) Dataset

Parneet Kaur, Arslan Aziz, Darshan Jain, Harshil Patel, Jonathan Hirokawa, Lachlan Townsend, Christoph Reimers, Fiona Hua

CVPRW 2022 pp. 2578-2586

doi:10.1109/CVPRW56347.2022.00290 /cvprw/2022/kaur2022cvprw-sea/

Abstract

Vessels move 90% of international cargo by volume, with the marine economy contributing to 5.1% of global GDP. As one of the oldest industries, the marine industry has yet to embrace innovations in modern technology to safeguard the blue economy. Situational awareness from intelligent vessel systems can enable enhanced safety and decision-making for mariners. As the foundation for these intelligent systems, advanced perception technology requires sufficient real-world operational data to leverage recent AI technologies. In this work, we introduce the Sea Situational Awareness (SeaSAw) dataset - a novel dataset that is comprised of 1.9 million images with 14.6 million objects associated with 20.4 million attributes from 12 object classes, making it the largest maritime dataset for object detection, fine-grained classification and tracking. Furthermore, this dataset consists of 9 sources in combination with various RGB cam-eras, mounted on different moving vessels, operating in different geographic locations globally, having variations in scenario, weather and illumination conditions. This data collection took place across 4 years with rigorous efforts on data selection, annotation, management and analysis to enhance the marine perception technology.

CVPRW Semantic Scholar

Cite

Text

Kaur et al. "Sea Situational Awareness (SeaSAw) Dataset." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00290

Markdown

[Kaur et al. "Sea Situational Awareness (SeaSAw) Dataset." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/kaur2022cvprw-sea/) doi:10.1109/CVPRW56347.2022.00290

BibTeX

@inproceedings{kaur2022cvprw-sea,
  title     = {{Sea Situational Awareness (SeaSAw) Dataset}},
  author    = {Kaur, Parneet and Aziz, Arslan and Jain, Darshan and Patel, Harshil and Hirokawa, Jonathan and Townsend, Lachlan and Reimers, Christoph and Hua, Fiona},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2022},
  pages     = {2578-2586},
  doi       = {10.1109/CVPRW56347.2022.00290},
  url       = {https://mlanthology.org/cvprw/2022/kaur2022cvprw-sea/}
}