# The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment

arXiv: https://arxiv.org/abs/1702.05564 · HTML: https://mlsanity.com/paper/1702.05564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2017.

## Built on by (most cited first, 4 of 4)

- [What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2306.15521/) (2023)
- [BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity](https://mlsanity.com/paper/2406.12723/) (2024)
- [Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition](https://mlsanity.com/paper/2309.14183/) (2023)
- [Semantic Segmentation for Fully Automated Macrofouling Analysis on Coatings after Field Exposure](https://mlsanity.com/paper/2211.11607/) (2022)

## Abstract (arXiv)

An original dataset for semantic segmentation, Ciona17, is introduced, which to the best of the authors' knowledge, is the first dataset of its kind with pixel-level annotations pertaining to invasive species in a marine environment. Diverse outdoor illumination, a range of object shapes, colour, and severe occlusion provide a significant real world challenge for the computer vision community. An accompanying ground-truthing tool for superpixel labeling, Truth and Crop, is also introduced. Finally, we provide a baseline using a variant of Fully Convolutional Networks, and report results in terms of the standard mean intersection over union (mIoU) metric.