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

Source: [The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment](<https://www.sota2.com/research/paper/68fa63211e209131ce9fd195>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:34:16.778Z

Authors: Angus Galloway, Graham W. Taylor, Aaron Ramsay, Medhat Moussa

Published: 2017-02-18T03:40:33.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1702.05564>)

[PDF](<https://arxiv.org/pdf/1702.05564.pdf>)