The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment
2017 14th Conference on Computer and Robot Vision (CRV) (2017)
Edmonton, AB, Canada
May 16, 2017 to May 19, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CRV.2017.46
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.
aquaculture, computer vision, feedforward neural nets, image annotation, image segmentation
A. Galloway, G. W. Taylor, A. Ramsay and M. Moussa, "The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment," 2017 14th Conference on Computer and Robot Vision (CRV), Edmonton, AB, Canada, 2018, pp. 361-366.