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Found 7 papers, 3 papers with code
Human-in-the-Loop Segmentation of Multi-species Coral Imagery
For extremely sparsely labeled images, we propose a labeling regime based on human-in-the-loop principles, resulting in significant improvement in annotation efficiency: If only 5 point labels per image are available, our proposed human-in-the-loop approach improves on the state-of-the-art by 17. 3% for pixel accuracy and 22. 6% for mIoU; and by 10. 6% and 19. 1% when 10 point labels per image are available.
Image Labels Are All You Need for Coarse Seagrass Segmentation
Our method outperforms previous approaches that require patch-level labels on the multi-species 'DeepSeagrass' dataset by 6. 8% (absolute) for the class-weighted F1 score, and by 12. 1% (absolute) for the seagrass presence/absence F1 score on the 'Global Wetlands' dataset.
A Real-time Edge-AI System for Reef Surveys
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels.
Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery
We propose a point label aware method for propagating labels within superpixel regions to obtain augmented ground truth for training a semantic segmentation model.
The CSIRO Crown-of-Thorn Starfish Detection Dataset
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attempt to manage COTS populations to ecologically sustainable levels.
DeepSeagrass Dataset
We introduce a dataset of seagrass images collected by a biologist snorkelling in Moreton Bay, Queensland, Australia, as described in our publication: arXiv:2009. 09924.
Multi-species Seagrass Detection and Classification from Underwater Images
Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images.
