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Found 5 papers, 4 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.
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.
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.
