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20202026
most citedPoint Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery

27 citations · 32 across the 13 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

Why Domain Matters: Domain-Aware Benchmarking of Underwater Object Detection and Annotation Quality

Melanie Wille, Dimity Miller, Tobias Fischer +1

Underwater object detection is strongly affected by domain shift, where performance can vary significantly across different locations, habitats, and deployment conditions. However,…

cs.CV2026

Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection

Melanie Wille, Dimity Miller, Tobias Fischer +1

Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in underwater environments. Existing benchmarks test…

cs.CV2025

Are All Marine Species Created Equal? Performance Disparities in Underwater Object Detection

Melanie Wille, Tobias Fischer, Scarlett Raine

Underwater object detection is critical for monitoring marine ecosystems but poses unique challenges, including degraded image quality, imbalanced class distribution, and distinct…

cs.CV2025

AI-driven Dispensing of Coral Reseeding Devices for Broad-scale Restoration of the Great Barrier Reef

Scarlett Raine, Emilio Olivastri, Benjamin Moshirian +1

Coral reefs are on the brink of collapse, with climate change, ocean acidification, and pollution leading to a projected 70-90% loss of coral species within the next decade. Reef r…

cs.CV2024★ 1 cited

Reducing Label Dependency for Underwater Scene Understanding: A Survey of Datasets, Techniques and Applications

Scarlett Raine, Frederic Maire, Niko Suenderhauf +1

Underwater surveys provide long-term data for informing management strategies, monitoring coral reef health, and estimating blue carbon stocks. Advances in broad-scale survey metho…

cs.CV2024

Human-in-the-Loop Segmentation of Multi-species Coral Imagery

Scarlett Raine, Ross Marchant, Brano Kusy +3

Marine surveys by robotic underwater and surface vehicles result in substantial quantities of coral reef imagery, however labeling these images is expensive and time-consuming for…

cs.CV2023★ 1 cited

Image Labels Are All You Need for Coarse Seagrass Segmentation

Scarlett Raine, Ross Marchant, Brano Kusy +2

Seagrass meadows serve as critical carbon sinks, but estimating the amount of carbon they store requires knowledge of the seagrass species present. Underwater and surface vehicles…

cs.CV2022★ 27 cited

Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery

Scarlett Raine, Ross Marchant, Brano Kusy +2

Monitoring coral reefs using underwater vehicles increases the range of marine surveys and availability of historical ecological data by collecting significant quantities of images…

cs.CV2021

DeepSeagrass Dataset

Scarlett Raine, Ross Marchant, Peyman Moghadam +3

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. The images…

cs.CV2020

Multi-species Seagrass Detection and Classification from Underwater Images

Scarlett Raine, Ross Marchant, Peyman Moghadam +3

Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images. Manual review of these images to extract ecologi…