TL;DR
This paper introduces DeepFish, a large-scale, realistic underwater fish habitat dataset with diverse labels for multiple computer vision tasks, aiming to advance underwater visual analysis methods.
Contribution
It provides a comprehensive, multi-label dataset for fish detection, localization, and sizing in complex habitats, filling a gap in existing underwater image datasets.
Findings
Pre-trained models perform reasonably but still have room for improvement.
The dataset reveals significant variability in underwater environments.
Benchmark results highlight challenges in current methods.
Abstract
Visual analysis of complex fish habitats is an important step towards sustainable fisheries for human consumption and environmental protection. Deep Learning methods have shown great promise for scene analysis when trained on large-scale datasets. However, current datasets for fish analysis tend to focus on the classification task within constrained, plain environments which do not capture the complexity of underwater fish habitats. To address this limitation, we present DeepFish as a benchmark suite with a large-scale dataset to train and test methods for several computer vision tasks. The dataset consists of approximately 40 thousand images collected underwater from 20 \green{habitats in the} marine-environments of tropical Australia. The dataset originally contained only classification labels. Thus, we collected point-level and segmentation labels to have a more comprehensive fish…
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