Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

• URL: http://arxiv.org/abs/2606.25989v1
• Date: Wed, 24 Jun 2026 15:59:48 GMT
• Title: Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery
• Abstract summary: We present a taxonomy-aware deep learning framework that aligns the training loss and the inference rule with the hierarchical structure of biological classification.<n>The system achieves a mean taxonomic distance of 1.581, within 3% of the 1st-place solution.
• Score: 1.2642567066850365
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Existing approaches struggle with severe domain shift across collection platforms, fine-grained visual similarity between closely related species, and uneven annotation granularity, where many specimens can only be identified to genus or a coarser taxonomic rank. We present a taxonomy-aware deep learning framework that aligns both the training loss and the inference rule with the hierarchical structure of biological classification, combining a taxonomy-weighted loss, minimum-risk Bayesian inference, multi-scale feature encoding, and independent per-rank classification heads. Evaluated on the FathomNet 2025 dataset1 (79 marine classes across seven taxonomic ranks), the system achieves a mean taxonomic distance of 1.581, within 3% of the 1st-place solution (1.535), with the largest gains from metric-aligned inference and simple, decoupled components that generalize better than learned dependencies under distribution shift.

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