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New dataset and metrics aim to improve real-world fish counting accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Caltech Fish Counting 2026 (CFC26), a new dataset and evaluation framework designed to better reflect real-world conditions for sonar-based fish counting. The dataset expands upon CFC22, incorporating data from eleven deployment locations across nine Pacific salmon river systems, from Alaska to Northern California. CFC26 also introduces two novel evaluation metrics, nMNE and nMANE, which are intended to capture operational workflow errors that traditional frame-level detection metrics might miss. These new metrics have already revealed previously undetected directional biases in upstream versus downstream fish counts, which could have significant ecological implications. AI

IMPACT Improves AI model evaluation for ecological monitoring and conservation efforts.

RANK_REASON The cluster describes a new dataset and evaluation metrics for a specific research problem (fish counting), published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset and metrics aim to improve real-world fish counting accuracy

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The cluster describes a new dataset and evaluation metrics for a specific research problem (fish counting), published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Madison Van Horn, Suzanne Stathatos, Sevan Brodjian, Justin Kay, Kai Van Brunt, Erik Young, Sara Beery, Pietro Perona, Michael Hobley ·

    CFC26: Building Evaluations for Deployment in Sonar-Based Fish Counting

    arXiv:2610.22549v1 Announce Type: new Abstract: Accurate counts of fish populations are essential for monitoring ecosystems and making decisions for conservation, harvest limits, and river management. In many field deployments, these counts are obtained from sonar videos that exp…

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