English(EN) CFC26: Building Evaluations for Deployment in Sonar-Based Fish Counting

新数据集和指标旨在提高现实世界鱼类计数准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

研究人员推出了 Caltech Fish Counting 2026 (CFC26)，这是一个新的数据集和评估框架，旨在更好地反映声纳鱼类计数在现实世界条件下的情况。该数据集扩展了 CFC22，纳入了来自阿拉斯加到北加州九个太平洋鲑鱼河流系统中十一个部署点的数据。CFC26 还引入了两个新颖的评估指标，nMNE 和 nMANE，旨在捕捉传统帧级检测指标可能忽略的操作工作流程错误。这些新指标已经揭示了之前未检测到的上游与下游鱼类计数方向性偏差，这可能具有重大的生态意义。 AI

影响改进了生态监测和保护工作的 AI 模型评估。

排序理由该集群描述了一个特定研究问题（鱼类计数）的新数据集和评估指标，已在 arXiv 上发布。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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报道来源 [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 · 2026-10-19 04:00

CFC26：为基于声纳的鱼类计数部署构建评估

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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CFC26：为基于声纳的鱼类计数部署构建评估

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