arXiv 2510.171792025-10-20cs.CVcs.AI

浮游生物识别的分布外检测基准测试：海洋生态监测中先进方法的系统评估

Benchmarking Out-of-Distribution Detection for Plankton Recognition: A Systematic Evaluation of Advanced Methods in Marine Ecological Monitoring

Beijing Normal University（北京师范大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）

Yingzi Han, Jiakai He, Chuanlong Xie, Jianping Li

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AI总结：

本文基于 DYB-PlanktonNet 构建多种分布偏移基准，系统评估22种浮游生物 OoD 检测方法，发现 ViM 在 Far-OoD 场景中表现最优。

摘要：

由于训练数据与测试数据之间存在分布偏移（Out-of-Distribution，OoD），自动化浮游生物识别模型在实际部署过程中面临重大挑战。这源于浮游生物复杂的形态、巨大的物种多样性以及新物种的不断发现，从而导致推理过程中出现不可预测的错误。尽管近年来 OoD 检测方法发展迅速，但浮游生物识别领域仍缺乏对最新计算机视觉进展的系统整合，也缺乏用于大规模评估的统一基准。为解决这一问题，本文基于 DYB-PlanktonNet 数据集精心设计了一系列模拟各种分布偏移场景的 OoD 基准，并系统评估了二十二种 OoD 检测方法。大量实验结果表明，ViM 方法在所构建的基准上显著优于其他方法，尤其在 Far-OoD 场景中表现突出，在关键指标上取得大幅提升。这一全面评估不仅为自动化浮游生物识别中的算法选择提供了可靠参考，也为未来浮游生物 OoD 检测研究奠定了坚实基础。据我们所知，本研究首次对浮游生物识别中的分布外数据检测方法进行大规模、系统性评估与分析。代码可在 https://github.com/BlackJack0083/PlanktonOoD 获取。

英文摘要：

Automated plankton recognition models face significant challenges during real-world deployment due to distribution shifts (Out-of-Distribution, OoD) between training and test data. This stems from plankton's complex morphologies, vast species diversity, and the continuous discovery of novel species, which leads to unpredictable errors during inference. Despite rapid advancements in OoD detection methods in recent years, the field of plankton recognition still lacks a systematic integration of the latest computer vision developments and a unified benchmark for large-scale evaluation. To address this, this paper meticulously designed a series of OoD benchmarks simulating various distribution shift scenarios based on the DYB-PlanktonNet dataset \cite{875n-f104-21}, and systematically evaluated twenty-two OoD detection methods. Extensive experimental results demonstrate that the ViM \cite{wang2022vim} method significantly outperforms other approaches in our constructed benchmarks, particularly excelling in Far-OoD scenarios with substantial improvements in key metrics. This comprehensive evaluation not only provides a reliable reference for algorithm selection in automated plankton recognition but also lays a solid foundation for future research in plankton OoD detection. To our knowledge, this study marks the first large-scale, systematic evaluation and analysis of Out-of-Distribution data detection methods in plankton recognition. Code is available at https://github.com/BlackJack0083/PlanktonOoD.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。