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新性识别:通过开放式识别对鱼类进行分类
Manuel Córdova1, Ricardo da Silva Torres2, Aloysius van Helmond3
1Agricultural Biosystems Engineering Group, Wageningen University and Research, 6700 AA Wageningen, The Netherlands.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
像OSN和PISVM这样的开放式识别方法可以有效地识别已知的和未知的鱼类物种,这对于可持续的海洋资源管理和自动注册至关重要. 这些计算机视觉方法克服了封闭式系统的局限性.
科学领域:
- 计算机视觉 计算机视觉
- 海洋生物学 海洋生物学
- 机器学习 机器学习
背景情况:
- 可持续的海洋资源管理需要通过准确的物种登记来减少鱼类的排放.
- 目前用于鱼类分类的计算机视觉方法受到其封闭性质的限制,无法识别未知的物种.
- 现实世界的场景需要开放式的识别来处理在不同的环境中遇到的新鲜鱼类.
研究的目的:
- 评估用于自动化鱼类登记的开放式识别技术.
- 为了比较多重高斯原型学习 (MGPL) 与开放式近邻 (OSNN) 和包含支持向量机器 (PISVM) 的概率的性能.
主要方法:
- 利用鱼类检测和体重估计数据集,包括9个物种的2216个鱼类图像.
- 评估了三个开放式识别算法:MGPL,OSN和PISVM.
- 基于它们分类已知和未知的鱼类物种的能力来评估算法.
主要成果:
- OSNN和PISVM在识别已知和未知的鱼类物种方面表现优于MGPL.
- OSNN获得了最高的F1宏分数0.79±0.05和AUROC分数0.92±0.01.01的最高分数.
- 在F1宏中,OSN的表现优于PISVM的0.05,在AUROC中,在分类已知和未知物种方面表现优于PISVM的0.03.
结论:
- 开放式识别模型,特别是OSNN,对于自动化鱼类登记和支持可持续渔业是有效的.
- 在现实世界鱼类分类任务中,OSNN提供了一个强大的解决方案来应对未知物种的挑战.
- 该研究强调了在海洋资源管理中准确识别物种的开放式方法的重要性.
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