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新的交互式机器学习工具用于海洋图像分析

H Poppy Clark¹, Abraham George Smith², Daniel McKay Fletcher³

¹Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, UK.

Royal Society open science

|August 19, 2024

概括

机器学习工具RootPainter快速准确地分析海洋图像,以识别海绵Mycale lingua. 这加速了生物多样性研究和对海洋生态系统的理解.

科学领域:

海洋生物学海洋生物学
图像分析图像分析
机器学习机器学习

背景情况:

海洋成像技术产生了大量的数据集,但手动分析是耗时的.
对海洋视频和图像数据的有效分析对于生态研究至关重要.
在大型数据集中识别像Mycale lingua这样的物种是一个重大挑战.

研究的目的:

为了证明开源工具RootPainter用于分析海洋图像数据集的有效性.
评估RootPainter在提取Mycale lingua.的存在和表面积数据方面的能力.
评估新的校正注释指标对模型培训和验证的影响.

主要方法:

在两个海洋图像数据集 (时间缩短和ROV视频) 上使用了交互式机器学习工具RootPainter.
开发并应用了用于客观模型培训评估的纠正注释指标.
员工转移学习以提高Mycale lingua模型的分析效率.

主要成果:

在识别Mycale lingua方面取得了很高的准确性,平均子得分为0.94 ± 0.06.
通过转移学习,分析效率比手动注释提高了6至16倍.
从两个数据集中成功提取了Mycale lingua的表面积测量.

结论:

RootPainter提供了一个快速而准确的解决方案,用于分析大型海洋图像数据集.
交互式机器学习工具可以显著提高生物多样性研究的速度和范围.
未来的应用包括对海洋生物多样性的时空模式的更好理解.

关键词:

在 RootPainter 中使用 RootPainter.自动化区域测量自动化区域测量盆地生态生态学盆地生态学计算机视觉计算机视觉交互式机器学习交互式机器学习海洋图像分析海洋图像分析

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