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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.

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概括
此摘要是机器生成的。

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.

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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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.