Clark H Poppy, Smith Abraham George, McKay Fletcher Daniel, Larsson Ann I, Jaspars Marcel, De Clippele Laurence H
Marine Biodiscovery Centre, Department of Chemistry, University of Aberdeen, Aberdeen AB24 3UE, UK.
Department of Computer Science, University of Copenhagen, Copenhagen 2100, Denmark.
R Soc Open Sci. 2024 May 22;11(5):231678. doi: 10.1098/rsos.231678. eCollection 2024 May.
Advancing imaging technologies are drastically increasing the rate of marine video and image data collection. Often these datasets are not analysed to their full potential as extracting information for multiple species is incredibly time-consuming. This study demonstrates the capability of the open-source interactive machine learning tool, RootPainter, to analyse large marine image datasets quickly and accurately. The ability of RootPainter to extract the presence and surface area of the cold-water coral reef associate sponge species, , was tested in two datasets: 18 346 time-lapse images and 1420 remotely operated vehicle video frames. New corrective annotation metrics integrated with RootPainter allow objective assessment of when to stop model training and reduce the need for manual model validation. Three highly accurate models were created using RootPainter, with an average dice score of 0.94 ± 0.06. Transfer learning aided the production of two of the models, increasing analysis efficiency from 6 to 16 times faster than manual annotation for time-lapse images. Surface area measurements were extracted from both datasets allowing future investigation of sponge behaviours and distributions. Moving forward, interactive machine learning tools and model sharing could dramatically increase image analysis speeds, collaborative research and our understanding of spatiotemporal patterns in biodiversity.
先进的成像技术正在大幅提高海洋视频和图像数据的收集速度。通常，这些数据集并未得到充分分析，因为提取多个物种的信息非常耗时。本研究展示了开源交互式机器学习工具RootPainter快速、准确分析大型海洋图像数据集的能力。在两个数据集中测试了RootPainter提取冷水珊瑚礁伴生海绵物种的存在和表面积的能力，这两个数据集分别是18346张延时图像和1420个遥控潜水器视频帧。与RootPainter集成的新的校正注释指标允许客观评估何时停止模型训练，并减少手动模型验证的需求。使用RootPainter创建了三个高度准确的模型，平均骰子系数得分为0.94±0.06。迁移学习辅助了其中两个模型的生成，对于延时图像，分析效率比手动注释快6到16倍。从两个数据集中提取了表面积测量值，以便未来研究海绵的行为和分布。展望未来，交互式机器学习工具和模型共享可以显著提高图像分析速度、促进合作研究，并增进我们对生物多样性时空模式的理解。
