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首页> 外文期刊>ICES Journal of Marine Science >Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning
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Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning

机译:使用深度学习,开发一种支持智能浮游植物检测的微观图像数据集

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摘要

Phytoplankton plays an important role in marine ecological environment and aquaculture. However, the recognition and detection of phytoplankton rely on manual operations. As the foundation of achieving intelligence and releasing human labour, a phytoplankton microscopic image dataset PMID2019 for phytoplankton automated detection is presented. The PMID2019 dataset contains 10819 phytoplankton microscopic images of 24 different categories. We leverage microscopes to collect images of phytoplankton in the laboratory environment. Each object in the images is manually labelled with a bounding box and category of ground-truth. In addition, living cells move quickly making it difficult to capture images of them. In order to generalize the dataset for in situ applications, we further utilize Cycle-GAN to achieve the domain migration between dead and living cell samples. We built a synthetic dataset to generate the corresponding living cell samples from the original dead ones. The PMID2019 dataset will not only benefit the development of phytoplankton microscopic vision technology in the future, but also can be widely used to assess the performance of the state-of-the-art object detection algorithms for phytoplankton recognition. Finally, we illustrate the performances of some state-of-the-art object detection algorithms, which may provide new ideas for monitoring marine ecosystems.
机译:Phytoplankton在海洋生态环境和水产养殖中起着重要作用。然而,浮游植物依赖手动操作的识别和检测。作为实现智能和释放人工劳动力的基础,提出了一种浮游植物微观图像数据集PMID2019用于浮游植物自动检测。 PMID2019数据集包含24个不同类别的10819 Phytoplankton显微图像。我们利用显微镜在实验室环境中收集浮游植物的图像。图像中的每个对象都用边界框和地面真理类别标记。此外,活细胞快速移动,难以捕获它们的图像。为了推广数据集以便原位应用,我们进一步利用循环甘核来实现死亡和活细胞样本之间的域迁移。我们构建了一个合成数据集以生成原始死亡人员的相应生活单元格样本。 PMID2019 DataSet不仅将在未来受益于Phytoplankton显微视觉技术的发展,而且还可以广泛用于评估最先进的物体检测算法的性能,用于浮游植物识别。最后,我们说明了一些最先进的对象检测算法的性能,这可以为监测海洋生态系统提供新的想法。

著录项

  • 来源
    《ICES Journal of Marine Science》 |2020年第4期|1427-1439|共13页
  • 作者单位

    Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao 266100 Peoples R China;

    Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao 266100 Peoples R China;

    Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao 266100 Peoples R China;

    Chinese Acad Sci Inst Oceanol CAS Key Lab Marine Ecol & Environm Sci 7 Nanhai Rd Qingdao 266071 Peoples R China;

    Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao 266100 Peoples R China;

    Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao 266100 Peoples R China;

    Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao 266100 Peoples R China;

    Ocean Univ China Coll Informat Sci & Engn 238 Songling Rd Qingdao 266100 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    deep learning; microscopic image; object detection; phytoplankton dataset;

    机译:深入学习;微观图像;物体检测;Phytoplankton数据集;

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