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Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning
Li, Qiong1; Sun, Xin1; Dong, Junyu1; Song, Shuqun2; Zhang, Tongtong1; Liu, Dan1; Zhang, Han1; Han, Shuai1
2020-07-01
发表期刊ICES JOURNAL OF MARINE SCIENCE
ISSN1054-3139
卷号77期号:4页码:1427-1439
通讯作者Sun, Xin(sunxin@ouc.edu.cn)
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.
关键词deep learning microscopic image object detection phytoplankton dataset
DOI10.1093/icesjms/fsz171
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[U1706218] ; National Natural Science Foundation of China[61971388] ; Key Research and Development Program of Shandong Province[GG201703140154] ; Applied Basic Research Programs of Qingdao[18-2-2-38-jch]
WOS研究方向Fisheries ; Marine & Freshwater Biology ; Oceanography
WOS类目Fisheries ; Marine & Freshwater Biology ; Oceanography
WOS记录号WOS:000582718700015
出版者OXFORD UNIV PRESS
引用统计
被引频次:23[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.qdio.ac.cn/handle/337002/168966
专题海洋生态与环境科学重点实验室
通讯作者Sun, Xin
作者单位1.Ocean Univ China, Coll Informat Sci & Engn, 238 Songling Rd, Qingdao 266100, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Marine Ecol & Environm Sci, 7 Nanhai Rd, Qingdao 266071, Peoples R China
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Li, Qiong,Sun, Xin,Dong, Junyu,et al. Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning[J]. ICES JOURNAL OF MARINE SCIENCE,2020,77(4):1427-1439.
APA Li, Qiong.,Sun, Xin.,Dong, Junyu.,Song, Shuqun.,Zhang, Tongtong.,...&Han, Shuai.(2020).Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning.ICES JOURNAL OF MARINE SCIENCE,77(4),1427-1439.
MLA Li, Qiong,et al."Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning".ICES JOURNAL OF MARINE SCIENCE 77.4(2020):1427-1439.
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