Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning
文献类型：期刊论文
| 作者 | Li, Qiong2 ; Sun, Xin2; Dong, Junyu2; Song, Shuqun1; Zhang, Tongtong2; Liu, Dan2; Zhang, Han2; Han, Shuai2
|
| 刊名 | ICES JOURNAL OF MARINE SCIENCE
 |
| 出版日期 | 2020-07-01 |
| 卷号 | 77期号:4页码:1427-1439 |
| ISSN号 | 1054-3139 |
| 关键词 | deep learning microscopic image object detection phytoplankton dataset |
| DOI | 10.1093/icesjms/fsz171 |
| 通讯作者 | Sun, Xin(sunxin@ouc.edu.cn) |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| 出版者 | OXFORD UNIV PRESS |
| WOS记录号 | WOS:000582718700015 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/168965]  |
| 专题 | 海洋研究所_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.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Marine Ecol & Environm Sci, 7 Nanhai Rd, Qingdao 266071, Peoples R China 2.Ocean Univ China, Coll Informat Sci & Engn, 238 Songling Rd, Qingdao 266100, Peoples R China |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：海洋研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。