IOCAS-IR  > 海洋生态与环境科学重点实验室
A Deep Learning Model to Recognize and Quantitatively Analyze Cold Seep Substrates and the Dominant Associated Species
Wang, Haining1,2,3; Fu, Xiaoxue4; Zhao, Chengqian5; Luan, Zhendong2,3,6,7; Li, Chaolun1,2,3,6
2021-11-25
Source PublicationFRONTIERS IN MARINE SCIENCE
Volume8Pages:11
Corresponding AuthorLi, Chaolun(lcl@qdio.ac.cn)
AbstractCharacterizing habitats and species distribution is important to understand the structure and function of cold seep ecosystems. This paper develops a deep learning model for the fast and accurate recognition and classification of substrates and the dominant associated species in cold seeps. Considering the dense distribution of the dominant associated species and small objects caused by overlap in cold seeps, the feature pyramid network (FPN) embed into the faster region-convolutional neural network (R-CNN) was used to detect large-scale changes and small missing objects without increasing the number of calculations. We applied three classifiers (Faster R-CNN + FPN for mussel beds, lobster clusters and biological mixing, CNN for shell debris and exposed authigenic carbonates, and VGG16 for reduced sediments and muddy bottom) to improve the recognition accuracy of substrates. The model's results were manually verified using images obtained in the Formosa cold seep during a 2016 cruise. The recognition accuracy of the two dominant species, e.g., Gigantidas platifrons and Munidopsidae could be 70.85 and 56.16%, respectively. Seven subcategories of substrates were also classified with a mean accuracy of 74.87%. The developed model is a promising tool for the fast and accurate characterization of substrates and epifauna in cold seeps, which is crucial for large-scale quantitative analyses.
Keywordcold seep substrates epifauna Faster R-CNN FPN VGG16
DOI10.3389/fmars.2021.775433
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[42030407] ; National Natural Science Foundation of China[42076091] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA22050303] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42020401] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA22050302] ; National Key R&D Program of the Ministry of Science and Technology[2018YFC0310802] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences
WOS Research AreaEnvironmental Sciences & Ecology ; Marine & Freshwater Biology
WOS SubjectEnvironmental Sciences ; Marine & Freshwater Biology
WOS IDWOS:000729362000001
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/177447
Collection海洋生态与环境科学重点实验室
海洋地质与环境重点实验室
Corresponding AuthorLi, Chaolun
Affiliation1.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Ecol & Environm Sci, Qingdao, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, Deep Sea Res Ctr, Qingdao, Peoples R China
4.Qingdao Univ Sci & Technol, Artificial Intelligence Lab, Qingdao, Peoples R China
5.Fudan Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
6.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao, Peoples R China
7.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Geol & Environm, Qingdao, Peoples R China
First Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Haining,Fu, Xiaoxue,Zhao, Chengqian,et al. A Deep Learning Model to Recognize and Quantitatively Analyze Cold Seep Substrates and the Dominant Associated Species[J]. FRONTIERS IN MARINE SCIENCE,2021,8:11.
APA Wang, Haining,Fu, Xiaoxue,Zhao, Chengqian,Luan, Zhendong,&Li, Chaolun.(2021).A Deep Learning Model to Recognize and Quantitatively Analyze Cold Seep Substrates and the Dominant Associated Species.FRONTIERS IN MARINE SCIENCE,8,11.
MLA Wang, Haining,et al."A Deep Learning Model to Recognize and Quantitatively Analyze Cold Seep Substrates and the Dominant Associated Species".FRONTIERS IN MARINE SCIENCE 8(2021):11.
Files in This Item:
File Name/Size DocType Version Access License
fmars-08-775433.pdf(3853KB)期刊论文出版稿延迟开放CC BY-NC-SAView 2023-7-1后可获取
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Haining]'s Articles
[Fu, Xiaoxue]'s Articles
[Zhao, Chengqian]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Haining]'s Articles
[Fu, Xiaoxue]'s Articles
[Zhao, Chengqian]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Haining]'s Articles
[Fu, Xiaoxue]'s Articles
[Zhao, Chengqian]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: fmars-08-775433.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.