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A Deep Learning Model to Recognize and Quantitatively Analyze Cold Seep Substrates and the Dominant Associated Species

文献类型：期刊论文

作者Wang, Haining2,3,5; Fu, Xiaoxue7; Zhao, Chengqian6; Luan, Zhendong1,2,3,4; Li, Chaolun1,2,3,5
刊名FRONTIERS IN MARINE SCIENCE
出版日期2021-11-25
卷号8页码:11
关键词cold seep substrates epifauna Faster R-CNN FPN VGG16
DOI10.3389/fmars.2021.775433
通讯作者Li, Chaolun(lcl@qdio.ac.cn)
英文摘要Characterizing 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.
资助项目National 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研究方向Environmental Sciences & Ecology ; Marine & Freshwater Biology
语种英语
WOS记录号WOS:000729362000001
出版者FRONTIERS MEDIA SA
源URL[http://ir.qdio.ac.cn/handle/337002/177446]  
专题海洋研究所_海洋生态与环境科学重点实验室
海洋研究所_海洋地质与环境重点实验室
通讯作者Li, Chaolun
作者单位1.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, Deep Sea Res Ctr, Qingdao, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Geol & Environm, Qingdao, Peoples R China
5.Chinese Acad Sci, Inst Oceanol, Key Lab Marine Ecol & Environm Sci, Qingdao, Peoples R China
6.Fudan Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
7.Qingdao Univ Sci & Technol, Artificial Intelligence Lab, Qingdao, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：海洋研究所

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