College of Oceanography and Space Informatics,China University of Petroleum(East China),Qingdao 266580 ,China ;Technology Innovation Center for Maritime Silk Road Marine Resources andEnvironment Networked Observation,Ministry of Natural Resources,Qingdao 265800 ,China 在期刊界中查找 在百度中查找 在本站中查找
Qingdao Institute of Software,College of Computer Science and Technology,China Universityof Petroleum(East China),Qingdao 266580 ,China 在期刊界中查找 在百度中查找 在本站中查找
First Institute of Oceanography,Ministry of NaturalResources,Qingdao 266061 ,China ;Technology Innovation Center for Maritime Silk Road Marine Resources andEnvironment Networked Observation,Ministry of Natural Resources,Qingdao 265800 ,China 在期刊界中查找 在百度中查找 在本站中查找
Mesoscale eddies are a common marine mesoscale phenomenon. Studying the distribution and motion characteristics of mesoscale eddies plays an important role in shipping,climate,and military affairs. At present,the identification of marine mesoscale eddies is a hot research topic in the field of oceanography and computer science. In this paper,the two-dimensional identification and three-dimensional structure construction of mesoscale eddies are studied and analyzed by using deep learning method and framework. Firstly,the global marine physics reanalysis data are obtained to visualize streamlines to build the dataset of eddy streamlines. Secondly,the YOLO v5s convolutional neural network is used to train the eddy dataset and to effectively detect mesoscale eddies in the South China Sea region. The experimental results show that the mean precision accuracy of the optimal model obtained after YOLO v5s training reaches 86.10%,which could provide an effective basis for the identification of mesoscale eddies and the construction of 3D structures. Finally,based on the eddy detection results at the same time, the detected eddies at different depths are determined whether they belonged to the same eddy,then the 3D structure of the eddy is built.
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