| 摘要: |
| 深度学习可以通过深度神经网络，使机器理解学习数据，从而提高数据分类效果和预测结果的准确性，因此在海洋信息探测中应用越来越受到重视。作者基于深度学习的基本原理，阐述了海洋上常用的深度学习神经网络模型，并结合海洋信息探测要素，对温度、盐度、风场、有效波高和海冰等进行了海洋环境信息因子的预测分析；同时，对船舶、溢油和涡旋等进行了海洋目标识别与检测。最后针对其现状进行了探讨，总结了深度学习在海洋信息探测中发展所面临的问题。 |
| 关键词: 深度学习 海洋信息探测 神经网络 预测 识别检测 |
| DOI：10.11759/hykx20210716002 |
| 分类号:P71 |
| 基金项目:上海市科委科研计划项目（18DZ2253900）；教育部产学合作协同育人项目（202102245031） |
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| Research progress in the application of deep learning to ocean information detection:status and prospect |
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ZHANG Xue-wei1, HAN Zhen1,2, GUO Xin1
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1.College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;2.Shanghai Engineering Research Center of Estuarine and Oceanographic Mapping, Shanghai 201306, China
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| Abstract: |
| Deep learning can enable machines to understand the learning data through deep neural networks to improve the data classification effect and the accuracy of prediction results. Therefore, its application in ocean information detection has received more attention. Based on the basic principles of deep learning, this paper describes a deep learning neural network model commonly used in the ocean. Marine environmental information factors such as temperature, salinity, wind field, effective wave height, and sea ice are predicted and analyzed with ocean information detection elements. At the same time, marine target identification and detection are carried out for ships, oil spills, and eddies. Finally, the status of deep learning in ocean information exploration is discussed, and problems faced by the development of deep learning in ocean information exploration are summarized. |
| Key words: deep learning ocean information exploration neural network prediction identify testing |
