Received date: 2023-12-30
Revised date: 2024-08-12
Online published: 2024-11-25
Ocean eddies are prevalent oceanic phenomenon that play a crucial role in the global transportation of oceanic materials and energy. Although traditional methods for detecting ocean eddies are widely used, they suffer from significant drawbacks such as excessive reliance on expert-set thresholds, continuous manual intervention, large detection errors, low efficiency, and poor global applicability, making it difficult to adapt to the complex and variable marine environment. Currently, the rapid development of artificial intelligence (AI) presents a promising solution for the intelligent detection of ocean eddies. AI can automatically and rapidly extract deep features from images, effectively address the challenges posed by the high similarity in oceanic phenomenon features and significant geometric variability. This paper provides an overview of AI-based oceanic eddy identification methods based on different deep learning methods, focuses on coder-decoder structure, fully convolutional neural network, multi-scale context method and attention mechanism, and aims to provide valuable insights and references for future ocean eddy research.
XU Guangjun , SHI Yucheng , YU Yang , XIE Huarong , XIE Wenhong , LIU Jingyuan , LIN Xiayan , LIU Yu , DONG Changming . Recent developments in AI-based oceanic eddy identification[J]. Journal of Marine Sciences, 2024 , 42(3) : 38 -50 . DOI: 10.3969/j.issn.1001-909X.2024.03.003
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