With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of detecting eddies from one experiment is not directly inherited from the detection model for other experiments. Therefore, a cross-domain submesoscale eddy detection neural network (CDEDNet) based on the high-frequency radar (HFR) data of the Nansan and Xuwen region is proposed in this paper. Firstly, a fundamental deep eddy detection architecture CDEDNet-0 is constructed with a fully convolutional network (FCN). Secondly, for solving the problem of insufficient labeled eddy data, an instance-based domain adaption method is adopted in CDEDNet-1 to increase training samples. Thirdly, for tackling the problem of unable to inherit previous detection experience, parameter-based transfer learning is incorporated in CDEDNet-2 for multi-scene eddy detection. The experiment results demonstrate CDEDNet-1 and CDEDNet-2 perform better than CDEDNet-0 in terms of accuracy. Meanwhile, eddy characteristics including eddy type, radius, occurring time, merger, and dynamic trajectory are analyzed for the Nansan and Xuwen regions.

中文翻译：

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用于高频雷达的跨域亚尺度涡流检测神经网络

随着深度学习的快速发展，神经网络成为涡流检测的一种有效方法。然而，之前的工作采用传统的神经网络，专注于在单一场景下仅使用有限的数据来提高检测精度。同时，从一个实验中检测涡流的经验并没有直接从其他实验的检测模型中继承。因此，本文提出了一种基于南三和徐闻地区高频雷达（HFR）数据的跨域亚尺度涡旋检测神经网络（CDEDNet）。首先，基本的深度涡流检测架构 CDEDNet-0 是用全卷积网络 (FCN) 构建的。其次，为了解决标记涡流数据不足的问题，CDEDNet-1采用基于实例的域自适应方法来增加训练样本。第三，为了解决无法继承以往检测经验的问题，CDEDNet-2 中引入了基于参数的迁移学习，用于多场景涡流检测。实验结果表明 CDEDNet-1 和 CDEDNet-2 在准确性方面的表现优于 CDEDNet-0。同时，分析了南三和徐闻地区的涡类型、半径、发生时间、并合、动力轨迹等涡特征。