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Received：10 October 2022，
Revised：2023-01-14，
Published：16 November 2023
移动端阅览
目的
2
海洋涡旋精准检测是揭示海洋涡旋演变规律及其与其他海洋现象相互作用的基础。然而，海洋涡旋在其活跃海域呈现小尺度目标、密集分布的特点，导致显著的检测精度低问题。传统方法受限于人工设计参数缺乏泛化能力，而深度学习模型的高采样率在检测小目标过程中底层细节和轮廓等信息损失严重，使得目标检测轮廓与目标真实轮廓相差甚远。针对海洋涡旋小目标特点导致检测精度低，高采样率深度模型检测轮廓不精确的问题，提出一种改进的U-Net网络。
方法
2
该模型基于渐进式采样结构，为获取上下文信息提升不同极性海洋涡旋目标的检测精度，增加上下文特征融合模块；为增加该模块对海洋涡旋小目标的关注，在特征融合前对最底层特征嵌入残差注意力模块，使模型可以更多关注海洋涡旋的轮廓信息。最后引入数据扩充方法缓解模型存在的过拟合问题。
结果
2
本文以南大西洋的卫星海表面高度数据集开展实验，结果表明，本文模型检测准确率达到了93.24%，同时在海洋涡旋的检测数量上与真实结果更加接近，验证了模型在小目标检测方面的性能更加优秀。
结论
2
本文提出的海洋涡旋小目标检测模型，在检测海洋涡旋的性能与海洋涡旋目标轮廓精准度方面均显著优于全卷积神经网络（fully convolutional network，FCN）等深度学习模型。
Objective
2
Ocean eddies are responsible for most of the material transportation and energy transfers in the ocean. The accurate detection of these eddies serves as the basis for revealing the evolution of ocean eddies and their interactions with other marine phenomena. However， small-scale objects and dense distribution are often observed in the active area of ocean eddies， which leads to problem of low detection accuracy. Traditional detection methods are limited by the poor generalizability of the artificial parameter design. These methods also have poor ocean eddy detection accuracy compared with deep learning methods. However， a deep learning model with high sampling rate loses the underlying details and contour information in the process of small target detection. The target detection contour is located far from the real contour of the target. To address the low detection accuracy caused by the loss of low-level detail information and contour information of small-scale ocean eddy targets， this paper proposes an improved U-Net network.
Method
2
Based on the U-shaped progressive sampling network， a context feature fusion module is added to fuse the features of each coding layer， and a residual attention mechanism is added to the target features before the feature fusion in order for the model to pay attention to the contour information of the ocean eddies. A data augmentation method is then introduced to reduce the overfitting problem of the model. Feature fusion is carried out through the context feature fusion module， which takes the three-layer feature map of the U-shaped structure coding layer of the U-Net network as input， the lowest-level feature map as the target feature， and the last two-layer feature map as the context and target features. The context feature map is initially upsampled to the same size as the lowest-level feature through the deconvolution structure， and the number of channels is reduced to 1/2 of the lowest-level feature in order to prevent the amount of information of the context feature from exceeding that of the target feature. L2 norm and ReLU are then used to achieve the fusion of context and target features. The proposed model uses two contextual feature fusion modules， which take the first to third layer feature maps of the encoding layer as input and the second to fourth layer feature maps as input， respectively. The residual attention mechanism consists of two processing channels. The first channel has a residual structure （batch norm， conv of 1 × 1 kernel and multiple concatenation of ReLU） that prevents gradient disappearance and extracts certain contour information， while the second channel comprises a down-up sampling layer and a sigmoid layer to extract high-level semantic information. To effectively reduce the over-fitting phenomenon， random region sampling and random mask processing are used for data augmentation. In the experiment， the model is trained in the NVIDIA GTX 1080Ti GPU environment， where its initial learning rate is set to 1 × 10
-3
， the loss function is optimized by the Adam optimizer， the batch size of the model training is set to 16， and the number of iterations is set to 200.
Result
2
The satellite sea surface height dataset of the South Atlantic is used for the experiments. Ablation experiments are carried out to test the influence of each module on the performance of the ocean eddies detection model. The effects of adding the context feature fusion module， adding the attention mechanism module， and adding both modules at the same time are compared， and the detection effect after adding the data augmentation method is analyzed. In the ablation experiment， due to the introduction of the contextual feature fusion module and the residual attention mechanism， the model can fuse the contextual features of the ocean eddies in different feature layers， and the network can extract additional low-level spatial details of the ocean eddies. Each module improves the detection performance of the model， and the optimal detection accuracy of the model after using the data augmentation method reaches 93.27%. Compared with other deep learning models， the proposed model has a detection accuracy of up to 93.24%， and its detected number of ocean eddies is closer to the truth， thereby verifying its excellent performance in small target detection. Meanwhile， compared with the fully convolutional network （FCN） model， the proposed model can detect more small-scale ocean eddy targets， and the detected ocean eddy target contour is closer to the truth， thereby verifying the positive effect of progressive sampling on small target detection.
Conclusion
2
The proposed model significantly outperforms the other deep learning models in detecting ocean eddies. Compared with the state-of-the-art， the proposed model achieves a higher small target detection accuracy， and the detected contour of ocean eddies is closer to the truth.
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