In high-resolution Synthetic Aperture Radar (SAR) ship detection, the number of SAR samples seriously affects the performance of the algorithms based on deep learning. In this paper, aiming at the application requirements of high-resolution ship detection in small samples, a high-resolution SAR ship detection method combining an improved sample generation network, Multiscale Wasserstein Auxiliary Classifier Generative Adversarial Networks (MW-ACGAN) and the Yolo v3 network is proposed. Firstly, the multi-scale Wasserstein distance and gradient penalty loss are used to improve the original Auxiliary Classifier Generative Adversarial Networks (ACGAN), so that the improved network can stably generate high-resolution SAR ship images. Secondly, the multi-scale loss term is added to the network, so the multi-scale image output layers are added, and multi-scale SAR ship images can be generated. Then, the original ship data set and the generated data are combined into a composite data set to train the Yolo v3 target detection network, so as to solve the problem of low detection accuracy under small sample data set. The experimental results of Gaofen-3 (GF-3) 3 m SAR data show that the MW-ACGAN network can generate multi-scale and multi-class ship slices, and the confidence level of ResNet18 is higher than that of ACGAN network, with an average score of 0.91. The detection results of Yolo v3 network model show that the detection accuracy trained by the composite data set is as high as 94%, which is far better than that trained only by the original SAR data set. These results show that our method can make the best use of the original data set, improve the accuracy of ship detection.

中文翻译：

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生成用于船舶检测的多尺度高分辨率SAR图像

在高分辨率合成孔径雷达（SAR）舰船检测中，SAR样本的数量严重影响了基于深度学习的算法的性能。针对小样本高分辨率船舶探测的应用需求，结合改进的样本生成网络，多尺度瓦瑟斯坦辅助分类器生成对抗网络和Yolo v3的高分辨率SAR船舶探测方法网络被提议。首先，利用多尺度Wasserstein距离和梯度惩罚损失来改进原始的辅助分类器生成对抗网络（ACGAN），从而使改进的网络能够稳定地生成高分辨率SAR舰船图像。其次，将多尺度损失项添加到网络中，从而添加多尺度图像输出层，并可以生成多尺度SAR船图像。然后，将原始舰船数据集和生成的数据组合成一个综合数据集来训练Yolo v3目标检测网络，从而解决了在小样本数据集下检测精度低的问题。高分3号（GF-3）3 m SAR数据的实验结果表明，MW-ACGAN网络可以产生多尺度多类别的船舶切片，ResNet18的置信度高于ACGAN网络。平均分数为0.91。Yolo v3网络模型的检测结果表明，复合数据集训练的检测精度高达94％，远远优于仅原始SAR数据集训练的检测精度。这些结果表明，我们的方法可以充分利用原始数据集，提高船舶检测的准确性。