Ship detection on synthetic aperture radar (SAR) imagery has many valuable applications for both civil and military fields and has received extraordinary attention in recent years. The traditional detection methods are insensitive to multiscale ships and usually time-consuming, results in low detection accuracy and limitation for real-time processing. To balance the accuracy and speed, an end-to-end ship detection method for complex inshore and offshore scenes based on deep convolutional neural networks (CNNs) is proposed in this paper. First, the SAR images are divided into different grids, and the anchor boxes are predefined based on the responsible grids for dense ship prediction. Then, Darknet-53 with residual units is adopted as a backbone to extract features, and a top-down pyramid structure is added for multiscale feature fusion with concatenation. By this means, abundant hierarchical features containing both spatial and semantic information are extracted. Meanwhile, the strategies such as soft non-maximum suppression (Soft-NMS), mix-up and mosaic data augmentation, multiscale training, and hybrid optimization are used for performance enhancement. Besides, the model is trained from scratch to avoid learning objective bias of pretraining. The proposed one-stage method adopts end-to-end inference by a single network, so the detection speed can be guaranteed due to the concise paradigm. Extensive experiments are performed on the public SAR ship detection dataset (SSDD), and the results show that the method can detect both inshore and offshore ships with higher accuracy than other mainstream methods, yielding the accuracy with an average of 95.52%, and the detection speed is quite fast with about 72 frames per second (FPS). The actual Sentinel-1 and Gaofen-3 data are utilized for verification, and the detection results also show the effectiveness and robustness of the method.

中文翻译：

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基于深度CNN的复杂场景SAR图像端到端舰船检测

合成孔径雷达（SAR）图像上的船舶检测在民用和军事领域都有许多有价值的应用，并且近年来受到了极大的关注。传统的检测方法对多尺度船舶不敏感，通常很耗时，导致检测精度低，并且限制了实时处理。为了平衡精度和速度，本文提出了一种基于深度卷积神经网络（CNN）的复杂的近海和近海场景的端到端舰船检测方法。首先，将SAR图像划分为不同的网格，并根据负责的网格预定义锚框，以进行密集的船舶预测。然后，采用带有残差单元的Darknet-53作为主干来提取特征，并添加了自上而下的金字塔结构，以进行带级数的多尺度特征融合。通过这种方式，提取了既包含空间信息又包含语义信息的丰富层次结构特征。同时，使用诸如软非最大抑制（Soft-NMS），混合和镶嵌数据增强，多尺度训练以及混合优化等策略来提高性能。此外，该模型是从头开始训练的，以避免学习预训练的客观偏差。所提出的一级方法采用单个网络的端到端推理，因此，由于其简洁的范例，可以保证检测速度。在公共SAR船舶检测数据集（SSDD）上进行了广泛的实验，结果表明，该方法可以比其他主流方法以更高的精度检测近海和近海船舶，产生的准确性平均为95.52％，并且检测速度相当快，每秒约72帧（FPS）。利用实际的Sentinel-1和Gaofen-3数据进行验证，检测结果也表明了该方法的有效性和鲁棒性。