Recently, deep-learning methods have been successfully applied to the ship detection in the synthetic aperture radar (SAR) images. It is still a great challenge to detect multiscale SAR ships due to the broad diversity of the scales and the strong interference of the inshore background. Most prevalent approaches are based on the anchor mechanism that uses the predefined anchors to search the possible regions containing objects. However, the anchor settings have a great impact on their detection performance as well as the generalization ability. Furthermore, considering the sparsity of the ships, most anchors are redundant and will lead to the computation increase. In this article, a novel detection method named feature balancing and refinement network (FBR-Net) is proposed. First, our method eliminates the effect of anchors by adopting a general anchor-free strategy that directly learns the encoded bounding boxes. Second, we leverage the proposed attention-guided balanced pyramid to balance semantically the multiple features across different levels. It can help the detector learn more information about the small-scale ships in complex scenes. Third, considering the SAR imaging mechanism, the interference near the ship boundary with the similar scattering power probably affects the localization accuracy because of feature misalignment. To tackle the localization issue, a feature-refinement module is proposed to refine the object features and guide the semantic enhancement. Finally, extensive experiments are conducted to show the effectiveness of our FBR-Net compared with the general anchor-free baseline. The detection results on the SAR ship detection dataset (SSDD) and AIR-SARShip-1.0 dataset illustrate that our method achieves the state-of-the-art performance.

中文翻译：

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一种基于特征平衡和细化网络的SAR图像多尺度船舶检测无锚方法

最近，深度学习方法已成功应用于合成孔径雷达（SAR）图像中的船舶检测。由于尺度的广泛多样性和近岸背景的强烈干扰，探测多尺度SAR船舶仍然是一个巨大的挑战。最流行的方法是基于锚机制，该机制使用预定义的锚来搜索包含对象的可能区域。然而，锚点设置对其检测性能以及泛化能力有很大影响。此外，考虑到船舶的稀疏性，大多数锚都是冗余的，会导致计算量增加。在本文中，提出了一种名为特征平衡和细化网络（FBR-Net）的新型检测方法。第一的，我们的方法通过采用直接学习编码的边界框的通用无锚策略来消除锚的影响。其次，我们利用所提出的注意力引导平衡金字塔在语义上平衡不同级别的多个特征。它可以帮助检测器在复杂场景中了解更多关于小型船舶的信息。第三，考虑到 SAR 成像机制，船舶边界附近具有相似散射功率的干扰可能会因为特征未对准而影响定位精度。为了解决定位问题，提出了一个特征细化模块来细化对象特征并指导语义增强。最后，进行了大量实验以显示与一般无锚基线相比，我们的 FBR-Net 的有效性。