In the field of target detection, a prominent area is represented by ship detection in SAR imagery based on deep learning, particularly for fine-grained ship detection, with dataset quality as a crucial factor influencing detection accuracy. Datasets constructed with commonly used slice-based annotation methods suffer from a lack of scalability and low efficiency in repeated editing and reuse. Existing SAR ship datasets mostly consist of medium to low resolution imagery, leading to coarse ship categories and limited background scenarios. We developed the “annotate entire image, then slice” workflow (AEISW) and constructed a sub-meter SAR fine-grained ship detection dataset (SDFSD) by using 846 sub-meter SAR images that include 96,921 ship instances of 15 ship types across 35,787 slices. The data cover major ports and shipping routes globally, with varied and complex backgrounds, offering diverse annotation information. Several State-of-the-Art rotational detection models were used to evaluate the dataset, providing a baseline for ship detection and fine-grained ship detection. The SDFSD is a high spatial resolution ship detection dataset that could drive advancements in research on ship detection and fine-grained detection in SAR imagery.

中文翻译：

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SDFSD-v1.0： 用于细粒度船舶检测的亚米级 SAR 数据集


在目标检测领域，基于深度学习的 SAR 图像中的船舶检测是一个突出区域，特别是对于细粒度船舶检测，数据集质量是影响检测精度的关键因素。使用常用的基于切片的标注方法构建的数据集缺乏可扩展性，重复编辑和复用效率低下。现有的 SAR 船舶数据集主要由中低分辨率影像组成，导致船舶类别粗糙且背景场景有限。我们开发了“注释整个图像，然后切片”工作流程 （AEISW），并使用 846 张亚米级 SAR 图像构建了一个亚米级 SAR 细粒度船舶检测数据集 （SDFSD），其中包括 35,787 个切片中 15 种船型 的 96,921 个船舶实例。数据覆盖全球主要港口和航线，背景多样复杂，标注信息多样。使用了几个最先进的旋转检测模型来评估数据集，为船舶检测和细粒度船舶检测提供了基线。SDFSD 是一种高空间分辨率船舶检测数据集，可以推动船舶检测和 SAR 影像中精细检测的研究进展。