Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology. LS-SSDD-v1.0 is available at https://github.com/TianwenZhang0825/LS-SSDD-v1.0-OPEN.

中文翻译：

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LS-SSDD-v1.0：深度学习数据集，用于从大型Sentinel-1 SAR图像进行小舰船检测

合成孔径雷达（SAR）图像中的船舶检测正成为研究热点。近年来，随着人工智能的兴起，深度学习几乎以其精度高，速度快，人工干预少等优势在SAR船舶检测领域占主导地位。然而，今天，仍然缺乏可靠的深度学习SAR船舶可以满足大场景星载SAR图像中船舶探测的实际迁移应用的探测数据集。因此，为解决此问题，本文发布了Sentinel-1的大型SAR船舶检测数据集-v1.0（LS-SSDD-v1.0），用于大规模背景下的小型船舶检测。LS-SSDD-v1.0包含15个大规模SAR图像，SAR专家通过利用自动识别系统（AIS）和Google Earth的支持正确地标记了地面真相。为了便于网络训练，将大尺寸图像直接切成9000个无铃铛的子图像，为以后在大尺寸SAR图像中呈现检测结果提供了方便。值得注意的是，LS-SSDD-v1.0具有五个优点：（1）大型背景，（2）小型船舶检测，（3）丰富的纯背景，（4）全自动检测流程以及（5）众多且标准化的研究基准。最后但并非最不重要的一点是，结合丰富的纯背景优势，我们还提出了一种纯背景混合训练机制（PBHT机制），以抑制大规模SAR图像中的陆地虚警。消融研究的实验结果可以验证PBHT机制的有效性。LS-SSDD-v1。0可以激发相关学者对具有实用价值的SAR船舶探测方法进行深入研究，有利于SAR智能解释技术的发展。LS-SSDD-v1.0在以下位置可用https://github.com/TianwenZhang0825/LS-SSDD-v1.0-OPEN。