SDFSD-v1.0: A Sub-Meter SAR Dataset for Fine-Grained Ship Detection

Abstract

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.

Keywords

• ship dataset
• sub-meter SAR
• dataset construction
• deep learning