| Summary: | Fine-grained ship detection is an important task in high-resolution satellite remote sensing applications. However, large aspect ratios and severe category imbalance make fine-grained ship detection a challenging problem. Current methods usually extract square-like features that do not work well to detect ships with large aspect ratios, and the misalignments in feature representation will severely degrade the performance of ship localization and classification. To tackle this, we propose a shape-aware feature learning method to mitigate the misalignments during feature extraction. Furthermore, for the issue of category imbalance, we design a shape-aware instance switching to balance the quantity distribution of ships in different categories, which can greatly improve the network's learning ability for rare instances. To verify the effectiveness of the proposed method, we contribute a multicategory ship detection dataset (MCSD) that contains 4000 images carefully labeled with oriented bounding boxes, including 16 types of ship objects and nearly 18 000 instances. We conduct experiments on our MCSD and ShipRSImageNet, and extensive experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods.
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