Fine-Grained Ship Recognition With Spatial-Aligned Feature Pyramid Network and Adaptive Prototypical Contrastive Learning

Fine-grained ship recognition endeavors to accurately locate ship targets and recognize their respective fine-grained categories. Current ship recognition methods primarily rely on the feature pyramid network (FPN) for extracting multiscale features. However, FPN exhibits a spatial misalignment issue when fusing features from adjacent-scale feature maps, leading to an inability to extract fine-grained features. Consequently, this limitation constrains the fine-grained recognition capabilities of these recognition methods. Moreover, ship targets possess a high level of intraclass diversity and interclass similarity, yet existing recognition models struggle to extract features with strong category separability, resulting in weakened fine-grained ship recognition performance. In order to solve the spatial misalignment problem that occurs in FPN, a spatial-aligned FPN (SAFPN) is investigated. SAFPN employs a spatial-aware alignment fusion module (SAFM) to effectively extract rich fine-grained features between adjacent-scale feature maps. Moreover, in response to the challenge posed by low category separability in features due to the intraclass diversity and interclass similarity among ship targets, an adaptive prototypical contrastive learning (APCL) method is further proposed. By introducing prototypical contrastive loss, APCL effectively enhances the category separability of ship features, thereby improving the performance of fine-grained ship recognition. Numerous experiments are validated on two fine-grained ship recognition datasets: FGSD and ShipRSImageNet. The experimental results demonstrate that the proposed SAFPN and APCL facilitate the model in extracting fine-grained features with strong category separability, effectively enhancing the performance of fine-grained ship recognition. Our code will be public and available at https://github.com/liyangfan0/Fine-Grained-Ship-Recognition.