Discover the Unknown Ones in Fine-Grained Ship Detection

The remote sensing image-based ship identification technology has great applications in areas, such as national defense and fishery management. However, existing remote sensing ship studies mainly focus on a closed environment and overlook actual sea conditions, while new military ships will be encountered. These unknown categories of ships will be ignored or misclassified by existing models, dramatically affecting the accurate assessment of the maritime situation. Furthermore, existing unknown detection methods for natural images fail to tackle the remote sensing ship detection problem for the property of high similarity in the overall appearance. To cope with this problem, this article proposes a fine-grained unknown ship detection network. First, we explore a class-balanced proposal sampler (CBPS) to avoid the inefficient information learning. Second, we propose a fine-grained memory bank-based contrastive learning (FGCL) strategy to separate different categories. Finally, to further separate unknown classes, we adopt an uncertainty-aware unknown learner (UAUL) with logit to reduce the uncertainty of fine-grained predictions. Experiments conducted in three public ship detection datasets ShipRSImageNet, DOSR, and HRSC2016 show that the method not only achieves good detection on unknown class ships but also improves the detection accuracy on known classes. The code is available at: https://github.com/FoRGEU/DUONet