Fine-Grained Ship Recognition for Complex Background Based on Global to Local and Progressive Learning

The existing deep learning fine-grained recognition models have low recognition accuracy (Acc) in complex natural environments. We propose a fine-grained ship recognition model [global to local progressive learning module (GLPM)] based on global to local and progressive learning for complex backgrounds to address this problem. GLPM improves fine-grained ship targets' recognition Acc in natural complex backgrounds by progressively guiding high-level global features to low-level local features for learning and enhancing local fine-grained features expression ability. First, the global features are obtained by adding the feature pyramid attention (FPA) module to the deepest level of the backbone network. Second, the decoding and reconstruction of low-level local feature information at different levels are guided, and the global features are used to weight the key pixel values of local features of the target. Finally, the output of the backbone network is combined with the output vector of the global to local progressive module for classification, and the whole model can be trained end-to-end. We experiment on naturally collected complex background images containing 59 ship classes, and the public maritime (MAR)-ships contain pictures of 23 types of ships. The results show that the proposed method outperforms the methods compared in this letter while providing valuable ideas for the research of fine-grained ship recognition models in complex natural environments.