Fine-grained ship detection based on consistency criteria of hierarchical classification

Hierarchical and fine-grained detection of ships is essential in both military and civilian applications. Existing fine-grained detection approaches often need part-level labeling or an attention mechanism to retrieve key features. However, they do not properly exploit the affiliation information inherent in the hierarchical categorization structure of ships to increase fine-grained detection performance. Aiming at ships' hierarchical classification, we built a multi-level consistent classification mathematical model for ships. This paper proposed a fine-grained detection method and loss function based on the strict consistency criterion across multiple classification levels and created a multi-level compatible fine-grained ship detection network(MLCDet). The experimental results show that the method is effective, robust,has low resource consumption, and can effectively utilize the affiliation between categories in the classification system to improve object detection accuracy. Without the requirement for parts annotation information, mAP is increased by 1.3 percent. At the same time, the total model parameters arc only increased by 0.02 percent, while the inference speed remains unchanged.