Two-Stage Fine-Grained Ship Recognition with a Detector Guided by Key Regions and a Multi-Patch Joint Classifier

For human beings, fine-grained object recognition is a progressive process that proceeds from global outlines to local details. They can determine how to further focus on the distinctive regions based on the overall context, followed by recognition. To enhance the algorithm’s capability to capture critical features, a multi-stage recognition framework, integrated with human-attended key regions for fine-grained ship recognition, is proposed in this manuscript. First, a set of distinctive templates is constructed following human identification logic. On this basis, a supervised attention method, Key Regions Guided Yolo11 (KRGY), with part-to-whole regulation is proposed to help the model focus on critical components, leading to better recognition and location performance. Furthermore, a multi-head joint recognition classification module is proposed, with key regions of ship cropped with the distinctive templates. With the hypothesis and verification framework Key Regions Guided Yolo11-Multi Head Classifier (KRGY-MHC), the accuracy of ship recognition is significantly improved based on a challenging datasets with high inter-class similarity DCL-11.