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Ship recognition with optical remote sensing images is currently widely used in fishery management, ship traffic surveillance, and maritime warfare. However, it currently faces two major challenges: recognizing rotated targets and achieving fine-grained recognition. To address these challenges, this paper presents a new model called Related-YOLO. This model utilizes the mechanisms of relational attention to stress positional relationships between the components of a ship, extracting key features more accurately. Furthermore, it introduces a hierarchical clustering algorithm to implement adaptive anchor boxes. To tackle the issue of detecting multiple targets at different scales, a small target detection head is added. Additionally, the model employs deformable convolution to extract the features of targets with diverse shapes. To evaluate the performance of the proposed model, a new dataset named FGWC-18 is established, specifically designed for fine-grained warship recognition. Experimental results demonstrate the excellent performance of the model on this dataset and two other public datasets, namely FGSC-23 and FGSCR-42. In summary, our model offers a new route to solve the challenging issues of detecting rotating targets and fine-grained recognition with remote sensing images, which provides a reliable foundation for the application of remote sensing images in a wide range of fields. [ABSTRACT FROM AUTHOR]

Ship recognition with optical remote sensing images is currently widely used in fishery management, ship traffic surveillance, and maritime warfare. However, it currently faces two major challenges: recognizing rotated targets and achieving fine-grained recognition. To address these challenges, this paper presents a new model called Related-YOLO. This model utilizes the mechanisms of relational attention to stress positional relationships between the components of a ship, extracting key features more accurately. Furthermore, it introduces a hierarchical clustering algorithm to implement adaptive anchor boxes. To tackle the issue of detecting multiple targets at different scales, a small target detection head is added. Additionally, the model employs deformable convolution to extract the features of targets with diverse shapes. To evaluate the performance of the proposed model, a new dataset named FGWC-18 is established, specifically designed for fine-grained warship recognition. Experimental results demonstrate the excellent performance of the model on this dataset and two other public datasets, namely FGSC-23 and FGSCR-42. In summary, our model offers a new route to solve the challenging issues of detecting rotating targets and fine-grained recognition with remote sensing images, which provides a reliable foundation for the application of remote sensing images in a wide range of fields.

Fine-grained ship detection is very important in the remote sensing field. Most previous remote sensing object detection works only utilize the global features for fine-grained object detection, which ignores the local information, deteriorating the detection performance. In this article, we propose a multigranularity self-attention network (MGANet), which can exploit both the global and local features for fine-grained ship detection. The MGANet consists of two modules: a local-global features alignment module (LAM) and a multigranularity self-attention module (MSM). The LAM is designed to align features of object parts and features of the object by using the convolution with different strides. The MSM introduces a self-attention mechanism that can effectively fuse the global and local features for fine-grained ship detection. In addition, we launched an oriented bounding box-based fine-grained ship detection (OFSD) dataset which is the largest fine-grained ship dataset to test and verify the effectiveness of the proposed MGANet method. Comprehensive evaluations on the OFSD and ShipRSImageNet datasets demonstrate the superiority of our proposed MGANet method over existing state-of-the-art methods for fine-grained ship detection in remote sensing images.