Issues such as complex noise interference and the long-tail distribution of data present many challenges to the multi-category ship detection task in synthetic aperture radar (SAR) images. This paper proposes an efficient multi-category-oriented SAR ship detector, which adopts a powerful lightweight feature enhancement vision transformer backbone (FEViT) for a comprehensive feature representation in SAR ship image and, hence, is referred to as FEVT-SAR. FEViT includes two innovative lightweight modules: localized feature interactive convolution block (LFICB) and dual-granularity attention transformer block (DGTB). LFICB fuses multi-receptive field local features to suppress speckle noise, while DGTB employs a coarse-to-fine self-attention to capture the global dependencies and avoids enormous computational costs. Moreover, a selective CopyPaste augmentation (SCP) paradigm is designed to rebalance ship data distribution through data sampling. Finally, the performance of the FEVT-SAR is evaluated on two typical SAR ship datasets, namely SRSDD and HRSID. Experimental results show that the mean average precision (mAP50) of FEVT-SAR reaches 68.59% and 89.62%, respectively. The proposed FEVT-SAR outperforms several state-of-the-art-oriented bounding box detectors in the multi-category ship dataset SRSDD while demonstrating its robustness in the single-category ship dataset HRSID. The code is available at https://github.com/dmfdmf/FEVT-SAR.

The unrestricted development and utilization of marine resources have resulted in a series of practical problems, such as the destruction of marine ecology. The wide application of radar, satellites and other detection equipment has gradually led to a large variety of large-capacity marine spatiotemporal trajectory data from a vast number of sources. In the field of marine domain awareness, there is an urgent need to use relevant information technology means to control and monitor ships and accurately classify and identify ship behavior patterns through multisource data fusion analysis. In addition, the increase in the type and quantity of trajectory data has produced a corresponding increase in the complexity and difficulty of data processing that cannot be adequately addressed by traditional data mining algorithms. Therefore, this paper provides a deep learning-based algorithm for the recognition of four main motion types of the ship from automatic identification system (AIS) data: anchoring, mooring, sailing and fishing. A new method for classifying patterns is presented that combines the computer vision and time series domains. Experiments are carried out on a dataset constructed from the open AIS data of ships in the coastal waters of the United States, which show that the method proposed in this paper achieves more than 95% recognition accuracy. The experimental results confirm that the method proposed in this paper is effective in classifying ship trajectories using AIS data and that it can provide efficient technical support for marine supervision departments.

Ship recognition is a fundamental and essential step in maritime activities, and it can be widely used in maritime rescue, vessel management, and other applications. However, most studies conducted in this area use synthetic aperture radar (SAR) images and space-borne optical images, and those studies utilizing visible images are limited to the coarse-grained level. In this study, we constructed a fine-grained ship dataset with real images and simulation images that consisted of five categories of ships. To solve the problem of low accuracy in fine-grained ship classification with different angles in visible images, a network based on domain adaptation and a transformer was proposed. Concretely, style transfer was first used to reduce the gap between the simulation images and real images. Then, with the goal of utilizing the simulation images to execute classification tasks on the real images, a domain adaptation network based on local maximum mean discrepancy (LMMD) was used to align the different domain distributions. Furthermore, considering the innate attention mechanism of the transformer, a vision transformer (ViT) was chosen as the feature extraction module to extract the fine-grained features, and a fully connected layer was used as the classifier. Finally, the experimental results showed that our network had good performance on the fine-grained ship dataset with an overall accuracy rate of 96.0%, and the mean average precision (mAP) of detecting first and then classifying with our network was 87.5%, which also verified the feasibility of using images generated by computer simulation technology for auxiliary training.

Oriented ship detection in Synthetic Aperture Radar (SAR) images has broad applications in maritime surveillance and other fields. While deep learning advancements have significantly improved ship detection performance, persistent challenges remain for existing methods. These include the inherent misalignment between regression and classification tasks and the boundary discontinuity problem in oriented object detection. These issues hinder efficient and accurate ship detection in complex scenarios. To address these challenges, we propose TIAR-SAR, a novel oriented SAR ship detector featuring a task interaction head and composite angle regression. First, we propose a task interaction detection head (Tihead) capable of predicting both oriented bounding boxes (OBBs) and horizontal bounding boxes (HBBs) simultaneously. Within the Tihead, a “decompose-then-interact” structure is designed. This structure not only mitigates feature misalignment but also promotes feature interaction between regression and classification tasks, thereby enhancing prediction consistency. Second, we propose a joint angle refinement mechanism (JARM). The JARM addresses the non-differentiability problem of the traditional rotated Intersection over Union (IoU) loss through the design of a composite angle regression loss (CARL) function, which strategically combines direct and indirect angle regression methods. A boundary angle correction mechanism (BACM) is then designed to enhance angle estimation accuracy. During inference, BACM dynamically replaces an object’s OBB prediction with its corresponding HBB if the OBB exhibits excessive angle deviation when the angle of the object is near the predefined boundary. Finally, the performance and applicability of the proposed methods are evaluated through extensive experiments on multiple public datasets, including SRSDD, HRSID, and DOTAv1. Experimental results derived from the use of the SRSDD dataset demonstrate that the mAP50 of the proposed method reaches 63.91%, an improvement of 4.17% compared with baseline methods. The detector achieves 17.42 FPS on 1024 × 1024 images using an RTX 2080 Ti GPU, with a model size of only 21.92 MB. Comparative experiments with other state-of-the-art methods on the HRSID dataset demonstrate the proposed method’s superior detection performance in complex nearshore scenarios. Furthermore, when further tested on the DOTAv1 dataset, the mAP50 can reach 79.1%.

Oriented ship detection in Synthetic Aperture Radar (SAR) images is significantly challenged by inherent speckle noise, which severely degrades detection performance. To address this issue, a noise-guided detection model named NG-YOLO is proposed based on YOLO11s-OBB. Specifically, a Mixed Noise aware Dual-Branch Feature Enhancement (MN-DBFE) module is designed to adaptively inject speckle and additive noise during training, enabling an auxiliary branch to effectively learn under lying noise distributions. A Feature Caching and Reuse (FCR) mechanism is then introduced to improve detection speed, which makes the learned feature biases fixed by setting the injected noises to zero during inference. Extensive experiments on the HRSID and SRSDD datasets demonstrate the effectiveness of the proposed model in SAR ship detection tasks. The proposed NG-YOLO achieves mAP50 scores of 91.0% and 63.5% on HRSID and SRSDD, respectively, outperforming the YOLO11s OBB baseline by 4.8% and 7.2%.