SAR Ship Detection Research Articles

Ellipse Polar Encoding for Oriented SAR Ship Detection

Ellipse Polar Encoding for Oriented SAR Ship Detection

DBW-YOLO: A High-Precision SAR Ship Detection Method for Complex Environments

DBW-YOLO: A High-Precision SAR Ship Detection Method for Complex Environments

MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention

MSFA-YOLO: A Multi-Scale SAR Ship Detection Algorithm Based on Fused Attention

ELLK-Net: An Efficient Lightweight Large Kernel Network for SAR Ship Detection

ELLK-Net: An Efficient Lightweight Large Kernel Network for SAR Ship Detection

Multilevel Pyramid Feature Extraction and Task Decoupling Network for SAR Ship Detection

Multilevel Pyramid Feature Extraction and Task Decoupling Network for SAR Ship Detection

M-FSDistill: A Feature Map Knowledge Distillation Algorithm for SAR Ship Detection

M-FSDistill: A Feature Map Knowledge Distillation Algorithm for SAR Ship Detection

BFEA: A SAR Ship Detection Model Based on Attention Mechanism and Multiscale Feature Fusion

BFEA: A SAR Ship Detection Model Based on Attention Mechanism and Multiscale Feature Fusion

Filling and Disentanglement: Towards Low- and High-Order Parallel Single-Domain Generalization for SAR Ship Detection

Filling and Disentanglement: Towards Low- and High-Order Parallel Single-Domain Generalization for SAR Ship Detection

From optical to SAR： A SAR ship detection algorithm based on multi-level cross-modality alignment

From optical to SAR： A SAR ship detection algorithm based on multi-level cross-modality alignment

A Compact and High-Efficiency Anchor-Free Network Based on Contour Key Points for SAR Ship Detection

A Compact and High-Efficiency Anchor-Free Network Based on Contour Key Points for SAR Ship Detection

YOLO-RC: SAR Ship Detection Guided by Characteristics of Range-Compressed Domain

YOLO-RC: SAR Ship Detection Guided by Characteristics of Range-Compressed Domain

CroMoDa: Unsupervised Oriented SAR Ship Detection via Cross-Modality Distribution Alignment

CroMoDa: Unsupervised Oriented SAR Ship Detection via Cross-Modality Distribution Alignment

CFAR-DP-FW: A CFAR-Guided Dual-Polarization Fusion Framework for Large-Scene SAR Ship Detection

CFAR-DP-FW: A CFAR-Guided Dual-Polarization Fusion Framework for Large-Scene SAR Ship Detection

NPA2Net: A Nested Path Aggregation Attention Network for Oriented SAR Ship Detection

NPA2Net: A Nested Path Aggregation Attention Network for Oriented SAR Ship Detection

Lightweight SAR Ship Detection Network Based on Transformer and Feature Enhancement

Lightweight SAR Ship Detection Network Based on Transformer and Feature Enhancement

A novel twin branch network based on mutual training strategy for ship detection in SAR images

There are inconsistent tasks and insufficient training in the SAR ship detection model, which severely limit the detection performance of the model. Therefore, we propose a twin branch network and design two loss functions: regression reverse convergence loss and classification mutual learning loss. The twin branch network is a simple but effective method containing two components: twin regression network and twin classification network. Aiming at the inconsistencies between training and testing in regression branches, we propose a regression reverse convergence loss (RRC Loss) based on twin regression networks. This loss can make multiple training samples in the twin regression branch converge to the label from the opposite direction. In this way, the test distribution can be closer to the training distribution after processing. For inadequate training in classification branch, Inspired by knowledge distillation, we construct self-knowledge distillation using a twin classification network. Meanwhile, our proposed classification mutual learning loss (CML Loss) enables the twin classification network not only to conduct supervised learning based on the label but also to learn from each other. Experiments on SSDD and HRSID datasets prove that, compared with the original method, the proposed method can improve the AP by 2.7–4.9% based on different backbone networks, and the detection performance is better than other advanced algorithms.

DSF-Net: A Dual Feature Shuffle Guided Multi-Field Fusion Network for SAR Small Ship Target Detection

SAR images play a crucial role in ship detection across diverse scenarios due to their all-day, all-weather characteristics. However, detecting SAR ship targets poses inherent challenges due to their small sizes, complex backgrounds, and dense ship scenes. Consequently, instances of missed detection and false detection are common issues. To address these challenges, we propose the DSF-Net, a novel framework specifically designed to enhance small SAR ship detection performance. Within this framework, we introduce the Pixel-wise Shuffle Attention module (PWSA) as a pivotal step to strengthen the feature extraction capability. To enhance long-range dependencies and facilitate information communication between channels, we propose a Non-Local Shuffle Attention (NLSA) module. Moreover, NLSA ensures the stability of the feature transfer structure and effectively addresses the issue of missed detection for small-sized targets. Secondly, we introduce a novel Triple Receptive Field-Spatial Pyramid Pooling (TRF-SPP) module designed to mitigate the issue of false detection in complex scenes stemming from inadequate contextual information. Lastly, we propose the R-tradeoff loss to augment the detection capability for small targets, expedite training convergence, and fortify resistance against false detection. Quantitative validation and qualitative visualization experiments are conducted to substantiate the proposed assumption of structural stability and evaluate the effectiveness of the proposed modules. On the LS-SSDDv1.0 dataset, the mAP50−95 demonstrates a remarkable improvement of 8.5% compared to the baseline model. The F1 score exhibits a notable enhancement of 6.9%, surpassing the performance of advanced target detection methods such as YOLO V8.

Sar Ship Detection Based on Convnext with Multi-Pooling Channel Attention and Feature Intensification Pyramid Network.

The advancements in ship detection technology using convolutional neural networks (CNNs) regarding synthetic aperture radar (SAR) images have been significant. Yet, there are still some limitations in the existing detection algorithms. First, the backbones cannot generate high-quality multiscale feature maps. Second, there is a lack of suitable attention mechanisms to suppress false alarms. Third, the current feature intensification algorithms are unable to effectively enhance the shallow feature's semantic information, which hinders the detection of small ships. Fourth, top-level feature maps have rich semantic information; however, as a result of the reduction of channels, the semantic information is weakened. These four problems lead to poor performance in SAR ship detection and recognition. To address the mentioned issues, we put forward a new approach that has the following characteristics. First, we use Convnext as the backbone to generate high-quality multiscale feature maps. Second, to suppress false alarms, the multi-pooling channel attention (MPCA) is designed to generate a corresponding weight for each channel, suppressing redundant feature maps, and further optimizing the feature maps generated by Convnext. Third, a feature intensification pyramid network (FIPN) is specifically designed to intensify the feature maps, especially the shallow feature maps. Fourth, a top-level feature intensification (TLFI) is also proposed to compensate for semantic information loss within the top-level feature maps by utilizing semantic information from different spaces. The experimental dataset employed is the SAR Ship Detection Dataset (SSDD), and the experimental findings display that our approach exhibits superiority compared to other advanced approaches. The overall Average Precision (AP) reaches up to 95.6% on the SSDD, which improves the accuracy by at least 1.7% compared to the current excellent methods.

SAR ship detection based on salience region extraction and multi-branch attention

SAR ship detection based on salience region extraction and multi-branch attention

Rotated SAR Ship Detection based on Gaussian Wasserstein Distance Loss

Rotated SAR Ship Detection based on Gaussian Wasserstein Distance Loss