SAR Ship Detection Research Articles

ADV-YOLO: improved SAR ship detection model based on YOLOv8

ADV-YOLO: improved SAR ship detection model based on YOLOv8

SAR Ship Detection Using Image Histograms and Machine Learning Approach

SAR Ship Detection Using Image Histograms and Machine Learning Approach

Improve the Performance of SAR Ship Detectors by Small Object Detection Strategies

Although advanced deep learning techniques have significantly improved SAR ship detection, accurately detecting small ships remains challenging due to their limited size and the few appearance and geometric clues available. In order to solve this problem, we propose several small object detection strategies. The backbone network uses space-to-depth convolution to replace stride and pooling. It reduces information loss during down-sampling. The neck integrates multiple layers of features globally and injects global and local information into different levels. It avoids the inherent information loss of traditional feature pyramid networks and strengthens the information fusion ability without significantly increasing latency. The proposed intersection-of-union considers the center distance and scale of small ships specifically. It reduces the sensitivity of intersection-of-union to positional deviations of small ships, which is helpful for training toward small ships. During training, the smaller the localization loss of small ships, the greater their localization loss gains are. By this, the supervision of small ships is strengthened in the loss function, which can make the losses more biased toward small ships. A series of experiments are conducted on two commonly used datasets, SSDD and SAR-Ship-Dataset. The experimental results show that the proposed method can detect small ships successfully and thus improve the overall performance of detectors.

Data Matters: Rethinking the Data Distribution in Semi-Supervised Oriented SAR Ship Detection

Data Matters: Rethinking the Data Distribution in Semi-Supervised Oriented SAR Ship Detection

MFFC-SDN: multi-level feature fusion codec-based ship detection network in SAR images

ABSTRACT In recent years, with the development of deep learning, SAR image-based ship target-detection technology has become a current research hotspot. SAR images are characterized by complex backgrounds, significant speckle noise interference, and poor interpretability. To address this challenge, this study proposes a new Gaussian circle radius determination strategy and a multi-level feature fusion codec-based ship detection network (MFFC-SDN) for SAR images. Differing from CenterNet’s corner-oriented Gaussian circle strategy, the Gaussian circle strategy proposed in this paper takes the centre point coordinates of the real target as the origin to define a circular region. This approach allows for more precise supervision of the training model. Meanwhile, we propose MFFC-SDN to solve the problem of the poor performance of CenterNet in SAR ship detection. MFFC-SDN is an anchor-free, single-stage target-detection method with a low computation cost. In MFFC-SDN, we introduce a Multilevel Feature Recapture Codec Module (MFR-CM) to enhance the feature extraction capability of the network by using the features of the encoding stage twice because of the easy loss of image features in the lengthy codec process of the CenterNet network backbone. A Residual Attention Pyramid Feature Fusion Module (RA-PFFM) is designed to enhance the feature map and obtain multi-scale features. Experimental results on the SSDD and SAR-ship-dataset show that MFFC-SDN significantly enhances SAR image target-detection performance, effectively addressing detection challenges related to large target size variations and complex environmental conditions. Compared with existing algorithms, MFFC-SDN achieves the highest A P 50 .

SAR-ShipSwin: enhancing SAR ship detection with robustness in complex environment

SAR-ShipSwin: enhancing SAR ship detection with robustness in complex environment

FS-YOLO: a multi-scale SAR ship detection network in complex scenes

FS-YOLO: a multi-scale SAR ship detection network in complex scenes

Scattering-Point-Guided Oriented RepPoints for Ship Detection

Ship detection finds extensive applications in fisheries management, maritime rescue, and surveillance. However, detecting nearshore targets in SAR images is challenging due to land scattering interference and non-axisymmetric ship shapes. Existing SAR ship detection models struggle to adapt to oriented ship detection in complex nearshore environments. To address this, we propose an oriented-reppoints target detection scheme guided by scattering points in SAR images. Our method deeply integrates SAR image target scattering characteristics and designs an adaptive sample selection scheme guided by target scattering points. This incorporates scattering position features into the sample quality measurement scheme, providing the network with a higher-quality set of proposed reppoints. We also introduce a novel supervised guidance paradigm that uses target scattering points to guide the initialization of reppoints, mitigating the influence of land scattering interference on the initial reppoints quality. This achieves adaptive feature learning, enhancing the quality of the initial reppoints set and the performance of object detection. Our method has been extensively tested on the SSDD and HRSID datasets, where we achieved mAP scores of 89.8% and 80.8%, respectively. These scores represent significant improvements over the baseline methods, demonstrating the effectiveness and robustness of our approach. Additionally, our method exhibits strong anti-interference capabilities in nearshore detection and has achieved state-of-the-art performance.

FESAR: SAR ship detection model based on local spatial relationship capture and fused convolutional enhancement

FESAR: SAR ship detection model based on local spatial relationship capture and fused convolutional enhancement

A Lightweight SAR Image Ship Detection Method Based on Improved Convolution and YOLOv7

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposed an attention mechanism for multi-scale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), was proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our method is more lightweight, only 18.43 M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy (AP50) of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 2.6% and 3.9% higher than that of YOLOv7, respectively.

SAR ship detection network based on global context and multi-scale feature enhancement

SAR ship detection network based on global context and multi-scale feature enhancement

SAR ship detection through generative knowledge transfer

SAR ship detection through generative knowledge transfer

SAR Ship Detection Based on Explainable Evidence Learning Under Intraclass Imbalance

SAR Ship Detection Based on Explainable Evidence Learning Under Intraclass Imbalance

AMANet: Advancing SAR Ship Detection With Adaptive Multi-Hierarchical Attention Network

AMANet: Advancing SAR Ship Detection With Adaptive Multi-Hierarchical Attention Network

A Real-Time SAR Ship Detection Method Based on Improved CenterNet for Navigational Intent Prediction

A Real-Time SAR Ship Detection Method Based on Improved CenterNet for Navigational Intent Prediction

A Domain Adaptive Few-Shot SAR Ship Detection Algorithm Driven by the Latent Similarity between Optical and SAR Images

A Domain Adaptive Few-Shot SAR Ship Detection Algorithm Driven by the Latent Similarity between Optical and SAR Images

Adaptive Multiscale Reversible Column Network for SAR Ship Detection

Adaptive Multiscale Reversible Column Network for SAR Ship Detection

GMDR-Net: A Lightweight OBB-Based SAR Ship Detection Model Based on Gaussian Mixture Data Augmentation and Distance Rotation IOU Loss

GMDR-Net: A Lightweight OBB-Based SAR Ship Detection Model Based on Gaussian Mixture Data Augmentation and Distance Rotation IOU Loss

A Zero-Shot NAS Method for SAR Ship Detection Under Polynomial Search Complexity

A Zero-Shot NAS Method for SAR Ship Detection Under Polynomial Search Complexity

GS-YOLO: A Lightweight SAR Ship Detection Model Based on Enhanced GhostNetV2 and SE Attention Mechanism

GS-YOLO: A Lightweight SAR Ship Detection Model Based on Enhanced GhostNetV2 and SE Attention Mechanism