Ship Detection In Synthetic Aperture Radar Images Research Articles

Scene-aware data augmentation for ship detection in SAR images

ABSTRACT Ship detection in synthetic aperture radar (SAR) images is widely applied in marine monitoring. In recent years, convolutional neural networks (CNNs) have made significant advances in SAR ship detection. However, the scene imbalance problem, i.e. much more offshore scenes than inshore scenes in SAR images, severely hampers the improvement of detection accuracy. In this article, we propose scene-aware data augmentation to address the problem. First, a novel concept, inshore rate, is introduced to estimate the land area percentage of various scenes in SAR images. We can divide image samples into inshore and offshore ones based on the inshore rate without manual category annotations. Second, to augment inshore samples, we synthesize dummy inshore images by cropping the ground truth bounding boxes (GT bboxes) from offshore images and pasting them onto inshore images. This offline data augmentation strategy can address the scene imbalance problem and improve detection accuracy. The experiments conducted on SAR Ship Detection Dataset (SSDD) and Large-Scale SAR Ship Detection Dataset (LS-SSDD) demonstrate the effectiveness of the proposed method in improving detection performance.

A new method of ship detection in complex background of SAR images based on YOLO

Synthetic Aperture Radar (SAR) is a remote sensing technology capable of high-resolution imaging of the ground surface under any weather and light conditions. SAR image ship detection is a technique that uses the ship target features in SAR images to identify and locate ships, which is of great value for applications in the fields of maritime traffic management, marine environment monitoring, and maritime security and defence. However, SAR image ship detection also faces some challenges, such as complex sea surface background, low signal-to-noise ratio, and the diversity and density of ship targets, which lead to the shortcomings of traditional feature extraction and classifier-based detection methods in terms of accuracy and efficiency. To address these challenges, this paper proposes a new method for ship detection in SAR images based on YOLOv7. When dealing with SAR images containing clutter interference and complex backgrounds, many models face the dual challenge of insufficient real-time and accuracy. To address this problem, this study proposes a new PCAN aggregation network, which firstly introduces the SimAM attention mechanism, which significantly enhances the model's ability to identify the differences between ships and background clutter, further improves the ability of focusing on target features, and effectively It further improves the ability to focus on target features, effectively reduces the interference of background noise, and improves the detection accuracy in complex scenes. At the same time, the PConv strategy is added to further lighten the model structure, thus accelerating the computation process and improving the inference efficiency. We trained and tested the model on the SAR-Ship-Dataset dataset, and achieved an accuracy of 91.1%, an F1 score of 92.9, and an FPS of 46, demonstrating its excellent detection capability. By comparing and analysing the model with several classical and cutting-edge methods in the current field, the proposed model in this study demonstrates obvious performance advantages, and in addition, through ablation experiments, we further confirm the effectiveness and contribution of PCAN.

A CFAR-Enhanced Ship Detector for SAR Images Based on YOLOv5s

Ship detection and recognition in Synthetic Aperture Radar (SAR) images are crucial for maritime surveillance and traffic management. Limited availability of high-quality datasets hinders in-depth exploration of ship features in complex SAR images. While most existing SAR ship research is primarily based on Convolutional Neural Networks (CNNs), and although deep learning advances SAR image interpretation, it often prioritizes recognition over computational efficiency and underutilizes SAR image prior information. Therefore, this paper proposes YOLOv5s-based ship detection in SAR images. Firstly, for comprehensive detection enhancement, we employ the lightweight YOLOv5s model as the baseline. Secondly, we introduce a sub-net into YOLOv5s, learning traditional features to augment ship feature representation of Constant False Alarm Rate (CFAR). Additionally, we attempt to incorporate frequency-domain information into the channel attention mechanism to further improve detection. Extensive experiments on the Ship Recognition and Detection Dataset (SRSDDv1.0) in complex SAR scenarios confirm our method’s 68.04% detection accuracy and 60.25% recall, with a compact 18.51 M model size. Our network surpasses peers in mAP, F1 score, model size, and inference speed, displaying robustness across diverse complex scenes.

An efficient YOLO for ship detection in SAR images via channel shuffled reparameterized convolution blocks and dynamic head

Synthetic aperture radar (SAR) is a crucial active imaging technology in remote sensing, offering valuable information for applications like climate monitoring, environmental analysis, and ship surveillance. Ship detection in SAR images remains challenging due to diverse vessel types and environmental interference, especially in inshore areas, despite the proven effectiveness of deep learning-based algorithms. This paper presents an efficient deep learning method named you only look once-shuffle reparameterized blocks with dynamic head (YOLO-SRBD) based on YOLOv8. Additionally, post-processing incorporates the soft non-maximum suppression to enhance precision. Experiments conducted on SAR image datasets demonstrate that the proposed method surpasses the original YOLOv8 both qualitatively and quantitatively, highlighting its feasibility for practical applications. The detection accuracy of the proposed YOLO-SRBD in the high resolution SAR images dataset rose from 89.9% to 91.3%, and the average precision increased from 66.7% to 74.3%, showing significant performance enhancement.

A Lightweight Arbitrarily Oriented Detector Based on Transformers and Deformable Features for Ship Detection in SAR Images

Lightweight ship detection is an important application of synthetic aperture radar (SAR). The prevailing trend in recent research involves employing a detection framework based on convolutional neural networks (CNNs) and horizontal bounding boxes (HBBs). However, CNNs with local receptive fields fall short in acquiring adequate contextual information and exhibit sensitivity to noise. Moreover, HBBs introduce significant interference from both the background and adjacent ships. To overcome these limitations, this paper proposes a lightweight transformer-based method for detecting arbitrarily oriented ships in SAR images, called LD-Det, which excels at promptly and accurately identifying rotating ship targets. First, light pyramid vision transformer (LightPVT) is introduced as a lightweight backbone network. Built upon PVT v2-B0-Li, it effectively captures the long-range dependencies of ships in SAR images. Subsequently, multi-scale deformable feature pyramid network (MDFPN) is constructed as a neck network, utilizing the multi-scale deformable convolution (MDC) module to adjust receptive field regions and extract ship features from SAR images more effectively. Lastly, shared deformable head (SDHead) is proposed as a head network, enhancing ship feature extraction with the combination of deformable convolution operations and a shared parameter structure design. Experimental evaluations on two publicly available datasets validate the efficacy of the proposed method. Notably, the proposed method achieves state-of-the-art detection performance when compared with other lightweight methods in detecting rotated targets.

Density-based ship detection in SAR images: Extension to a self-similarity perspective

Nonlocal self-similarity is an important property of Synthetic Aperture Radar (SAR) images to characterize the repetitiveness of features embodied by SAR images within nonlocal areas and has been used for enhancement of SAR images. Existing SAR ship detectors often independently handle small sub-images cropped from a large marine SAR image and do not exploit the nonlocal self-similarity therein. In this paper, we propose a new ship detector from the perspective of nonlocal self-similarity in SAR images to improve the ship detection performance, basically including three stages: prescreening, intra-cue calculation, and inter-cue calculation. In the pre-screening stage, we design a new Histogram-based Density (HD) feature to rapidly select candidate sub-images potentially containing ship targets from a large SAR image. In the intra-cue calculation stage, target cues within a single candidate sub-image are extracted. In the inter-cue calculation stage, thanks to the nonlocal self-similarity among different candidate sub-images in terms of density features, we innovatively extract a weighted superpixel-HD map to obtain accumulated intra-cues across all the candidate sub-images. Finally, for each candidate sub-image, we fuse its inter-cue and intra-cue to obtain final detection results. Experimental results based on real SAR images show that our newly proposed method provides a better target-to-clutter contrast and ship detection performance than those of other state-of-the-art detection approaches.

Ranking Ship Detection Methods Using SAR Images Based on Machine Learning and Artificial Intelligence

We aimed to improve the performance of ship detection methods in synthetic aperture radar (SAR) images by utilizing machine learning (ML) and artificial intelligence (AI) techniques. The maritime industry faces challenges in collecting precise data due to constantly changing sea conditions and weather, which can affect various maritime operations, such as maritime security, rescue missions, and real-time monitoring of water boundaries. To overcome these challenges, we present a survey of AI- and ML-based techniques for ship detection in SAR images that provide a more effective and reliable way to detect and classify ships in a variety of weather conditions, both onshore and offshore. We identified key features frequently used in the existing literature and applied the graph theory matrix approach (GTMA) to rank the available methods. This study’s findings can help users select a quick and efficient ship detection and classification method, improving the accuracy and efficiency of maritime operations. Moreover, the results of this study will contribute to advancing AI- and ML-based techniques for ship detection in SAR images, providing a valuable resource for the maritime industry.

PPA-Net: Pyramid Pooling Attention Network for Multi-Scale Ship Detection in SAR Images

In light of recent advances in deep learning and Synthetic Aperture Radar (SAR) technology, there has been a growing adoption of ship detection models that are based on deep learning methodologies. However, the efficiency of SAR ship detection models is significantly impacted by complex backgrounds, noise, and multi-scale ships (the number of pixels occupied by ships in SAR images varies significantly). To address the aforementioned issues, this research proposes a Pyramid Pooling Attention Network (PPA-Net) for SAR multi-scale ship detection. Firstly, a Pyramid Pooled Attention Module (PPAM) is designed to alleviate the influence of background noise on ship detection while its parallel component favors the processing of multiple ship sizes. Different from the previous attention module, the PPAM module can better suppress the background noise in SAR images because it considers the saliency of ships in SAR images. Secondly, an Adaptive Feature Balancing Module (AFBM) is developed, which can automatically balance the conflict between ship semantic information and location information. Finally, the detection capabilities of the ship detection model for multi-scale ships are further improved by introducing the Atrous Spatial Pyramid Pooling (ASPP) module. This innovative module enhances the detection model’s ability to detect ships of varying scales by extracting features from multiple scales using atrous convolutions and spatial pyramid pooling. PPA-Net achieved detection accuracies of 95.19% and 89.27% on the High-Resolution SAR Images Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD), respectively. The experimental results demonstrate that PPA-Net outperforms other ship detection models.

A Lightweight Radar Ship Detection Framework with Hybrid Attentions

One of the current research areas in the synthetic aperture radar (SAR) processing fields is deep learning-based ship detection in SAR imagery. Recently, ship detection in SAR images has achieved continuous breakthroughs in detection precision. However, determining how to strike a better balance between the precision and complexity of the algorithm is very meaningful for real-time object detection in real SAR application scenarios, and has attracted extensive attention from scholars. In this paper, a lightweight object detection framework for radar ship detection named multiple hybrid attentions ship detector (MHASD) with multiple hybrid attention mechanisms is proposed. It aims to reduce the complexity without loss of detection precision. First, considering that the ship features in SAR images are not inconspicuous compared with other images, a hybrid attention residual module (HARM) is developed in the deep-level layer to obtain features rapidly and effectively via the local channel attention and the parallel self-attentions. Meanwhile, it is also capable of ensuring high detection precision of the model. Second, an attention-based feature fusion scheme (AFFS) is proposed in the model neck to further heighten the features of the object. Meanwhile, AFFS constructs and develops a fresh hybrid attention feature fusion module (HAFFM) upon the local channel and spatial attentions to guarantee the applicability of the detection model. The Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) experimental results demonstrate that MHASD can balance detection speed and precision (improving average precision by 1.2% and achieving 13.7 GFLOPS). More importantly, extensive experiments on the SAR Ship Detection Dataset (SSDD) demonstrate that the proposed method is less affected by the background such as ports and rocks.

Scattering-Point-Guided RPN for Oriented Ship Detection in SAR Images

Ship detection in synthetic aperture radar (SAR) images has attracted widespread attention due to its significance and challenges. In recent years, numerous detectors based on deep learning have achieved good performance in the field of SAR ship detection. However, ship targets of the same type always have various representations in SAR images under different imaging conditions, while different types of ships may have a high degree of similarity, which considerably complicates SAR target recognition. Meanwhile, the ship target in the SAR image is also obscured by background and noise. To address these issues, this paper proposes a novel oriented ship detection method in SAR images named SPG-OSD. First, we propose an oriented two-stage detection module based on the scattering characteristics. Second, to reduce false alarms and missing ships, we improve the performance of the network by incorporating SAR scattering characteristics in the first stage of the detector. A scattering-point-guided region proposal network (RPN) is designed to predict possible key scattering points and make the regression and classification stages of RPN increase attention to the vicinity of key scattering points and reduce attention to background and noise. Third, supervised contrastive learning is introduced to alleviate the problem of minute discrepancies among SAR object classes. Region-of-Interest (RoI) contrastive loss is proposed to enhance inter-class distinction and diminish intra-class variance. Extensive experiments are conducted on the SAR ship detection dataset from the Gaofen-3 satellite, and the experimental results demonstrate the effectiveness of SPG-OSD and show that our method achieves state-of-the-art performance.

Arbitrary-Oriented Ship Detection Method Based on Long-Edge Decomposition Rotated Bounding Box Encoding in SAR Images

Due to the limitations of the horizontal bounding boxes for locating the oriented ship targets in synthetic aperture radar (SAR) images, the rotated bounding box (RBB) has received wider attention in recent years. First, the existing RBB encodings suffer from boundary discontinuity problems, which interfere with the convergence of the model, and then lead to some problems, such as the inaccurate location of the ship targets in the boundary state. Thus, from the perspective that the long-edge features of the ships are more representative of their orientation, the long-edge decomposition RBB encoding has been proposed in this paper, which can avoid the boundary discontinuity problem. Second, the problem of the positive and negative samples imbalance is serious for the SAR ship images because only a few ship targets exist in the vast background of these images. Since the ship targets of different sizes are subject to varying degrees of interference caused by this problem, a multiscale elliptical Gaussian sample balancing strategy has been proposed in this paper, which can mitigate the impact of this problem by labeling the loss weights of the negative samples within the target foreground area with multiscale elliptical Gaussian kernels. Finally, experiments based on the CenterNet model were implemented on the benchmark SAR image dataset SSDD (SAR ship detection dataset). The experimental results demonstrate that our proposed long-edge decomposition RBB encoding outperforms other conventional RBB encodings in the task of oriented ship detection in SAR images. In addition, our proposed multiscale elliptical Gaussian sample balancing strategy is effective and can improve the model performance.

Unsupervised Ship Detection in SAR Images Using Superpixels and CSPNet

Ship detection in synthetic aperture radar (SAR) images is critical to the ocean surveillance and rescue. Although many deep learning SAR ship detection methods have been proposed, the performance of these methods depends on the size and quality of the training samples. To resolve these issues, this letter presents an unsupervised ship detection method in SAR images using superpixel segmentation and cross stage partial network (CSPNet). First, the SAR image is over-segmented into superpixels based on our previously proposed superpixel generation algorithm. Then, the complex signal kurtosis (CSK) and a local superpixel contrast are integrated as a statistical indicator for the automatic identification of ship superpixels and background superpixels, thus leading to generation of training samples. Finally, the segmented superpixels are input to the CSPNet, which can learn a representative feature set with high discrimination ability between ships and backgrounds. Our method can achieve pixel-level detection map rather than the bounding box result. Experiments based on the Gaofen-3 and TerraSAR SAR data demonstrate that our method can achieve above 90% actual detection rate.

Ship Detection in SAR Images Based on Multilevel Superpixel Segmentation and Fuzzy Fusion

Superpixel can maintain the boundary of the target and reduce the influence of speckle noise, which has been widely applied to synthetic aperture radar (SAR) image target detection. But the size of the superpixel has a great impact on the performance of superpixel-based SAR target detection algorithms. To solve this problem, we propose a multi-level ship target detection algorithm based on superpixel segmentation. Firstly, the SAR images are segmented in different levels with different superpixel sizes. Different descriptions of the SAR images are obtained in different levels. Secondly, we determine the feature of the superpixels in each level. And in order to enhance the adaptability of the proposed algorithm, we propose an adaptive distance calculation method to select the contrast superpixels in each level. Thirdly, the soft detection results are realized in each level by using the fuzzy C-means (FCM) algorithm. At last, the soft detection results obtained in different levels are fused by a new fusion strategy to achieve the final ship target detection result. The influences caused by different superxiel sizes can be effectively eased by fusion. Experiments in different SAR images have verified the effectiveness of the proposed algorithm in accurately detecting ship targets and insensitivity to the superpixel size.

YOLO-SD: Small Ship Detection in SAR Images by Multi-Scale Convolution and Feature Transformer Module

As an outstanding method for ocean monitoring, synthetic aperture radar (SAR) has received much attention from scholars in recent years. With the rapid advances in the field of SAR technology and image processing, significant progress has also been made in ship detection in SAR images. When dealing with large-scale ships on a wide sea surface, most existing algorithms can achieve great detection results. However, small ships in SAR images contain little feature information. It is difficult to differentiate them from the background clutter, and there is the problem of a low detection rate and high false alarms. To improve the detection accuracy for small ships, we propose an efficient ship detection model based on YOLOX, named YOLO-Ship Detection (YOLO-SD). First, Multi-Scale Convolution (MSC) is proposed to fuse feature information at different scales so as to resolve the problem of unbalanced semantic information in the lower layer and improve the ability of feature extraction. Further, the Feature Transformer Module (FTM) is designed to capture global features and link them to the context for the purpose of optimizing high-layer semantic information and ultimately achieving excellent detection performance. A large number of experiments on the HRSID and LS-SSDD-v1.0 datasets show that YOLO-SD achieves a better detection performance than the baseline YOLOX. Compared with other excellent object detection models, YOLO-SD still has an edge in terms of overall performance.

A Local-Sparse-Information-Aggregation Transformer with Explicit Contour Guidance for SAR Ship Detection

Ship detection in synthetic aperture radar (SAR) images has witnessed rapid development in recent years, especially after the adoption of convolutional neural network (CNN)-based methods. Recently, a transformer using self-attention and a feed forward neural network with a encoder-decoder structure has received much attention from researchers, due to its intrinsic characteristics of global-relation modeling between pixels and an enlarged global receptive field. However, when adapting transformers to SAR ship detection, one challenging issue cannot be ignored. Background clutter, such as a coast, an island, or a sea wave, made previous object detectors easily miss ships with a blurred contour. Therefore, in this paper, we propose a local-sparse-information-aggregation transformer with explicit contour guidance for ship detection in SAR images. Based on the Swin Transformer architecture, in order to effectively aggregate sparse meaningful cues of small-scale ships, a deformable attention mechanism is incorporated to change the original self-attention mechanism. Moreover, a novel contour-guided shape-enhancement module is proposed to explicitly enforce the contour constraints on the one-dimensional transformer architecture. Experimental results show that our proposed method achieves superior performance on the challenging HRSID and SSDD datasets.

D2ADet: dual-activation domain RetinaNet based on a deformable feature pyramid network for ship detection in SAR images

ABSTRACT RetinaNet has been widely used in the field of ship detection in synthetic aperture radar (SAR) images due to its simple structure and excellent performance. Although various modified RetinaNet models for SAR ship detection have been proposed, there are still some problems, such as how to capture more complete ship features and the unbalanced distribution of positive samples in different tasks, which have not been mentioned in previous studies. Aiming at the above problems, this paper proposes a modified RetinaNet detector for SAR ship detection, namely DADet. Specifically, a modified feature pyramid network is proposed to capture adaptive features that are more suitable for the ship. Then, a dual-activation domain training method is designed to decouple the sample assignment processes of different tasks to meet their requirements of positive samples for each task. Experimental results show that compared with RetinaNet, DADet obtains 7.67, 11.57 and 7.28 gains in AP50, AP75 and mAP on the public SAR ship detection dataset (SSDD), respectively. And DADet achieves the state-of-the-art performance compared with other methods.

Ship Detection in SAR Images via Cross-Attention Mechanism

Deep learning has been widely applied to ship detection in Synthetic Aperture Radar (SAR) images. Unlike optical images, the current object detection methods have the problem of weak feature representation due to the low object resolution in SAR images. In addition, disturbed by chaotic noise, the features of classification and location are prone to significant differences, resulting in classification and location task misalignment. Therefore, this paper proposes a novel SAR ship target detection algorithm based on Cross-Attention Mechanism (CAM), which can establish the information interaction between the classification and localization task and strengthen the correlation between features through attention. In addition, to suppress the noise in multi-scale feature fusion, we designed an Attention-based Feature Fusion Module (AFFM), which uses the attention information between channels to perform the re-weighting operation. This operation can enhance useful feature information and suppress noise information. Experimental results show that on a benchmark SAR Ship Detection Dataset (SSDD), the Fully Convolutional One-Stage Object Detector (FCOS) with ResNet-50 backbone network was optimized to improve AP by 6.5% and computational cost by 0.51%. RetinaNet with ResNet-50 backbone network was optimized to improve AP by 1.8% and computational cost by 0.51%.

A Lightweight Network Based on One-Level Feature for Ship Detection in SAR Images

Recently, deep learning has greatly promoted the development of detection methods for ship targets in synthetic aperture radar (SAR) images. However, existing detection networks are mostly based on large-scale models and high-cost computations, which require high-performance computing equipment to realize real-time processing and limit their hardware transplantation to onboard platforms. To address this problem, a lightweight ship detection network via YOLOX-s is proposed in this paper. Firstly, we remove the computationally heavy pyramidal structure and build a streamlined network based on a one-level feature for higher detection efficiency. Secondly, to expand the limited receptive field and enhance the semantic information of a single-feature map, a residual asymmetric dilated convolution (RADC) block is proposed. Through four branches with different dilation rates, the RADC block can help the detector to capture various ships in complex backgrounds. Finally, to tackle the imbalance problem between ships of different scales in the training stage, we put forward a balanced label assignment strategy called center-based uniform matching. To verify the effectiveness of the proposed method, we conduct extensive experiments on the SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Images Dataset (HRSID). The results show that our method can achieve comparable performance to general detection networks with much less computational cost.

Ship Detection in SAR Images Based on Feature Enhancement Swin Transformer and Adjacent Feature Fusion

Convolutional neural networks (CNNs) have achieved milestones in object detection of synthetic aperture radar (SAR) images. Recently, vision transformers and their variants have shown great promise in detection tasks. However, ship detection in SAR images remains a substantial challenge because of the characteristics of strong scattering, multi-scale, and complex backgrounds of ship objects in SAR images. This paper proposes an enhancement Swin transformer detection network, named ESTDNet, to complete the ship detection in SAR images to solve the above problems. We adopt the Swin transformer of Cascade-R-CNN (Cascade R-CNN Swin) as a benchmark model in ESTDNet. Based on this, we built two modules in ESTDNet: the feature enhancement Swin transformer (FESwin) module for improving feature extraction capability and the adjacent feature fusion (AFF) module for optimizing feature pyramids. Firstly, the FESwin module is employed as the backbone network, aggregating contextual information about perceptions before and after the Swin transformer model using CNN. It uses single-point channel information interaction as the primary and local spatial information interaction as the secondary for scale fusion based on capturing visual dependence through self-attention, which improves spatial-to-channel feature expression and increases the utilization of ship information from SAR images. Secondly, the AFF module is a weighted selection fusion of each high-level feature in the feature pyramid with its adjacent shallow-level features using learnable adaptive weights, allowing the ship information of SAR images to be focused on the feature maps at more scales and improving the recognition and localization capability for ships in SAR images. Finally, the ablation study conducted on the SSDD dataset validates the effectiveness of the two components proposed in the ESTDNet detector. Moreover, the experiments executed on two public datasets consisting of SSDD and SARShip demonstrate that the ESTDNet detector outperforms the state-of-the-art methods, which provides a new idea for ship detection in SAR images.

Generative knowledge transfer for ship detection in SAR images

This paper proposes a generative transfer learning framework consisting of a knowledge transfer network and a ship detection network to address the data acquisition and labeling problem for ship detection in Synthetic Aperture Radar (SAR) images. The knowledge transfer network generates pseudo-SAR images whose spatial distributions are consistent with labeled optical images and feature distributions are similar to SAR images. These pseudo-SAR images are further used to improve the generalization performance of convolutional neural network-based detection models. Experimental results on SAR Ship Detection Dataset (SSDD) and AIR-SARShip-1.0 Dataset confirm that the pseudo-SAR images generated by our method can benefit the final detection prediction even no labeled SAR image is given at the training stage. The proposed framework can also significantly reduce the probability of missing alarms and false positives in complex backgrounds.