Network For Ship Detection Research Articles

Few-Shot PolSAR Ship Detection Based on Polarimetric Features Selection and Improved Contrastive Self-Supervised Learning

Deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection over the past few years. However, the backscattering of manmade targets, including ships, is sensitive to the relative geometry between target orientation and radar line of sight, which makes the diversity of polarimetric and spatial features of ships. The diversity of scattering leads to a relative increase in the scarcity of PolSAR-labeled samples, which are difficult to obtain. To solve the abovementioned issue and extract the polarimetric and spatial features of PolSAR images better, this paper proposes a few-shot PolSAR ship detection method based on the combination of constructed polarimetric input data selection and improved contrastive self-supervised learning (CSSL) pre-training. Specifically, eight polarimetric feature extraction methods are adopted to construct deep learning network input data with polarimetric features. The backbone is pre-trained with un-labeled PolSAR input data through an improved CSSL method without negative samples, which enhances the representation capability by the multi-scale feature fusion module (MFFM) and implements a regularization strategy by the mix-up auxiliary pathway (MUAP). The pre-trained backbone is applied to the downstream ship detection network; only a few labeled samples are used for fine-tuning and the construction method of polarimetric input data with the best detection effect is studied. The comparison and ablation experiment results on the self-established PolSAR ship detection dataset verify the superiority of the proposed method, especially in the case of few-shot learning.

Lightweight algorithm for multi-scale ship detection based on high-resolution SAR images

ABSTRACT As ship target detection technology has high application value in military and civil fields, it is significant to research ship detection in SAR images. Aiming at the complex and diverse backgrounds, significant differences in ship sizes, and real-time detection problems in the ship target detection task of SAR remote sensing images, a lightweight ship detection network based on the YOLOx-Tiny model is proposed. Firstly, a multi-scale ship feature extraction module is proposed, composed of a parallel multi-branch structure connected by a standard convolution layer, asymmetric convolution layer, and dilatation convolution layer with different expansion rates in turn. It makes better use of local features and global features and effectively improves the detection accuracy of multi-scale ship targets; Secondly, to ensure detection performance and eliminate background interference, we propose a whole SAR remote sensing image detection strategy based on an adaptive threshold, which effectively suppresses false alarms caused by background and improves detection speed. The experimental results on two different SAR ship datasets, SSDD and HRSID, show that, compared with several advanced methods, the effectiveness and superiority of the method in this paper are verified, and excellent results are shown in the detection of the whole SAR remote-sensing image. It can provide effective theoretical and technical support for ship detection on platforms with limited computing resources and has good application prospects.

Physics Guided Remote Sensing Image Synthesis Network for Ship Detection

Automatic detection and localization of objects in remote sensing images are of great significance for remote sensing systems. Existing frameworks usually train an object detection network using collected remote-sensing images. However, these models usually perform poorly due to the lack of large-scale training datasets, which is often the case for special remote sensing scenarios, e.g., the detection of ships in the open sea. Although image synthesis is a common strategy to alleviate the issue of data insufficiency, the trained model still performs poorly when being tested on real-world scenes. Aimed at this, a novel sensor-related image synthesis framework, dubbed as RS-ISP, is developed to address the lack of on-orbit remote sensing images. Specifically, our RS-ISP introduces two novel designs to ensure the distribution consistency between the generated images and the real images. 1) The first is a novel pipeline for modeling the physical process of noise production during image capture using specific sensors. 2) The second is the design of a detection-oriented image harmonization model. Similar to the existing design, our model first produces coarse synthetic images by copy-paste operation, on which the proposed harmonization process is used to reduce the variation of the pasted foreground and background. By incorporating these two designs into a unified framework, our RS-ISP is designed and used to produce large-scale synthetic images used to train the object detection model for detecting ships in remote sensing images. Comparative experiments demonstrated that RS-ISP increased the AP@.50 from 0.148 to 0.498 for the ship detection task. Code will be publicly available.

Semisupervised SAR Ship Detection Network via Scene Characteristic Learning

In recent years, target detection methods based on deep learning have achieved extensive development in synthetic aperture radar (SAR) ship detection. However, training such detectors requires target-level annotations of SAR images that are hard to be obtained in practice. To reduce the dependence of network training on expensive target-level annotations, we propose a novel semisupervised SAR ship detection network via scene characteristic learning. The proposed network focuses on utilizing the scene-level annotations of SAR images to improve the detection performance in the case of limited target-level annotations. Compared with the traditional fully supervised SAR ship detection network, the proposed network constructs a scene characteristic learning branch parallel with the detection branch. In the scene characteristic learning branch, a scene classification loss and a scene aggregation loss are designed to utilize the scene-level annotations. Under the constraint of these two losses, the feature extraction network can fully learn the scene characteristics of SAR images, thus enhancing its feature representation ability for ship targets and clutter. In addition, we propose a hierarchical test process from scene to target. After recognizing the scene types of input SAR images, we design different detection strategies for SAR images recognized as different scenes. The proposed test process can significantly reduce the inland and inshore false alarms, thus leading to higher detection performance. The experiments based on two measured SAR ship detection datasets demonstrate the effectiveness of the proposed method.

A Semisupervised Arbitrary-Oriented SAR Ship Detection Network Based on Interference Consistency Learning and Pseudolabel Calibration

A Semisupervised Arbitrary-Oriented SAR Ship Detection Network Based on Interference Consistency Learning and Pseudolabel Calibration

A Sidelobe-Aware Small Ship Detection Network for Synthetic Aperture Radar Imagery

Ship detection from synthetic aperture radar (SAR) remote sensing images is essential for monitoring water traffic and marine safety. Numerous methods for ship detection have been developed; however, the detection of small ships presents unique challenges. SAR image characteristics, such as the sidelobe effect and blurred outline induced by the special imaging mechanism, as well as the small ship size, are the primary factors that lower the detection accuracy. This paper provides a sidelobe-aware small ship detection network for synthetic aperture radar imagery. First, considering the sidelobe effect and blurred outline, dual-pooling, i.e., average pooling and max pooling, was utilized to build a feature extraction module that lowered the effects of strong scattering points outside of the ship body and enhanced the ship body information. Second, as the bipartition process of the average pooling and maximum pooling caused some loss of original data information, different feature maps in the network were concatenated to construct a new network structure to compensate for the information lost and enrich the small ship features. Third, because the traditional loss function based on centroid distance and aspect ratio may result in the same loss function value for different prediction box sizes, a novel loss function based on the dual Euclidean distances of the corner point coordinates between the prediction box and the real box was proposed, which could accurately describe various overlapping box situations. Experiments using the Large-Scale SAR Ship Detection Dataset (LS-SSDD), SAR Ship Detection Dataset (SSDD), and AIR-SARShip dataset validated the efficacy and state-of-the-art performance.

A Multiscale Feature Pyramid SAR Ship Detection Network With Robust Background Interference

A Multiscale Feature Pyramid SAR Ship Detection Network With Robust Background Interference

Scattering Characteristic-Aware Fully Polarized SAR Ship Detection Network Based on a Four-Component Decomposition Model

Scattering Characteristic-Aware Fully Polarized SAR Ship Detection Network Based on a Four-Component Decomposition Model

Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection

Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them to microwave synthetic aperture radar (SAR) images. First, most of the regions in the inshore scene include scattered spots and noises, which dramatically interfere with ship detection. Besides, SAR ship images contain ship targets of different sizes, especially small ships with dense distribution. Unfortunately, small ships have fewer distinguishing features making it difficult to be detected. In this article, we propose a novel SAR ship detection network called feature enhanced pyramid and shallow feature reconstruction network (FEPS-Net) to solve the above problems. We design a feature enhancement pyramid, which includes a spatial enhancement module to enhance spatial position information and suppress background noise, and the feature alignment module to solve the problem of feature misalignment during feature fusion. Additionally, to solve the problem of small ship detection in SAR ship images, we design a shallow feature reconstruction module to extract semantic information from small ships. The effectiveness of the proposed network for SAR ship detection is demonstrated by experiments on two publicly available datasets: SAR ship detection dataset and high-resolution SAR images dataset. The experimental results show that the proposed FEPS-Net has advantages in SAR ship detection over the current state-of-the-art methods.

MLBR-YOLOX: An Efficient SAR Ship Detection Network With Multilevel Background Removing Modules

On the remote sensing images of marine synthetic aperture radar (SAR), ship targets often occupy only a small part of an image, and the rest are all sea and coastal backgrounds. Existing neural networks based on SAR ship detection often directly detect an entire SAR image, which consumes a large amount of computing resources. In this article, a new marine SAR ship detection network, called multilevel background removing–you only look once X (MLBR-YOLOX), is proposed. First, a new plug-and-play module, called standalone spatial patch detector, is proposed to predetect the position of ships and filter out most of the sea backgrounds at the source image level. Second, a deep spatial feature detector is presented for detecting deep semantic features of the output of the backbone module to further reduce the computational cost of the neck and head modules. Finally, the original YOLOX network is adopted to locate and classify the pre-detected results. Experimental results on the SAR ship detection dataset and the high-resolution SAR images dataset indicate that the detection performance of MLBR-YOLOX is close to that of YOLOX, but the computational complexity is merely 23.97% and 12.50% of YOLOX's, respectively. Moreover, the experiment conducted on a large-scene Sentinel-1 SAR image illustrates that the proposed network has good migration application capability.

Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion

Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion

A Group-Wise Feature Enhancement-and-Fusion Network with Dual-Polarization Feature Enrichment for SAR Ship Detection

Ship detection in synthetic aperture radar (SAR) images is a significant and challenging task. However, most existing deep learning-based SAR ship detection approaches are confined to single-polarization SAR images and fail to leverage dual-polarization characteristics, which increases the difficulty of further improving the detection performance. One problem that requires a solution is how to make full use of the dual-polarization characteristics and how to excavate polarization features using the ship detection network. To tackle the problem, we propose a group-wise feature enhancement-and-fusion network with dual-polarization feature enrichment (GWFEF-Net) for better dual-polarization SAR ship detection. GWFEF-Net offers four contributions: (1) dual-polarization feature enrichment (DFE) for enriching the feature library and suppressing clutter interferences to facilitate feature extraction; (2) group-wise feature enhancement (GFE) for enhancing each polarization semantic feature to highlight each polarization feature region; (3) group-wise feature fusion (GFF) for fusing multi-scale polarization features to realize polarization features’ group-wise information interaction; (4) hybrid pooling channel attention (HPCA) for channel modeling to balance each polarization feature’s contribution. We conduct sufficient ablation studies to verify the effectiveness of each contribution. Extensive experiments on the Sentinel-1 dual-polarization SAR ship dataset demonstrate the superior performance of GWFEF-Net, with 94.18% in average precision (AP), compared with the other ten competitive methods. Specifically, GWFEF-Net can yield a 2.51% AP improvement compared with the second-best method.

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.

LSDNet: Lightweight CNN Model Driven by PNF for PolSAR Image Ship Detection

Polarimetric synthetic aperture radar (PolSAR) as an active microwave imaging device employing pulse compression technology to obtain echo information of multiple polarization channels has been widely used in marine target detection. However, the detection of ship targets in PolSAR images is often disturbed by clutter interference, such as side lobes, ghost ships, etc. Additionally, the current common convolutional neural network object detection models are accompanied by a large number of parameters, thereby, their performance is greatly limited by available datasets. Therefore, aiming at solving the above questions, we propose a lightweight ship detection model driven by a polarimetric notch filter (PNF) feature for PolSAR ship detection. First, considering the scattering mechanism of target and clutter, the PNF feature is constructed with geometric perturbation information to overcome the vulnerability of existing amplitude feature-driven-based detectors to clutter interferences. Then, a lightweight ship detection network (LSDNet) is proposed, which uses a limited multilayer convolutional structure supported by a small-scale dataset and introduces dilated convolution during the extraction of high-level features to achieve spatial feature perception of weak and small ship targets. Finally, combined with the region-based fully convolutional head network, the model parameter is further reduced while training and inference efficiency is accelerated. Experimental results on a substantial number of PolSAR images show that the PNF-driven LSDNet model is better than the state-of-the-art detection models in terms of accuracy and efficiency, whose parameter quantity is 41.04% less than that of Faster R-CNN and achieves 0.92 of F1 Score and 0.9683 of average precision.

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.

Feature Enhanced Fully Convolutional Network for Ship Detection

Focusing on the problem of high false detection and missed detection in the existing ship detection methods, which is affected by the background noise on sea surface and the various scales of different types of ships, a novel feature enhanced fully convolutional network is proposed for ship detection. First, a multi-scale feature enhancement mechanism is introduced to improve the feature descriptive power of the model for ships. In each single-scale feature extraction stage, the statistical information is calculated and utilized to suppress the interference of background noise, such as wave and clutter, on the feature description of ships. Meanwhile, in the multi-scale feature fusion stage, two steps feature adaptive fusion strategy is utilized to improve the network awareness of multi-scale ships. Furthermore, an improved object detection head network is devised under the regression analysis framework, which optimizes the calculation of centerness through an attention enhancement mechanism in regression branch to better suppress the low-quality bounding boxes, and improve ship detection performance while ensuring detection efficiency. The mean average precision of the proposed method reaches 91.6% on the large-scale maritime surveillance dataset SeaShips, and the detection speed is 9 frames per second. Experimental results demonstrate the proposed method has good detection accuracy and robustness.

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.

Fully Deformable Convolutional Network for Ship Detection in Remote Sensing Imagery

In high spatial resolution remote sensing imagery (HRSI), ship detection plays a fundamental role in a wide variety of applications. Despite the remarkable progress made by many methods, ship detection remains challenging due to the dense distribution, the complex background, and the huge differences in scale and orientation of ships. To address the above problems, a novel, fully deformable convolutional network (FD-Net) is proposed for dense and multiple-scale ship detection in HRSI, which could effectively extract features at variable scales, orientations and aspect ratios by integrating deformable convolution into the entire network structure. In order to boost more accurate spatial and semantic information flow in the network, an enhanced feature pyramid network (EFPN) is designed based on deformable convolution constructing bottom-up feature maps. Additionally, in considering of the feature level imbalance in feature fusion, an adaptive balanced feature integrated (ABFI) module is connected after EFPN to model the scale-sensitive dependence among feature maps and highlight the valuable features. To further enhance the generalization ability of FD-Net, extra data augmentation and training methods are jointly designed for model training. Extensive experiments are conducted on two public remote sensing datasets, DIOR and DOTA, which then strongly prove the effectiveness of our method in remote sensing field.

Multi-Scale Ship Detection Algorithm Based on a Lightweight Neural Network for Spaceborne SAR Images

The current limited spaceborne hardware resources and the diversity of ship target scales in SAR images have led to the requirement of on-orbit real-time detection of ship targets in spaceborne synthetic aperture radar (SAR) images. In this paper, we propose a lightweight ship detection network based on the YOLOv4-LITE model. In order to facilitate the network migration to the satellite, the method uses MobileNetv2 as the backbone feature extraction network of the model. To solve the problem of ship target scale diversity in SAR images, an improved receptive field block (RFB) structure is introduced, enhancing the feature extraction ability of the network, and improving the accuracy of multi-scale ship target detection. A sliding window block method is designed to detect the whole SAR image, which can solve the problem of image input. Experiments on the SAR ship dataset SSDD show that the detection speed of the improved lightweight network could reach up to 47.16 FPS, with the mean average precision (mAP) of 95.03%, and the model size is only 49.34 M, which demonstrates that the proposed network can accurately and quickly detect ship targets. The proposed network model can provide a reference for constructing a spaceborne real-time lightweight ship detection network, which can balance the detection accuracy and speed of the network.

FIAD net: a Fast SAR ship detection network based on feature integration attention and self-supervised learning

ABSTRACT Synthetic Aperture Radar (SAR) Ship Detection (SSD) is an important application, and it has been widely used in commercial and military fields. With the development of remote sensing technology, the use of a large amount of increasing unlabeled data is a challenge for model training, and the interference caused by the reefs and the nearshore facilities is also a challenge for SAR ship detection. In this work, we propose a Feature Integration Attention Detection (FIAD) Network to detect ships in SAR images, namely the FIAD Net. The FIAD Net has two contributions: a ModiBYOL method based on self-supervised learning and a novel Feature Integration Attention (FIA) module. The FIA module could enhance the feature learning ability of the backbone network, and the ModiBYOL could utilize the unlabeled data as a source for backbone pre-training. Experiments on the OpenSARShip dataset, the SAR ship detection dataset (SSDD), and the SAR Ship dataset show that the proposed method achieves an accuracy comparable to the state-of-art methods with a speed of 47 Frames Per Second (FPS). Besides, the effectiveness of the proposed modules is studied through an ablation study.