Ship Detection Method Research Articles

SAR Remote Sensing Image Ship Detection Method NanoDet Based on Visual Saliency

In the Synthetic Aperture Radar (SAR) remote sensing image, ships are visually significant targets on the sea surface. Because they are made of metal, thus the backscatter is strong, while the sea surface is smooth and the backscatter is weak. However, the large-width SAR remote sensing image has a complicated sea background, and the features of various ship targets are quite different. To solve this problem, a SAR remote sensing image ship detection model called NanoDet is proposed. NanoDet is based on visual saliency. First, the image samples are divided into various scene categories using an automatic clustering algorithm. Second, differentiated saliency detection is performed for images in various scenes. Finally, the optimized lightweight network model, NanoDet, is used to perform feature learning on the training samples added with the saliency maps, so that the system model can achieve fast and high-precision ship detection effects. This method is helpful for the real-time application of SAR images. The lightweight model is conducive to hardware transplantation in the future.This study conducts experiments based on the public data set SSDD and AIR-SARship-2.0, and the experiments results verify the effectiveness of our approach.

PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure.

Machine learning-based ship detection and tracking using satellite images for maritime surveillance

The recent advancement in remote sensing technologies has resulted in the availability of different imaging modes and higher resolution satellite images. Accessibility of these remote sensing or satellite images, automatic ship detection and tracking has become an important research topic in the field of maritime surveillance. In this paper, a novel method for ship detection using satellite images is proposed. First the preprocessing is carried out to remove the noise from the images using Ship Detection and Tracking (SDT) filter. Then, the land masking (sea-land area separation) and cloud masking is carried out based on the gradient feature extraction using SDT edge detection, along with SDT segmentation. Finally, the ships are identified using the Machine Learning (ML) classifiers like Support Vector Machine (SVM), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), KNN, and Gaussian Naïve Bayes-based classifier based on the features extracted from Histogram of Oriented Gradients (HOG). The proposed work is cross validated using the Google earth data. Performance of our proposed method is evaluated using the recall and the precision values. Further, for tracking ships, an improved multiple hypothesis tracking (MHT) algorithm is proposed and tested using the Kaggle dataset.

A Novel Cargo Ship Detection and Directional Discrimination Method for Remote Sensing Image Based on Lightweight Network

Recently, cargo ship detection in remote sensing images based on deep learning is of great significance for cargo ship monitoring. However, the existing detection network is not only unable to realize autonomous operation on spaceborne platforms due to the limitation of computing and storage, but the detection result also lacks the directional information of the cargo ship. In order to address the above problems, we propose a novel cargo ship detection and directional discrimination method for remote sensing images based on a lightweight network. Specifically, we design an efficient and lightweight feature extraction network called the one-shot aggregation and depthwise separable network (OSADSNet), which is inspired by one-shot feature aggregation modules and depthwise separable convolutions. Additionally, we combine the RPN with the K-Mean++ algorithm to obtain the K-RPN, which can produce a more suitable region proposal for cargo ship detection. Furthermore, without introducing extra parameters, the directional discrimination of the cargo ship is transformed into a classification task, and the directional discrimination is completed when the detection task is completed. Experiments on a self-built remote sensing image cargo ship dataset indicate that our model can provide relatively accurate and fast detection for cargo ships (mAP of 91.96% and prediction time of 46 ms per image) and discriminate the directions (north, east, south, and west) of cargo ships, with fewer parameters (model size of 110 MB), which is more suitable for autonomous operation on spaceborne platforms. Therefore, the proposed method can meet the needs of cargo ship detection and directional discrimination in remote sensing images on spaceborne platforms.

Small-Sized Ship Detection Nearshore Based on Lightweight Active Learning Model with a Small Number of Labeled Data for SAR Imagery

Marine ship detection by synthetic aperture radar (SAR) is an important remote sensing technology. The rapid development of big data and artificial intelligence technology has facilitated the wide use of deep learning methods in SAR imagery for ship detection. Although deep learning can achieve a much better detection performance than traditional methods, it is difficult to achieve satisfying performance for small-sized ships nearshore due to the weak scattering caused by their material and simple structure. Another difficulty is that a huge amount of data needs to be manually labeled to obtain a reliable CNN model. Manual labeling each datum not only takes too much time but also requires a high degree of professional knowledge. In addition, the land and island with high backscattering often cause high false alarms for ship detection in the nearshore area. In this study, a novel method based on candidate target detection, boundary box optimization, and convolutional neural network (CNN) embedded with active learning strategy is proposed to improve the accuracy and efficiency of ship detection in nearshore areas. The candidate target detection results are obtained by global threshold segmentation. Then, the strategy of boundary box optimization is defined and applied to reduce the noise and false alarms caused by island and land targets as well as by sidelobe interference. Finally, a lightweight CNN embedded with active learning scheme is used to classify the ships using only a small labeled training set. Experimental results show that the performance of the proposed method for small-sized ship detection can achieve 97.78% accuracy and 0.96 F1-score with Sentinel-1 images in complex nearshore areas.

SAR ship detection in complex background based on multi-feature fusion and non-local channel attention mechanism

ABSTRACT With the development of artificial intelligence (AI) and synthetic aperture radar (SAR) technology, SAR ship target automatic detection has made significant progress. However, due to the complex structure and various sizes of ship, detecting multi-scale ships under complex backgrounds is still challenging. The existing convolution neural networks (CNNs)-based SAR ships detection methods suffer from insufficient feature extraction capabilities and poor feature reusability. To address the issues, an SAR ship detection method based on multi-feature fusion and non-local channel attention mechanism (MFNL-Net) is proposed in this article. MFNL-Net is a one-stage detector and designed to improve the performance of detecting multi-scale ships by enhancing the feature extraction ability and nonlinear relationship between different features. Specially, the non-local channel attention block (NLCA-Block) is employed to enhance the nonlinear relationship between different channel features and the ability of local feature expression; the pooling feature fusion (PFF) module and the deconvolution feature fusion (DFF) module are reasonably combined to obtain abundant ships target feature information and suppress interference caused by surroundings; the multi-scale spatial pyramid pooling (MSPP) is used to boost feature information interaction and reduce the loss of significant feature; the focus classification loss is used to alleviate influences of the positive-negative samples imbalance. To evaluate the effectiveness of MFNL-Net, experiments are conducted on real SAR ship datasets SSDD and GS-Ship. Experimental results show that our method obtains 9.10%, 8.61%, 7.23%, and 4.44% gains in mean average precision (mAP) compared with the four state-of-the-art CNNs-based methods, respectively.

R-CenterNet+: Anchor-Free Detector for Ship Detection in SAR Images.

In recent years, the rapid development of Deep Learning (DL) has provided a new method for ship detection in Synthetic Aperture Radar (SAR) images. However, there are still four challenges in this task. (1) The ship targets in SAR images are very sparse. A large number of unnecessary anchor boxes may be generated on the feature map when using traditional anchor-based detection models, which could greatly increase the amount of computation and make it difficult to achieve real-time rapid detection. (2) The size of the ship targets in SAR images is relatively small. Most of the detection methods have poor performance on small ships in large scenes. (3) The terrestrial background in SAR images is very complicated. Ship targets are susceptible to interference from complex backgrounds, and there are serious false detections and missed detections. (4) The ship targets in SAR images are characterized by a large aspect ratio, arbitrary direction and dense arrangement. Traditional horizontal box detection can cause non-target areas to interfere with the extraction of ship features, and it is difficult to accurately express the length, width and axial information of ship targets. To solve these problems, we propose an effective lightweight anchor-free detector called R-Centernet+ in the paper. Its features are as follows: the Convolutional Block Attention Module (CBAM) is introduced to the backbone network to improve the focusing ability on small ships; the Foreground Enhance Module (FEM) is used to introduce foreground information to reduce the interference of the complex background; the detection head that can output the ship angle map is designed to realize the rotation detection of ship targets. To verify the validity of the proposed model in this paper, experiments are performed on two public SAR image datasets, i.e., SAR Ship Detection Dataset (SSDD) and AIR-SARShip. The results show that the proposed R-Centernet+ detector can detect both inshore and offshore ships with higher accuracy than traditional models with an average precision of 95.11% on SSDD and 84.89% on AIR-SARShip, and the detection speed is quite fast with 33 frames per second.

Domain Adaptive Ship Detection in Optical Remote Sensing Images

With the successful application of the convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where the imaging condition changes significantly. Although training with the two domains together can solve this problem to some extent, the large domain shift will lead to sub-optimal feature representations, and thus weaken the generalization ability on both domains. In this paper, a domain adaptive ship detection method is proposed to better detect ships between different domains. Specifically, the proposed method minimizes the domain discrepancies via both image-level adaption and instance-level adaption. In image-level adaption, we use multiple receptive field integration and channel domain attention to enhance the feature’s resistance to scale and environmental changes, respectively. Moreover, a novel boundary regression module is proposed in instance-level adaption to correct the localization deviation of the ship proposals caused by the domain shift. Compared with conventional regression approaches, the proposed boundary regression module is able to make more accurate predictions via the effective extreme point features. The two adaption components are implemented by learning the corresponding domain classifiers respectively in an adversarial training way, thereby obtaining a robust model suitable for both of the two domains. Experiments on both supervised and unsupervised domain adaption scenarios are conducted to verify the effectiveness of the proposed method.

PAG-YOLO: A Portable Attention-Guided YOLO Network for Small Ship Detection

The YOLO network has been extensively employed in the field of ship detection in optical images. However, the YOLO model rarely considers the global and local relationships in the input image, which limits the final target prediction performance to a certain extent, especially for small ship targets. To address this problem, we propose a novel small ship detection method, which improves the detection accuracy compared with the YOLO-based network architecture and does not increase the amount of computation significantly. Specifically, attention mechanisms in spatial and channel dimensions are proposed to adaptively assign the importance of features in different scales. Moreover, in order to improve the training efficiency and detection accuracy, a new loss function is employed to constrain the detection step, which enables the detector to learn the shape of the ship target more efficiently. The experimental results on a public and high-quality ship dataset indicate that our method realizes state-of-the-art performance in comparison with several widely used advanced approaches.

A deep attention mechanism method for maritime salient ship detection in complex sea background

A deep attention mechanism method for maritime salient ship detection in complex sea background

Ship Detection in SAR Images by Aggregating Densities of Fisher Vectors: Extension to a Global Perspective

Fisher vectors (FVs) can capture multiple order information from superpixels (SPs) in synthetic aperture radar (SAR) images. Existing FV-based ship detectors mainly exploit the local contrast of FVs (LCFVs) but do not consider their global density features. This may lead to degraded performance in terms of discrimination between ship targets and the complex sea clutter. In this article, two new global cues from FVs are designed based on the fact that target FVs exhibit much lower densities than those of clutter FVs and also have large distances to the latter. Our two new global cues can suppress the sea clutter and significantly enhance ship targets throughout the SAR image. We also design an improved local cue from FVs for ship detection, in which the intensity contrast of SPs is incorporated into the existing LCFV indicator to reduce false alarms. By fusing the above two new global cues (and an improved local cue from FVs), we propose a new method for ship detection in SAR images. Experimental results based on Gaofen-3 SAR images show that the newly proposed detector provides better detection performance than other state-of-the-art detectors, especially in the presence of strong and highly heterogeneous sea clutter.

Unsupervised Ship Detection for Single-Channel SAR Images Based on Multiscale Saliency and Complex Signal Kurtosis

Traditional ship detection methods for synthetic aperture radar (SAR) mainly utilize the amplitude information to distinguish ship targets from sea clutter, including constant false alarm rate (CFAR), visual attention model, and deep learning methods. The CFAR algorithms adopt the dense sliding window strategy, which is very time-consuming and may generate numerous false alarms. The deep learning methods are supervised and difficult to obtain satisfactory performance when the number of labeled samples is insufficient. The visual attention models can quickly focus on the potential target area, and however, it is still difficult to eliminate the strong clutter, such as radio frequency interference and azimuth ambiguity. In fact, as a coherent imaging system, SAR data itself are complex-valued. Compared with the amplitude information, complex information can essentially reflect the difference between ship target and sea clutter. To improve the accuracy and efficiency of ship detection, in this letter, a novel unsupervised ship detection method based on multiscale saliency and complex signal kurtosis (MSS-CSK) for single-channel SAR images is proposed, which contains the proposal extraction stage and the target discrimination stage. The experimental results based on the Radarsat-2 real SAR data show that the proposed method has high detection accuracy and efficiency.

A novel Ship detection method from SAR image with reduced false alarm

Many research works using deep learning techniques for automatic ship detection from SAR images have good detection accuracy. But the main problem in these methods is false detection, mostly due to speckle presence. Therefore, we propose a new deep learning model with a novel preprocessing stage to address this problem. We are introducing a deep learning architecture to detect and localize ships in the SAR image. First, generate a three-channel image from gray-scale SAR image. Then, this image is used to train the model to predict the ship’s position in the SAR image. We experimented on the public SAR ship detection dataset (SSDD) and Dataset of Ship Detection for Deep Learning under Complex Backgrounds (SDCD) to validate the proposed method’s feasibility. We used python 3.5 for coding with the Keras framework in the NVIDIA Tesla K80 GPU hardware platform. The experimental results indicated that our proposed method’s ship detection accuracy has increased with reduced false detection percentage.

N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction

High-resolution images provided by synthetic aperture radar (SAR) play an increasingly important role in the field of ship detection. Numerous algorithms have been so far proposed and relative competitive results have been achieved in detecting different targets. However, ship detection using SAR images is still challenging because these images are still affected by different degrees of noise while inshore ships are affected by shore image contrasts. To solve these problems, this paper introduces a ship detection method called N-YOLO, which based on You Only Look Once (YOLO). The N-YOLO includes a noise level classifier (NLC), a SAR target potential area extraction module (STPAE) and a YOLOv5-based detection module. First, NLC derives and classifies the noise level of SAR images. Secondly, the STPAE module is composed by a CA-CFAR and expansion operation, which is used to extract the complete region of potential targets. Thirdly, the YOLOv5-based detection module combines the potential target area with the original image to get a new image. To evaluate the effectiveness of the N-YOLO, experiments are conducted using a reference GaoFen-3 dataset. The detection results show that competitive performance has been achieved by N-YOLO in comparison with several CNN-based algorithms.

Arbitrary-Oriented Ship Detection Based on RetinaNet for Remote Sensing Images

Aiming to address the problems of arbitrary orientations, large aspect ratios, and dense arrangements in ship detection, an arbitrary-oriented ship detection method based on RetinaNet is proposed. Our proposed method includes a rotated RetinaNet, a refined network, a feature alignment module, and an improved loss function. First, the rotated RetinaNet achieves rotation detection by using a feature pyramid network, rotated anchors, the skew intersection-over-union (IoU), and skew nonmaximum suppression. Then, the refined network and feature alignment module are introduced to achieve better detection accuracy. Finally, to address the boundary discontinuity, the loss function is improved by introducing the IoU constant factor. Considering the problems with the HRSC2016 dataset, we establish a new dataset with more accurate labels and more images and object samples. Through an ablation study, we thoroughly analyze the validity of the proposed rotated RetinaNet, feature alignment module, and improved loss function. The experimental results show that our method is superior to other state-of-the-art methods.

End‐to‐End Ship Detection in SAR Images for Complex Scenes Based on Deep CNNs

Ship detection on synthetic aperture radar (SAR) imagery has many valuable applications for both civil and military fields and has received extraordinary attention in recent years. The traditional detection methods are insensitive to multiscale ships and usually time‐consuming, results in low detection accuracy and limitation for real‐time processing. To balance the accuracy and speed, an end‐to‐end ship detection method for complex inshore and offshore scenes based on deep convolutional neural networks (CNNs) is proposed in this paper. First, the SAR images are divided into different grids, and the anchor boxes are predefined based on the responsible grids for dense ship prediction. Then, Darknet‐53 with residual units is adopted as a backbone to extract features, and a top‐down pyramid structure is added for multiscale feature fusion with concatenation. By this means, abundant hierarchical features containing both spatial and semantic information are extracted. Meanwhile, the strategies such as soft non‐maximum suppression (Soft‐NMS), mix‐up and mosaic data augmentation, multiscale training, and hybrid optimization are used for performance enhancement. Besides, the model is trained from scratch to avoid learning objective bias of pretraining. The proposed one‐stage method adopts end‐to‐end inference by a single network, so the detection speed can be guaranteed due to the concise paradigm. Extensive experiments are performed on the public SAR ship detection dataset (SSDD), and the results show that the method can detect both inshore and offshore ships with higher accuracy than other mainstream methods, yielding the accuracy with an average of 95.52%, and the detection speed is quite fast with about 72 frames per second (FPS). The actual Sentinel‐1 and Gaofen‐3 data are utilized for verification, and the detection results also show the effectiveness and robustness of the method.

ROS-Det: Arbitrary-Oriented Ship Detection in High Resolution Optical Remote Sensing Images via Rotated One-Stage Detector

To address these problems, namely, arbitrary orientations, various sizes, and dense distributions of ship detection, we propose an arbitrary-oriented ship detection method via rotated single-stage detector (ROS-Det), which integrates a feature pyramid network (FPN) based on an improved ResNet50, rotated anchors, a classification network, and a regression network together. Firstly, to improve robustness against various sizes of ships, the FPN is used to fusion multiscale convolutional feature maps. Through several tweaks, the improved ResNet50 can receive more information and reduce the computational cost. Secondly, for the purpose of arbitrary-oriented ship detection, rotated anchors, skew intersection over union (IoU), and skew non-maximum suppression (NMS) are introduced to RetinaNet. Then, on account of the disadvantages that the arbitrary-oriented object detection methods usually cause loss discontinuity problem, we improve the traditional smooth L1 loss function by introducing an IoU constant factor. Finally, based on several techniques such as data augmentation and transfer learning, we achieve ship detection on a public ship dataset HRSC2016. Through comparison experiments, we have analyzed and discussed the validity of our proposed ROS-Det, which achieves the state-of-the-art performance.

Scattering-Keypoint-Guided Network for Oriented Ship Detection in High-Resolution and Large-Scale SAR Images

Ship detection in synthetic aperture radar (SAR) images is a significant and challenging task. Recently, deep convolutional neural networks have been applied to solve the detection problem and made a great breakthrough. Previous works mostly rely on the manually designed anchor boxes to search for the region of interests, which is less flexible and suffers from a heavy computational load. Moreover, these detectors have limited performance in large-scale and complex scenes due to the strong interference of inshore background and the variability of object imaging characteristics. In this article, a novel ship detection method based on the scattering-keypoint-guided network is proposed to remedy these problems. First, an anchor-free network is built to eliminate the effect of anchor boxes, in which a more robust representation scheme is designed for the arbitrary oriented objects. Second, a context-aware feature selection module is introduced to dynamically learn both local and context features. In this process, the semantic information of objects can be enhanced while suppressing the background interference. Third, according to the SAR imaging mechanism, a set of scattering keypoints is defined to describe the local scattering regions and reflect the discriminative structural characteristics of ships. Based on this conception, a novel feature adaption method is proposed with the purpose of dealing with the imaging variability issue. Furthermore, to demonstrate the effectiveness of the proposed improvements, we build the Gaofen-3 ship detection dataset. Meanwhile, the public SAR ship detection dataset is introduced to verify the robustness and generalization ability of the detector. Experimental results on these two datasets show that the proposed method achieves the state-of-the-art performance.

Harbor Ship Detection Based on Channel Weighting and Spatial Information Fusion

Aiming at the problems of false alarms and missed detection caused by large differences among ship types and complex background in harbor remote sensing images, a robust single-stage ship detection method is proposed. First, a Channel Weighting Mechanism is devised, which self-learns the weights of different channel to enhance valid features in channel dimension. Second, a Spatial Information Fusion Module is designed to enhance features in spatial dimension, which extracts more information of ship appearance from shallow feature maps, then excavates potential contextual information in deep semantic features. Finally, to promote the detection ability of ships of various scales, a Multi-stage Weighted Fusion Pyramid is applied to optimize the fusion of high-stage features and low-stage features. Extensive experiments conducted on self-established dataset for harbor ships show that the proposed method provides the performance by 3.08%mAP compared to RetinaNet.

An Information-Geometric Optimization Method for Ship Detection in SAR Images

This study aims to investigate the information-geometric optimization method for ship detection in synthetic aperture radar (SAR) images. The detection strategy relies on an optimization-based representation of images, which considers the relationship between local and global statistical information and is more robust to sea clutter. The algorithm has three main stages. First, a constant false alarm rate (CFAR) detector is used to accomplish the initial detection. It is designed to acquire the initial distribution parameters of pure sea clutter and ship targets. Second, a Riemannian metric is constructed according to the local statistical information. Its purpose is to design the energy function of the optimization model. Third, the detection problem is formulated as a maximum decision rule. The experimental results show that the model can effectively detect ships in SAR images, is not sensitive to clutter, and can eliminate the effect of inhomogeneous sea state conditions. Compared with similar algorithms, the proposed algorithm has higher detection precision and good stability in ship detection in SAR images.