Recently,ship detection technology has been applied extensively in the marine security monitoring field.However,achieving accurate marine ship detection still poses significant challenges due to factors such as varyin...Recently,ship detection technology has been applied extensively in the marine security monitoring field.However,achieving accurate marine ship detection still poses significant challenges due to factors such as varying scales,slightly occluded objects,uneven illumination,and sea clutter.To address these issues,we propose a novel ship detection approach,i.e.,the Twin Feature Pyramid Network and Data Augmentation(TFPN-DA),which mainly consists of three modules.First,to eliminate the negative effects of slightly occluded objects and uneven illumination,we propose the Spatial Attention within the Twin Feature Pyramid Network(SA-TFPN)method,which is based on spatial attention to reconstruct the feature pyramid.Second,the ROI Feature Module(ROIFM)is introduced into the SA-TFPN,which is used to enhance specific crucial details from multi-scale features for object regression and classification.Additionally,data augmentation strategies such as spatial affine transformation and noise processing,are developed to optimize the data sample distribution.A self-construct dataset is used to train the detection model,and the experiments conducted on the dataset demonstrate the effectiveness of our model.展开更多
Recently,there has been a widespread application of deep learning in object detection with Synthetic Aperture Radar(SAR).The current algorithms based on Convolutional Neural Networks(CNN)often achieve good accuracy at...Recently,there has been a widespread application of deep learning in object detection with Synthetic Aperture Radar(SAR).The current algorithms based on Convolutional Neural Networks(CNN)often achieve good accuracy at the expense of more complex model structures and huge parameters,which poses a great challenge for real-time and accurate detection of multi-scale targets.To address these problems,we propose a lightweight real-time SAR ship object detector based on detection transformer(LSD-DETR)in this study.First,a lightweight backbone network LCNet containing a stem module and inverted residual structure is constructed to balance the inference speed and detection accuracy of model.Second,we design a transformer encoder with Cascaded Group Attention(CGA Encoder)to enrich the feature information of small targets in SAR images,which makes detection of small-sized ships more precise.Third,an efficient cross-scale feature fusion pyramid module(C3Het-FPN)is proposed through the lightweight units(C3Het)and the introduction of the weighted bidirectional feature pyramid(BiFPN)structure,which realizes the adaptive fusion of multi-scale features with fewer parameters.Ablation experiments and comparative experiments demonstrate the effectiveness of LSD-DETR.The model parameter of LSD-DETR is 8.8 M(only 20.6%of DETR),the model’s FPS reached 43.1,the average detection accuracy mAP50 on the SSDD and HRSID datasets reached 97.3%and 93.4%.Compared to advanced methods,the LSD-DETR can attain superior precision with fewer parameters,which enables accurate real-time object detection of multi-scale ships in SAR images.展开更多
Ship detection in synthetic aperture radar(SAR)image is crucial for marine surveillance and navigation.The application of detection network based on deep learning has achieved a promising result in SAR ship detection....Ship detection in synthetic aperture radar(SAR)image is crucial for marine surveillance and navigation.The application of detection network based on deep learning has achieved a promising result in SAR ship detection.However,the existing networks encounters challenges due to the complex backgrounds,diverse scales and irregular distribution of ship targets.To address these issues,this article proposes a detection algorithm that integrates global context of the images(GCF-Net).First,we construct a global feature extraction module in the backbone network of GCF-Net,which encodes features along different spatial directions.Then,we incorporate bi-directional feature pyramid network(BiFPN)in the neck network to fuse the multi-scale features selectively.Finally,we design a convolution and transformer mixed(CTM)detection head to obtain contextual information of targets and concentrate network attention on the most informative regions of the images.Experimental results demonstrate that the proposed method achieves more accurate detection of ship targets in SAR images.展开更多
Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing f...Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.展开更多
For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional ne...For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional neural network, LiraNet, which combines the idea of dense connections, residual connections and group convolution, including stem blocks and extractor modules.The designed stem block uses a series of small convolutions to extract the input image features, and the extractor network adopts the designed two-way dense connection module, which further reduces the network operation complexity. Mounting LiraNet on the object detection framework Darknet, this paper proposes Lira-you only look once(Lira-YOLO), a lightweight model for ship detection in radar images, which can easily be deployed on the mobile devices. Lira-YOLO's prediction module uses a two-layer YOLO prediction layer and adds a residual module for better feature delivery. At the same time, in order to fully verify the performance of the model, mini-RD, a lightweight distance Doppler domain radar images dataset, is constructed. Experiments show that the network complexity of Lira-YOLO is low, being only 2.980 Bflops, and the parameter quantity is smaller, which is only 4.3 MB. The mean average precision(mAP) indicators on the mini-RD and SAR ship detection dataset(SSDD) reach 83.21% and 85.46%, respectively,which is comparable to the tiny-YOLOv3. Lira-YOLO has achieved a good detection accuracy with less memory and computational cost.展开更多
To dates,most ship detection approaches for single-pol synthetic aperture radar（SAR） imagery try to ensure a constant false-alarm rate（CFAR）.A high performance ship detector relies on two key components：an accura...To dates,most ship detection approaches for single-pol synthetic aperture radar（SAR） imagery try to ensure a constant false-alarm rate（CFAR）.A high performance ship detector relies on two key components：an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR（CA-CFAR）.To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.展开更多
Synthetic Aperture Radar(SAR)image target detection has widespread applications in both military and civil domains.However,SAR images pose challenges due to strong scattering,indistinct edge contours,multi-scale repre...Synthetic Aperture Radar(SAR)image target detection has widespread applications in both military and civil domains.However,SAR images pose challenges due to strong scattering,indistinct edge contours,multi-scale representation,sparsity,and severe background interference,which make the existing target detection methods in low accuracy.To address this issue,this paper proposes a multi-scale fusion framework(Swin-PAFF)for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure(FPN).Firstly,to tackle the issue of inadequate perceptual image context information in SAR target detection,we propose an end-to-end SAR target detection network with the Transformer structure as the backbone.Furthermore,we enhance the ability of the Swin Transformer to acquire contextual features and cross-information by incorporating a Swin-CC backbone network model that combines the Spatial Depthwise Pooling(SDP)module and the self-attentive mechanism.Finally,we design a cross-layer fusion neck module(PAFF)that better handles multi-scale variations and complex situations(such as sparsity,background interference,etc.).Our devised approach yields a noteworthy AP@0.5:0.95 performance of 91.3%when assessed on the HRSID dataset.The application of our proposed technique has resulted in a noteworthy advancement of 8%in the AP@0.5:0.95 scores on the HRSID dataset.展开更多
A new paradigm for ship detection in polarimetric synthetic aperture radar(Pol-SAR)image is presented.We firstly utilize the scattering similarity parameters to investigate the differences of scattering mechanism betw...A new paradigm for ship detection in polarimetric synthetic aperture radar(Pol-SAR)image is presented.We firstly utilize the scattering similarity parameters to investigate the differences of scattering mechanism between ships and sea clutter.Based on these differences,we propose a novel ship detection metric,denoted as the scattering similarity based metric(SSM),to conduct ship detection task.The distribution model of SSM metric is investigated and modeled by kernel density estimation(KDE).Based on the statistical distribution,an adaptive constant false alarm rate(CFAR)detection scheme is implemented.We compare the proposed SSM with two classic polarimetric metrics,i.e.,the polarimetric cross-entropy(PCE)and the reflection symmetry metric(RSM).The experimental results conducted on C-band RADARSAT-2 Pol-SAR data demonstrate the feasibility and advantage of the proposed SSM metric both in sea clutter modeling and in ship detection.展开更多
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...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 prescreening 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 intracues 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.展开更多
Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on i...Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.展开更多
In this paper, we propose a SAR image ship detection model SSE-Ship that combines image context to extend the detection field of view domain and effectively enhance feature extraction information. This method aims to ...In this paper, we propose a SAR image ship detection model SSE-Ship that combines image context to extend the detection field of view domain and effectively enhance feature extraction information. This method aims to solve the problem of low detection rate in SAR images with ship combination and ship fusion scenes. Firstly, we propose STCSPB network to solve the problem of ship and non-ship object fusion by combining image contextual feature information to distinguish ship and non-ship objects. Secondly, we combine SE Attention to enhance the effective feature information and effectively improve the detection accuracy in combined ship driving scenes. Finally, we conducted extensive experiments on two standard base datasets, SAR-Ship and SSDD, to verify the effectiveness and stability of our proposed method. The experimental results show that the SSE-Ship model has P = 0.950, R = 0.946, mAP_0.5:0.95 = 0.656 and FPS = 50 on the SAR-Ship dataset and mAP_0.5 = 0.964 and R = 0.940 on the SSDD dataset.展开更多
An optimization of polarimetric contrast enhancement method is proposed to detect ships with lowship-to-clutter power ratio.The received power is calculated with Kennaugh matrix and an iterative algo-rithm is adopted ...An optimization of polarimetric contrast enhancement method is proposed to detect ships with lowship-to-clutter power ratio.The received power is calculated with Kennaugh matrix and an iterative algo-rithm is adopted to get the optimal polarimetric states.The optimization method depresses the power of o-cean clutter and increases the power of ship signal.With the double effects,the contrast of ship to oceanis dramatically increased.Thus small ship or weak signals of low ship-to-ocean power ratio can easily bedetected.Ship signals can be distinguished from speckle noise using the different variation trend after op-timization,and thus the threshold problem can be avoided.Moreover,the analyses of different ship'sKennaugh matrices give two implications.One is that the results are affected little by choosing differentKennaugh matrices of ships with strong intensity from Synthetic Aperture Radar(SAR)images.The otheris that ship's Kennaugh matrix chosen from real SAR images is more favorable than that of ideal scatter-ing.Finally,the optimization results are confirmed by polarimetric scattering angle and co-polarizationphase difference.展开更多
Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,du...Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,due to the high similarity between the man-made targets near shore and inshore ships,the classical methods are unable to achieve effective detection of inshore ships.To mitigate the influence of onshore ship-like objects,this paper proposes an inshore ship detection method in SAR images by using hybrid features.Firstly,the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images.Then,a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water.On this basis,the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left.Finally,to verify the effectiveness of the proposed method,numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.展开更多
This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase spectrum.The proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of...This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase spectrum.The proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea regions.In order to eliminate the influence of land regions in SAR images,a novel land removing method is explored.The removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these patches.Thus,an appropriate land segmentation threshold is accordingly obtained.Secondly,an automatic ship detector based on phase spectrum is proposed.The proposed detector is free from various idealized assumptions and can accurately detect ships in SAR images.Experimental results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.展开更多
The high coverage and all-weather capabilities of Synthetic Aperture Radar(SAR)image ship detection make it a widely accepted method for maritime ship positioning and identification.However,SAR ship detection faces ch...The high coverage and all-weather capabilities of Synthetic Aperture Radar(SAR)image ship detection make it a widely accepted method for maritime ship positioning and identification.However,SAR ship detection faces challenges such as indistinct ship contours,low resolution,multi-scale features,noise,and complex background interference.This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images,incorporating key structures to enhance performance.The YOLOv8 backbone is replaced by the Slim Backbone(SB),and the Delete Medium-sized Detection Head(DMDH)structure is eliminated to concentrate on shallow features.Dynamically adjusting the convolution kernel weights of the Omni-Dimensional Dynamic Convolution(ODConv)module can result in a reduction in computation and enhanced accuracy.Adjusting the model’s receptive field is done by the Large Selective Kernel Network(LSKNet)module,which captures shallow features.Additionally,a Multi-scale Spatial-Channel Attention(MSCA)module addresses multi-scale ship feature differences,enhancing feature fusion and local region focus.Experimental results on the HRSID and SSDD datasets demonstrate the model’s effectiveness,with a 67.8%reduction in parameters,a 3.4%improvement in AP(average precision)@0.5,and a 5.4%improvement in AP@0.5:0.95 on the HRSID dataset,and a 0.5%improvement in AP@0.5 and 1.7%in AP@0.5:0.95 on the SSDD dataset,surpassing other state-of-the-art methods.展开更多
Ship detection via spaceborne synthetic aperture radar(SAR)has become a research hotspot.However,existing small ship detection methods based on the radar signal domain and SAR image features cannot obtain highly accur...Ship detection via spaceborne synthetic aperture radar(SAR)has become a research hotspot.However,existing small ship detection methods based on the radar signal domain and SAR image features cannot obtain highly accurate results because of the obvious coherent speckle noise at sea and strong reflection interference from near‑shore objects.To resolve the above problems,this study proposes a dual‑domain joint dense multiple small ship target detection method for spaceborne SAR image that simultaneously detects objects in the image and frequency domains.This method uses an attention mechanism module and algorithm structure adjustments to improve the small ship target feature mining ability.In the frequency‑based image generation,extreme signal strength values are detected in the azimuth and range directions,with the results of the two complementing each other to realize dual‑domain joint small ship target detection.The comprehensive qualitative and quantitative evaluation results show that the proposed method can attain a final precision rate of 92.25%and achieve accurate results for SAR ship detection in open‑sea,coastal,and port area ships.The test results for the self‑built SAR small‑ship dataset demonstrate the effectiveness and universality of the method.展开更多
Remote sensing technology has been widely used for marine monitoring.However,due to the limitations of sensor technologies and data sources,effective monitoring of marine ships at night remains challenging.To address ...Remote sensing technology has been widely used for marine monitoring.However,due to the limitations of sensor technologies and data sources,effective monitoring of marine ships at night remains challenging.To address these challenges,our study developed SDGST,a high-resolution glimmer marine ship dataset from SDGSAT-1 satellite and proposed a ship detection and identification method based on the YOLOv5s model,the Glimmer YOLO model.Considering the characteristics of glimmer images,our model has made several effective improvements to the original YOLOv5s model.In particular,the improved model incorporates a new layer for detecting small targets and integrates the CA(Coordinate Attention)mechanism.To enhance the original feature fusion strategy,we introduced BiFPN(Bi-directional Feature Pyramid Network).We also adopted the EIOU Loss function and replaced the initially defined anchors with clustering results,thus improving detection performance.The mean Average Precision(mAP%)reaches 96.7%,which is a 5.1%improvement over the YOLOv5s model.Notably,it significantly improves the detection of small ships.This model demonstrates superior performance in ship detection under glimmer conditions compared to the original YOLOv5s model and other popular target detection models,and may serve as a valuable reference for achieving high-precision nighttime marine monitoring.展开更多
In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficien...In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.展开更多
Obtaining accurate ship positions and headings in remote sensing images plays a crucial role in various applications.However,current deep learning-based methods primarily focus on ship position detection,while the det...Obtaining accurate ship positions and headings in remote sensing images plays a crucial role in various applications.However,current deep learning-based methods primarily focus on ship position detection,while the detection of ship wakes relies on traditional non-deep learning approaches,which often underperform in complex marine environments.We proposed a novel,simple,and efficient method called Point-Vector Net.The proposed method leverages convolutional neural networks(CNN)for feature extraction and subsequently integrates multi-scale features to generate high-resolution feature maps.In the final stage,ship positions and headings are represented using a combination of points and vectors.Comparative experiments with results from automatic identification system(AIS)reports demonstrate that our method achieved impressive performance in two-class ship target detection,with an average precision of 96.4%,recall rate of 94.3%,and an F 1 score of 95.2%.Notably,the average heading error was 3.3°.The proposed model achieved a practical inference speed(FPS>30),and the average processing time for inferring a large-scale Sentinel-2 remote sensing image was 11.4 s.展开更多
文摘Recently,ship detection technology has been applied extensively in the marine security monitoring field.However,achieving accurate marine ship detection still poses significant challenges due to factors such as varying scales,slightly occluded objects,uneven illumination,and sea clutter.To address these issues,we propose a novel ship detection approach,i.e.,the Twin Feature Pyramid Network and Data Augmentation(TFPN-DA),which mainly consists of three modules.First,to eliminate the negative effects of slightly occluded objects and uneven illumination,we propose the Spatial Attention within the Twin Feature Pyramid Network(SA-TFPN)method,which is based on spatial attention to reconstruct the feature pyramid.Second,the ROI Feature Module(ROIFM)is introduced into the SA-TFPN,which is used to enhance specific crucial details from multi-scale features for object regression and classification.Additionally,data augmentation strategies such as spatial affine transformation and noise processing,are developed to optimize the data sample distribution.A self-construct dataset is used to train the detection model,and the experiments conducted on the dataset demonstrate the effectiveness of our model.
基金National Nature Science Foundation of China(No.U24A20589)National Key Research and Development Program of China(No.2023YFB3905504)+1 种基金Innovation Team of the Ministry of Education of China(No.8091B042227)Innovation Group of Sichuan Natural Science Foundation(No.2023NSFSC1974).
文摘Recently,there has been a widespread application of deep learning in object detection with Synthetic Aperture Radar(SAR).The current algorithms based on Convolutional Neural Networks(CNN)often achieve good accuracy at the expense of more complex model structures and huge parameters,which poses a great challenge for real-time and accurate detection of multi-scale targets.To address these problems,we propose a lightweight real-time SAR ship object detector based on detection transformer(LSD-DETR)in this study.First,a lightweight backbone network LCNet containing a stem module and inverted residual structure is constructed to balance the inference speed and detection accuracy of model.Second,we design a transformer encoder with Cascaded Group Attention(CGA Encoder)to enrich the feature information of small targets in SAR images,which makes detection of small-sized ships more precise.Third,an efficient cross-scale feature fusion pyramid module(C3Het-FPN)is proposed through the lightweight units(C3Het)and the introduction of the weighted bidirectional feature pyramid(BiFPN)structure,which realizes the adaptive fusion of multi-scale features with fewer parameters.Ablation experiments and comparative experiments demonstrate the effectiveness of LSD-DETR.The model parameter of LSD-DETR is 8.8 M(only 20.6%of DETR),the model’s FPS reached 43.1,the average detection accuracy mAP50 on the SSDD and HRSID datasets reached 97.3%and 93.4%.Compared to advanced methods,the LSD-DETR can attain superior precision with fewer parameters,which enables accurate real-time object detection of multi-scale ships in SAR images.
基金supported by the National Science Fund for Distinguished Young Scholars(No.62325104).
文摘Ship detection in synthetic aperture radar(SAR)image is crucial for marine surveillance and navigation.The application of detection network based on deep learning has achieved a promising result in SAR ship detection.However,the existing networks encounters challenges due to the complex backgrounds,diverse scales and irregular distribution of ship targets.To address these issues,this article proposes a detection algorithm that integrates global context of the images(GCF-Net).First,we construct a global feature extraction module in the backbone network of GCF-Net,which encodes features along different spatial directions.Then,we incorporate bi-directional feature pyramid network(BiFPN)in the neck network to fuse the multi-scale features selectively.Finally,we design a convolution and transformer mixed(CTM)detection head to obtain contextual information of targets and concentrate network attention on the most informative regions of the images.Experimental results demonstrate that the proposed method achieves more accurate detection of ship targets in SAR images.
文摘Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.
基金supported by the Joint Fund of Equipment Pre-Research and Aerospace Science and Industry (6141B07090102)。
文摘For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional neural network, LiraNet, which combines the idea of dense connections, residual connections and group convolution, including stem blocks and extractor modules.The designed stem block uses a series of small convolutions to extract the input image features, and the extractor network adopts the designed two-way dense connection module, which further reduces the network operation complexity. Mounting LiraNet on the object detection framework Darknet, this paper proposes Lira-you only look once(Lira-YOLO), a lightweight model for ship detection in radar images, which can easily be deployed on the mobile devices. Lira-YOLO's prediction module uses a two-layer YOLO prediction layer and adds a residual module for better feature delivery. At the same time, in order to fully verify the performance of the model, mini-RD, a lightweight distance Doppler domain radar images dataset, is constructed. Experiments show that the network complexity of Lira-YOLO is low, being only 2.980 Bflops, and the parameter quantity is smaller, which is only 4.3 MB. The mean average precision(mAP) indicators on the mini-RD and SAR ship detection dataset(SSDD) reach 83.21% and 85.46%, respectively,which is comparable to the tiny-YOLOv3. Lira-YOLO has achieved a good detection accuracy with less memory and computational cost.
基金The National Natural Science Foundation of China under contract No.61471024the National Marine Technology Program for Public Welfare under contract No.201505002-1the Beijing Higher Education Young Elite Teacher Project under contract No.YETP0514
文摘To dates,most ship detection approaches for single-pol synthetic aperture radar（SAR） imagery try to ensure a constant false-alarm rate（CFAR）.A high performance ship detector relies on two key components：an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR（CA-CFAR）.To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.
基金supported by the National Key Research and Development Program of China under Grant 2021YFC2801001the Natural Science Foundation of Shanghai under Grant 21ZR1426500the 2022 Graduate Top Innovative Talents Training Program at Shanghai Maritime University under Grant 2022YBR004.
文摘Synthetic Aperture Radar(SAR)image target detection has widespread applications in both military and civil domains.However,SAR images pose challenges due to strong scattering,indistinct edge contours,multi-scale representation,sparsity,and severe background interference,which make the existing target detection methods in low accuracy.To address this issue,this paper proposes a multi-scale fusion framework(Swin-PAFF)for SAR target detection that utilizes the global context perception capability of the Transformer and the multi-layer feature fusion learning ability of the feature pyramid structure(FPN).Firstly,to tackle the issue of inadequate perceptual image context information in SAR target detection,we propose an end-to-end SAR target detection network with the Transformer structure as the backbone.Furthermore,we enhance the ability of the Swin Transformer to acquire contextual features and cross-information by incorporating a Swin-CC backbone network model that combines the Spatial Depthwise Pooling(SDP)module and the self-attentive mechanism.Finally,we design a cross-layer fusion neck module(PAFF)that better handles multi-scale variations and complex situations(such as sparsity,background interference,etc.).Our devised approach yields a noteworthy AP@0.5:0.95 performance of 91.3%when assessed on the HRSID dataset.The application of our proposed technique has resulted in a noteworthy advancement of 8%in the AP@0.5:0.95 scores on the HRSID dataset.
基金The National Natural Science Foundation of China under contract No.61471024the National Marine Technology Program for Public Welfare under contract No.201505002。
文摘A new paradigm for ship detection in polarimetric synthetic aperture radar(Pol-SAR)image is presented.We firstly utilize the scattering similarity parameters to investigate the differences of scattering mechanism between ships and sea clutter.Based on these differences,we propose a novel ship detection metric,denoted as the scattering similarity based metric(SSM),to conduct ship detection task.The distribution model of SSM metric is investigated and modeled by kernel density estimation(KDE).Based on the statistical distribution,an adaptive constant false alarm rate(CFAR)detection scheme is implemented.We compare the proposed SSM with two classic polarimetric metrics,i.e.,the polarimetric cross-entropy(PCE)and the reflection symmetry metric(RSM).The experimental results conducted on C-band RADARSAT-2 Pol-SAR data demonstrate the feasibility and advantage of the proposed SSM metric both in sea clutter modeling and in ship detection.
基金supported by National Key R&D Program of China(No.2021YFA0715201)in part by National Natural Science Foundation of China(Nos.61925106,62022092,and 62101303)in part by Autonomous Research Project of Department of Electronic Engineering at Tsinghua University。
文摘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 prescreening 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 intracues 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.
基金This work was supported by the Outstanding Youth Science and Technology Innovation Team Project of Colleges and Universities in Hubei Province(Grant No.T201923)Key Science and Technology Project of Jingmen(Grant Nos.2021ZDYF024,2022ZDYF019)+2 种基金LIAS Pioneering Partnerships Award,UK(Grant No.P202ED10)Data Science Enhancement Fund,UK(Grant No.P202RE237)Cultivation Project of Jingchu University of Technology(Grant No.PY201904).
文摘Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore waters.Current deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of detection.To solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this paper.The core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship objects.We also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model overfitting.We compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called Seaships.Experiments show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.
文摘In this paper, we propose a SAR image ship detection model SSE-Ship that combines image context to extend the detection field of view domain and effectively enhance feature extraction information. This method aims to solve the problem of low detection rate in SAR images with ship combination and ship fusion scenes. Firstly, we propose STCSPB network to solve the problem of ship and non-ship object fusion by combining image contextual feature information to distinguish ship and non-ship objects. Secondly, we combine SE Attention to enhance the effective feature information and effectively improve the detection accuracy in combined ship driving scenes. Finally, we conducted extensive experiments on two standard base datasets, SAR-Ship and SSDD, to verify the effectiveness and stability of our proposed method. The experimental results show that the SSE-Ship model has P = 0.950, R = 0.946, mAP_0.5:0.95 = 0.656 and FPS = 50 on the SAR-Ship dataset and mAP_0.5 = 0.964 and R = 0.940 on the SSDD dataset.
基金the High Technology Research and Development Programme of China(No.2002AA633120)Sharing and Opening Projects of ENVISAT ASAR Data
文摘An optimization of polarimetric contrast enhancement method is proposed to detect ships with lowship-to-clutter power ratio.The received power is calculated with Kennaugh matrix and an iterative algo-rithm is adopted to get the optimal polarimetric states.The optimization method depresses the power of o-cean clutter and increases the power of ship signal.With the double effects,the contrast of ship to oceanis dramatically increased.Thus small ship or weak signals of low ship-to-ocean power ratio can easily bedetected.Ship signals can be distinguished from speckle noise using the different variation trend after op-timization,and thus the threshold problem can be avoided.Moreover,the analyses of different ship'sKennaugh matrices give two implications.One is that the results are affected little by choosing differentKennaugh matrices of ships with strong intensity from Synthetic Aperture Radar(SAR)images.The otheris that ship's Kennaugh matrix chosen from real SAR images is more favorable than that of ideal scatter-ing.Finally,the optimization results are confirmed by polarimetric scattering angle and co-polarizationphase difference.
基金Aeronautical Science Foundation of China(No.2018ZC51022)。
文摘Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,due to the high similarity between the man-made targets near shore and inshore ships,the classical methods are unable to achieve effective detection of inshore ships.To mitigate the influence of onshore ship-like objects,this paper proposes an inshore ship detection method in SAR images by using hybrid features.Firstly,the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images.Then,a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water.On this basis,the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left.Finally,to verify the effectiveness of the proposed method,numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.
基金China Postdoctoral Science Foundation,grant 2015M582182Fund of Henan Province Young Key Teacher,grant 2017GGJS019+1 种基金foundation of Henan Education Department,grant 19A520002Henan Postdoctoral Foundation,grant 001703007.
文摘This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase spectrum.The proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea regions.In order to eliminate the influence of land regions in SAR images,a novel land removing method is explored.The removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these patches.Thus,an appropriate land segmentation threshold is accordingly obtained.Secondly,an automatic ship detector based on phase spectrum is proposed.The proposed detector is free from various idealized assumptions and can accurately detect ships in SAR images.Experimental results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.
基金supported by the Open Research Fund Program of State Key Laboratory of Maritime Technology and Safety in 2024the National Natural Science Foundation of China(Grant No.52331012)the Natural Science Foundation of Shanghai(Grant No.21ZR1426500).
文摘The high coverage and all-weather capabilities of Synthetic Aperture Radar(SAR)image ship detection make it a widely accepted method for maritime ship positioning and identification.However,SAR ship detection faces challenges such as indistinct ship contours,low resolution,multi-scale features,noise,and complex background interference.This paper proposes a lightweight YOLOv8 model for small object detection in SAR ship images,incorporating key structures to enhance performance.The YOLOv8 backbone is replaced by the Slim Backbone(SB),and the Delete Medium-sized Detection Head(DMDH)structure is eliminated to concentrate on shallow features.Dynamically adjusting the convolution kernel weights of the Omni-Dimensional Dynamic Convolution(ODConv)module can result in a reduction in computation and enhanced accuracy.Adjusting the model’s receptive field is done by the Large Selective Kernel Network(LSKNet)module,which captures shallow features.Additionally,a Multi-scale Spatial-Channel Attention(MSCA)module addresses multi-scale ship feature differences,enhancing feature fusion and local region focus.Experimental results on the HRSID and SSDD datasets demonstrate the model’s effectiveness,with a 67.8%reduction in parameters,a 3.4%improvement in AP(average precision)@0.5,and a 5.4%improvement in AP@0.5:0.95 on the HRSID dataset,and a 0.5%improvement in AP@0.5 and 1.7%in AP@0.5:0.95 on the SSDD dataset,surpassing other state-of-the-art methods.
基金supported by the Foundation Strengthening Fund Project(No.2021-JCJQ-JJ0251)in part by the National Natural Science Foundation of China(Nos.42301384 and 42271448)。
文摘Ship detection via spaceborne synthetic aperture radar(SAR)has become a research hotspot.However,existing small ship detection methods based on the radar signal domain and SAR image features cannot obtain highly accurate results because of the obvious coherent speckle noise at sea and strong reflection interference from near‑shore objects.To resolve the above problems,this study proposes a dual‑domain joint dense multiple small ship target detection method for spaceborne SAR image that simultaneously detects objects in the image and frequency domains.This method uses an attention mechanism module and algorithm structure adjustments to improve the small ship target feature mining ability.In the frequency‑based image generation,extreme signal strength values are detected in the azimuth and range directions,with the results of the two complementing each other to realize dual‑domain joint small ship target detection.The comprehensive qualitative and quantitative evaluation results show that the proposed method can attain a final precision rate of 92.25%and achieve accurate results for SAR ship detection in open‑sea,coastal,and port area ships.The test results for the self‑built SAR small‑ship dataset demonstrate the effectiveness and universality of the method.
基金funded by Operation and Maintenance Project of Big Earth Data Center of the Chinese Academy of Sciences[grant no CAS-WX2022SDC-XK13]Joint HKU-CAS Laboratory for iEarth[grant no 313GJHZ2022074MI].
文摘Remote sensing technology has been widely used for marine monitoring.However,due to the limitations of sensor technologies and data sources,effective monitoring of marine ships at night remains challenging.To address these challenges,our study developed SDGST,a high-resolution glimmer marine ship dataset from SDGSAT-1 satellite and proposed a ship detection and identification method based on the YOLOv5s model,the Glimmer YOLO model.Considering the characteristics of glimmer images,our model has made several effective improvements to the original YOLOv5s model.In particular,the improved model incorporates a new layer for detecting small targets and integrates the CA(Coordinate Attention)mechanism.To enhance the original feature fusion strategy,we introduced BiFPN(Bi-directional Feature Pyramid Network).We also adopted the EIOU Loss function and replaced the initially defined anchors with clustering results,thus improving detection performance.The mean Average Precision(mAP%)reaches 96.7%,which is a 5.1%improvement over the YOLOv5s model.Notably,it significantly improves the detection of small ships.This model demonstrates superior performance in ship detection under glimmer conditions compared to the original YOLOv5s model and other popular target detection models,and may serve as a valuable reference for achieving high-precision nighttime marine monitoring.
文摘In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.
基金Supported by the National Key R&D Program of China(No.2022YFB3902400)the China High Resolution Earth Observation System Program(No.41-Y30F07-9001-20/22)。
文摘Obtaining accurate ship positions and headings in remote sensing images plays a crucial role in various applications.However,current deep learning-based methods primarily focus on ship position detection,while the detection of ship wakes relies on traditional non-deep learning approaches,which often underperform in complex marine environments.We proposed a novel,simple,and efficient method called Point-Vector Net.The proposed method leverages convolutional neural networks(CNN)for feature extraction and subsequently integrates multi-scale features to generate high-resolution feature maps.In the final stage,ship positions and headings are represented using a combination of points and vectors.Comparative experiments with results from automatic identification system(AIS)reports demonstrate that our method achieved impressive performance in two-class ship target detection,with an average precision of 96.4%,recall rate of 94.3%,and an F 1 score of 95.2%.Notably,the average heading error was 3.3°.The proposed model achieved a practical inference speed(FPS>30),and the average processing time for inferring a large-scale Sentinel-2 remote sensing image was 11.4 s.
