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Efficient Ship:A Hybrid Deep Learning Framework for Ship Detection in the River
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作者 Huafeng Chen Junxing Xue +2 位作者 Hanyun Wen Yurong Hu Yudong Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第1期301-320,共20页
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
关键词 ship detection deep learning data augmentation object location object classification
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Swin-PAFF: A SAR Ship Detection Network with Contextual Cross-Information Fusion 被引量:1
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作者 Yujun Zhang Dezhi Han Peng Chen 《Computers, Materials & Continua》 SCIE EI 2023年第11期2657-2675,共19页
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
关键词 TRANSFORMER deep learning SAR object detection ship detection
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SSE-Ship: A SAR Image Ship Detection Model with Expanded Detection Field of View and Enhanced Effective Feature Information
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作者 Liping Zheng Liang Tan +3 位作者 Liangjun Zhao Feng Ning Bo Xiao Yang Ye 《Open Journal of Applied Sciences》 CAS 2023年第4期562-578,共17页
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
关键词 ship detection SSE-ship STCSPB SE Attention
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Lira-YOLO: a lightweight model for ship detection in radar images 被引量:12
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作者 ZHOU Long WEI Suyuan +3 位作者 CUI Zhongma FANG Jiaqi YANG Xiaoting DING Wei 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2020年第5期950-956,共7页
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
关键词 LIGHTWEIGHT radar images ship detection you only look once(YOLO)
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A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection 被引量:3
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作者 LANG Haitao ZHANG Jie +2 位作者 WANG Yiduo ZHANG Xi MENG Junmin 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2016年第9期117-125,共9页
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
关键词 Bézier curve nonparametric method ship detection sea surface distribution synthetic aperture radar
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A new scattering similarity based metric for ship detection in polarimetric synthetic aperture radar image 被引量:2
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作者 Haitao Lang Yunhong Tao +1 位作者 Lihui Niu Hongji Shi 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2020年第5期145-150,共6页
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
关键词 Pol-SAR scattering similarity KDE CFAR ship detection
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Density-based ship detection in SAR images:Extension to a self-similarity perspective
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作者 Xueqian WANG Gang LI +2 位作者 Zhizhuo JIANG Yu LIU You HE 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第3期168-180,共13页
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
关键词 ship detection Synthetic aperture radar(SAR) DENSITY SELF-SIMILARITY HISTOGRAM
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SAR-LtYOLOv8:A Lightweight YOLOv8 Model for Small Object Detection in SAR Ship Images
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作者 Conghao Niu Dezhi Han +1 位作者 Bing Han Zhongdai Wu 《Computer Systems Science & Engineering》 2024年第6期1723-1748,共26页
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
关键词 SAR ship detection MSCA deep learning
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A Hybrid Features Based Detection Method for Inshore Ship Targets in SAR Imagery 被引量:2
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作者 Tong ZHENG Peng LEI Jun WANG 《Journal of Geodesy and Geoinformation Science》 CSCD 2023年第1期95-107,共13页
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
关键词 Convolutional Neural Network(CNN) Synthetic Aperture Radar(SAR) inshore ship detection hybrid features high-energy point number amplitude spectrum
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Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance
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作者 Kyamelia Roy Sheli Sinha Chaudhuri +1 位作者 Sayan Pramanik Soumen Banerjee 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期647-662,共16页
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
关键词 Auto-encoder computer vision deep convolution neural network satellite imagery semantic segmentation ship detection
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Ship detection and classification from optical remote sensing images: A survey 被引量:9
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作者 Bo LI Xiaoyang XIE +1 位作者 Xingxing WEI Wenting TANG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2021年第3期145-163,共19页
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
关键词 Optical remote sensing Satellite image Sea target detection ship classification ship detection
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Detection of weak ship signals with the optimization of polarimetric contrast enhancement 被引量:6
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作者 李海艳 He Yijun 《High Technology Letters》 EI CAS 2008年第1期85-91,共7页
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
关键词 polarimetric SAR ship detection OPTIMIZATION ocean remote sensing
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Phase spectrum based automatic ship detection in synthetic aperture radar images 被引量:2
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作者 Miaohui Zhang Baojun Qiao +1 位作者 Ming Xin Bo Zhang 《Journal of Ocean Engineering and Science》 SCIE 2021年第2期185-195,共11页
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
关键词 ship detection saliency detection phase spectrum sea-land segmentation
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