Abstract
As an active microwave sensor, synthetic aperture radar (SAR) has the characteristic of all-day and all-weather earth observation, which has become one of the most important means for high-resolution earth observation and global resource management. Ship detection in SAR images is also playing an increasingly important role in ocean observation and disaster relief. Nowadays, both traditional feature extraction methods and deep learning (DL) methods almost focus on improving ship detection accuracy, and the detection speed is neglected. However, the speed of SAR ship detection is extraordinarily significant, especially in real-time maritime rescue and emergency military decision-making. In order to solve this problem, this paper proposes a novel approach for high-speed ship detection in SAR images based on a grid convolutional neural network (G-CNN). This method improves the detection speed by meshing the input image, inspired by the basic thought of you only look once (YOLO), and using depthwise separable convolution. G-CNN is a brand new network structure proposed by us and it is mainly composed of a backbone convolutional neural network (B-CNN) and a detection convolutional neural network (D-CNN). First, SAR images to be detected are divided into grid cells and each grid cell is responsible for detection of specific ships. Then, the whole image is input into B-CNN to extract features. Finally, ship detection is completed in D-CNN under three scales. We experimented on an open SAR Ship Detection Dataset (SSDD) used by many other scholars and then validated the migration ability of G-CNN on two SAR images from RadarSat-1 and Gaofen-3. The experimental results show that the detection speed of our proposed method is faster than the existing other methods, such as faster-regions convolutional neural network (Faster R-CNN), single shot multi-box detector (SSD), and YOLO, under the same hardware environment with NVIDIA GTX1080 graphics processing unit (GPU) and the detection accuracy is kept within an acceptable range. Our proposed G-CNN ship detection system has great application values in real-time maritime disaster rescue and emergency military strategy formulation.
Highlights
As an active means of aerospace and aeronautical remote sensing, synthetic aperture radar based on microwave imaging technology has the characteristic of all-day and all-weather earth observation [1], which has a wide range of applications in environmental protection, disaster detection, ocean observation, resource exploration, and geological mapping [2]
We randomly divided the dataset into a training set, validation set, and test set according to the ratio of 7:2:1, where the validation set was used to adjust the model’s hyperparameters to avoid over-fitting [9]
We set Intersection over union (IoU) = 0.5 as another detection threshIonlda.ddition, it should be noted that image preprocessing (32 s per image), which will certainly incrIenasaedtdriatiinoinn,gittismhoe,uwldasbetoneostteadbltihshatmimoraegeacpcurerparteocmesosdinegls (a3v2osidecinogndths epneregimatiavgee)i,mwphaiccthofwbilald cesratmaipnlleysinincrtehaesetrtaraininininggptriomcee,sws.aHs tooweestvaebrl,iswhhmenoraecatuccaul rsahtiepmdoedteeclstioavnowidaisngcathrreiendegoauttivienitmhpe atcetst opf rboacdesssa,mimplaegseisnwtheeretrnaoint ipnrgepprroocceesssse.dH, osowtehveerd,ewtehcetinonacpturoaclesshsipdodeestencotitocnonwtaaisnctahrerietidmoeuotfiinmthagee tepsrtepprroocceessssi,nigmaasgiessswhoewrenniontFpigreuprero1c2e.sTsehdu,ss:o the detection process does not contain the time of imaRgemeopterSeepnsr.o20c1e9s,s1i1n, gx FaOsRisPEsEhRoRwEDnVeIiEtneWcFtiiognutriem1e2E.SSTDhDus=: Detection timeSSDD
Summary
As an active means of aerospace and aeronautical remote sensing, synthetic aperture radar based on microwave imaging technology has the characteristic of all-day and all-weather earth observation [1], which has a wide range of applications in environmental protection, disaster detection, ocean observation, resource exploration, and geological mapping [2]. The technology of ship target detection in synthetic aperture radar (SAR) images is of great significance for ocean surveillance and disaster relief. In 1978, the United States first acquired SAR surface ship targets from the Seasat-1 satellite, which opened up the exploration of ship detection technology in SAR images. The methods of SAR ship detection are mainly divided into two categories—traditional methods and modern deep learning (DL) methods
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