An SAR image target detection method based on the convolutional neural network

A convolutional neural network and target detection technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problem that SAR image data cannot meet the large amount of training data of convolutional neural network models, and improve the detection accuracy. rate effect

Active Publication Date: 2016-12-14
XIDIAN UNIV +1
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Problems solved by technology

[0006] The present invention expands the MiniSAR training set by using the Mstar data set, and solves the problem that the existing SAR image data cannot satisfy the need for a large amount of training data for the convolutional neural network model. In order to achieve the above invention, the technical solution includes the following:

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  • An SAR image target detection method based on the convolutional neural network
  • An SAR image target detection method based on the convolutional neural network
  • An SAR image target detection method based on the convolutional neural network

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Embodiment Construction

[0038] refer to figure 1 , the SAR image target detection method of the present invention mainly has three stages of model design, training and testing, and concrete steps are as follows:

[0039] 1. Model design stage

[0040] Step 1, design the convolutional neural network model M 0 '.

[0041] refer to figure 2 , convolutional neural network M' 0 It consists of two parts: feature extraction and classification judgment, among which:

[0042] The feature extraction part includes three convolutional layers, the convolutional layer is followed by a nonlinear layer, and the first two nonlinear layers are followed by a maximum pooling layer;

[0043] The classification decision part includes two fully connected layers, the first fully connected layer is followed by a nonlinear layer, the second fully connected layer is followed by a flexible maximum layer and a classification loss function layer, which is expressed as:

[0044] m 0 '=[I,C 1 ,R,Pm,C 2 ,R,Pm,C 3 ,R,Fc 1 ,...

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Abstract

The invention provides an SAR image target detection method based on the convolutional neural network and mainly solves the problem of low detection speed and low detection accuracy rate in the conventional SAR image target detection technology. The method comprises the steps of firstly training a convolutional neural network classification model M0 and designing two to-be-trained convolutional neural network models M1 and M2 based on the model M0; secondly, using an expanded MiniSAR training set as the input for the to-be-trained convolutional neural network models M1 and M2 and acquiring an optimal slice extracting model M12 and an optimal detection model M22 through training; thirdly, detecting the MiniSAR training set by using the optimal slice extracting model M12 and the optimal detection model M22; fourthly, reserving candidate areas with output probability values of the optimal detection model M22 being greater than a threshold value and acquiring a detection result. The method has the advantages of high detection speed and high detection accuracy rate and can be used for vehicle target detection.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to a target detection method, which can be used for vehicle target detection. Background technique [0002] Since the beginning of the 21st century, convolutional neural networks have been widely used in many fields such as detection, segmentation, and object recognition. In the ImageNet competition in 2012, convolutional neural networks have achieved unprecedented results, which is lower than the best method at that time. Half the error rate, and use the fast graphics processing unit GPU to train the convolutional neural network model. Due to the powerful parallel computing capability of the GPU, the time required for training the convolutional neural network model is greatly shortened. The success of this experiment brings computer vision. Come a new revolution. [0003] Synthetic Aperture Radar (SAR) is an active sensor that uses microwaves for perception. SAR imaging is not limit...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/084G06V20/13G06V2201/08G06N3/045
Inventor 杜兰刘彬毛家顺代慧刘宏伟
Owner XIDIAN UNIV
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