SAR image target detection method based on convolutional neural network

Abstract

The invention discloses a SAR image target detection method based on a convolutional neural network, which mainly solves the problems of slow detection speed and low detection accuracy in the existing SAR image target detection technology. The implementation steps are: 1. Train the convolutional neural network classification model M 0 , based on the model M 0 Design two convolutional neural network models M to be trained 1 , M 2 2. Use the expanded MiniSAR training set as the convolutional neural network model M to be trained 1 , M 2 input, and train to obtain the optimal slice extraction model M 12 and the optimal detection model M 22 ; 3. Use the optimal slice extraction model M 12 and the optimal detection model M 22 Detect the MiniSAR test set; 4. Use the optimal detection model M 22 The candidate regions whose output probability value is greater than the threshold are reserved to obtain the detection result. The invention has the advantages of fast detection speed and high detection accuracy, 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...

AI Technical Summary

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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