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Video SAR target detection method based on deep learning

A technology of target detection and deep learning, applied in the field of radar, can solve the problems of complex detection methods and low detection accuracy, and achieve the effect of simple implementation, wide application scenarios and high detection accuracy

Pending Publication Date: 2020-05-12
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

The above method detects targets from the perspective of imaging mechanism, which requires artificial modeling to extract SAR image features, the detection method is complex, and the detection accuracy is not high

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  • Video SAR target detection method based on deep learning
  • Video SAR target detection method based on deep learning
  • Video SAR target detection method based on deep learning

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Embodiment Construction

[0029] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0030] The present invention has designed a kind of video SAR target detection method based on deep learning, such as figure 1 As shown, the steps are as follows:

[0031] Step 1: Preprocess and divide the video data set to obtain training set and test set;

[0032] Step 2: Construct the Resnet101 residual network as a feature extractor to extract high-dimensional features of SAR images; in the process of constructing the Resnet101 residual network, introduce the FPN network architecture, and combine the feature maps of different scales before and after the pooling layer as Subsequent steps provide multi-scale combined image features;

[0033] Step 3: Construct the RPN network, input the image features output by the Resnet101 residual network into the RPN network, and output the candidate area;

[0034] Step 4: Construct the Faster-RCNN ...

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Abstract

The invention discloses a video SAR target detection method based on deep learning, and the method comprises the steps: carrying out the preprocessing and dividing of a video data set, and obtaining atraining set and a test set; constructing a Resnet101 residual network as a feature extractor, wherein the feature extractor is used for extracting high-dimensional features of the SAR image; constructing an RPN network, inputting the image features output by the Resnet101 residual network into the RPN network, and outputting candidate regions; and constructing a Faster-RCNN network, and inputting a result output by the RPN network into the Faster-RCNN network to obtain a video SAR target detection result. The method has the characteristics of being simple to implement, high in detection precision and wide in application scene.

Description

technical field [0001] The invention belongs to the technical field of radar, and in particular relates to a video SAR target detection method. Background technique [0002] SAR (Synthetic Aperture Radar), that is, synthetic aperture radar, is an active earth observation system that can be installed on aircraft, satellites, spacecraft and other flight platforms to observe the earth all-weather and all-weather, and has a certain surface penetration capability. Therefore, the SAR system has unique advantages in the application of disaster monitoring, environmental monitoring, ocean monitoring, resource exploration, crop yield estimation, surveying and mapping, and military applications, and can play a role that other remote sensing methods are difficult to play. attention. [0003] The current common mainstream SAR target detection methods can be divided into three categories, target detection based on statistical distribution of background clutter, target detection based on...

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06V10/464G06V10/25G06N3/045G06F18/23213G06F18/24
Inventor 秦尉博闫贺黄佳黄智杰
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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