Attention mechanism-based SAR image target detection method and test platform

A target detection algorithm and target detection technology are applied in the field of synthetic aperture radar image target detection method and target simulation test platform to achieve the effect of improving operating efficiency and good robustness

Active Publication Date: 2020-12-18
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These mathematical modeling and detection methods based on prior statistical information need to design classifiers according to the current recognition task or extract other features in a targeted manner. The robustness of the algorithm is not enough after the use scene changes, and it is necessary to continuously update the relevant modules of the algorithm to adapt to different tasks

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  • Attention mechanism-based SAR image target detection method and test platform
  • Attention mechanism-based SAR image target detection method and test platform
  • Attention mechanism-based SAR image target detection method and test platform

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Embodiment 1

[0038] Embodiment 1, the embodiment of the present invention discloses a SAR image target detection method based on attention mechanism, such as figure 1 As shown, the overall structure is a two-stage object detection model. In the feature extraction stage, a Global Attention Module (GAM) is used to extract high-level features of SAR images other than color and shape. In the candidate frame extraction stage, the local attention module (LAM) is used to obtain more accurate and effective candidate regions and improve the operating efficiency of the algorithm. Include the following steps:

[0039] Step 1: Input the SAR image, and use the global attention module to jointly calculate the attention weight from the two perspectives of the spatial domain and the channel domain, thereby forming a 3D attention mask and constructing a feature pyramid. Specifically include:

[0040] Step 11: Use deformable convolution to perform convolution operation on the spatial domain, and form a 2...

Embodiment 2

[0058] Embodiment 2, the SAR image target simulation test platform includes three test systems with different application environments and application forms, which are respectively a ground SAR target detection system, a ground SAR target classification recognition system and an on-board SAR target detection simulation system.

[0059] The above systems all include an image retrieval module, a display module and an algorithm module, the image retrieval module is respectively connected with the display module and the algorithm module, and the display module is connected with the algorithm module;

[0060] The image retrieval module is used to retrieve the image to be detected from the database;

[0061] The display module is used to display relevant information;

[0062] The algorithm module includes a target detection algorithm or a classification recognition algorithm, which is used for target detection or classification of the image to be detected according to specific instr...

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Abstract

The invention discloses an attention mechanism-based SAR image target detection method and a test platform, and the detection method comprises the steps: extracting advanced features, except color andshape, of a synthetic aperture radar image at a feature extraction stage by adopting a global attention module, and at a candidate frame extraction stage, using a local attention module to obtain a more accurate and effective candidate region, and performing target prediction on the feature pyramid with the advanced features by a prediction frame based on a classification regression network to obtain a detection result; and enabling the simulation test platform to achieve the functions of image preprocessing, target detection and recognition and the like, and forming a set of complete automatic target detection and recognition algorithm ground simulation test platform based on big data analysis. According to the detection method, through verification of the simulation test platform, goodrobustness and high detection accuracy are further verified.

Description

technical field [0001] The present invention relates to the technical field of deep learning and pattern recognition, and more specifically relates to an attention mechanism-based synthetic aperture radar image target detection method and a target simulation test platform. Background technique [0002] At present, synthetic aperture radar (SAR) is gradually occupying an important position in remote sensing earth observation, resource exploration, reconnaissance and early warning and other application fields, and has become an indispensable means of detection. Compared with visible light or infrared remote sensing images, SAR images have better imaging effects and more unique applications for some typical targets. In addition, the electromagnetic scattering mechanism of SAR provides SAR images with unique target characteristics. Although these features are not easy to observe and analyze from a visual point of view, the target information contained in them is of great help to...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06V10/449G06N3/048G06N3/045G06F18/241
Inventor 赵丹培朱纯博袁智超苑博张浩鹏史振威
Owner BEIHANG UNIV
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