Remote sensing image ship target fine-grained classification method based on dynamic convolution

A remote sensing image and classification method technology, applied in the direction of instruments, biological neural network models, scene recognition, etc., can solve the problem of low accuracy of fine-grained classification of ship targets in remote sensing images, and achieve the goal of improving feature learning ability and accuracy Effect

Pending Publication Date: 2021-03-09
BEIHANG UNIV
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[0004] Therefore, in order to solve the problem of low accuracy of fine-grained classification of ship targets in remote sensing i

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  • Remote sensing image ship target fine-grained classification method based on dynamic convolution
  • Remote sensing image ship target fine-grained classification method based on dynamic convolution
  • Remote sensing image ship target fine-grained classification method based on dynamic convolution

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[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0025] Such as figure 1 , figure 2 A fine-grained classification method for ship targets in remote sensing images based on dynamic convolution. The collected feature maps are input into the attention module, and the attention module generates K normalized attention weight parameters;

[0026] In the convolution processing of the acquired feature map, K parallel convolutions are used in the parallel convolution kernel module instead of individual convolutions...

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Abstract

The invention discloses a remote sensing image ship target fine-grained classification method based on dynamic convolution, and the method comprises the steps: inputting a collected feature map into an attention module, and enabling the attention module to generate K normalized attention weight parameters; in the convolution processing of the collected feature map, using K parallel convolution forreplacing independent convolution in a parallel convolution kernel module, combining attention weight parameters and convolution kernels of the K parallel convolution to form a dynamic convolution layer, and finally connecting the dynamic convolution layer to a classification network for classification; fusing an attention mechanism into dynamic convolution, achieving multi-core integration, adding and fusing normalized attention weight parameters calculated through the attention module in the front in a non-linear mode, thus the feature learning capacity of the model is improved, and then the accuracy of ship target fine-grained classification is improved.

Description

technical field [0001] The invention belongs to the field of digital image processing, and more specifically relates to a fine-grained classification method for ship targets in remote sensing images based on dynamic convolution. Background technique [0002] The classification technology of remote sensing image sea ship targets has always been the basic research in the field of remote sensing image processing, and it is of great significance in real-time port monitoring, maritime military ship target detection and fine recognition. Since most of the public research on ship targets in remote sensing images mainly focuses on the detection of ship targets and the subsequent binary classification of background and ship targets, a small number of documents specifically study the multi-classification tasks of ship targets, but the classification The number of categories is also very limited. In recent years, with the rapid development of deep learning technology, technologies rela...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/13G06N3/045G06F18/24
Inventor 姜志国邸杨骅张浩鹏赵丹培谢凤英
Owner BEIHANG UNIV
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