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Coding and decoding network port image segmentation method fusing semantic flow field

An image segmentation, encoding and decoding technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problem of low segmentation accuracy of port images, achieve complete segmentation results, high segmentation accuracy, and improve effectiveness

Pending Publication Date: 2021-06-29
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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Problems solved by technology

[0005] The purpose of the present invention is to provide a codec network port image segmentation method that integrates semantic flow fields, and is used to solve the problem of low port image segmentation accuracy in the prior art

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  • Coding and decoding network port image segmentation method fusing semantic flow field
  • Coding and decoding network port image segmentation method fusing semantic flow field
  • Coding and decoding network port image segmentation method fusing semantic flow field

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Embodiment Construction

[0037] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0038] This embodiment proposes a coding and decoding network port image segmentation method that integrates semantic flow fields. The process is as follows figure 1 As shown, the specific steps are as follows:

[0039] The image to be segmented is input to the trained codec network that fuses the semantic flow field, also known as the SFD-LinkNet (Semantic Flow Dilated Convolution LinkNet) network, to segment the port image into three categories: sea, land, and ship. Among them, the SFD-LinkNet network such as figure 2 As shown, the structure of the SFD-LinkNet network includes an encoding layer, a hole convolution layer, and a decoding layer. The following describes each layer in detail:

[0040] (1) Coding layer

[0041] The encoding layer is used to input the image to be segmented and perform feature encoding, and the encoded feature...

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Abstract

The invention relates to a coding and decoding network port image segmentation method fusing semantic flow field, and belongs to the technical field of image segmentation, and the method comprises the following steps:inputting an image to be segmented into a trained coding and decoding network fused with the semantic flow field, and segmenting the port image into a sea type, a land type and a ship type; wherein the coding and decoding network comprises a coding layer, a dilated convolution layer and a decoding layer which are connected in sequence, the coding layer comprises N layers of convolution modules which are connected in sequence, the decoding layer comprises N layers of deconvolution modules which are connected in sequence, each deconvolution module is internally provided with a flow alignment module, and the input of each flow alignment module is in jump connection with the convolution module of the corresponding level in the coding layer. According to the method, the validity of feature information transmission is improved by predicting a semantic flow field between feature maps and monitoring an up-sampling process by utilizing the flow alignment module, and the multi-scale information of the image is acquired by utilizing the cavity convolution layer, so that the method is more suitable for a port image segmentation task, a smooth and complete segmentation result is obtained, and the segmentation precision is relatively high.

Description

technical field [0001] The invention belongs to the technical field of image segmentation, and in particular relates to a coding and decoding network port image segmentation method that integrates semantic flow fields. Background technique [0002] With the rapid development of remote sensing technology, the use of remote sensing images for sea-land segmentation and ocean near-shore target detection has gradually become a current research hotspot, and has important applications in coastline extraction, sea area traffic control, and military monitoring. However, the image features of the remote sensing image port area are complex, there are many interference factors such as waves, clouds, shadows, etc., and there are blurred borders between docks and ships, which lead to pixel classification errors and boundary blur positioning in port image segmentation. , thus, accurate land-sea segmentation and robust ship detection are challenging. [0003] Traditional sea and land image...

Claims

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Application Information

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IPC IPC(8): G06K9/34G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V10/267G06N3/045G06F18/2431G06F18/25G06F18/214
Inventor 郭海涛卢俊高慧林雨准龚志辉余东行袁洲牛艺婷饶子昱王家琪
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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