Image semantic segmentation method based on multi-receptive-field context semantic information

A semantic information, multi-receptive field technology, applied in the field of image recognition, can solve the problems of large memory, consumption, etc., to achieve the advantages of accuracy, low memory consumption, and convenient implementation.

Pending Publication Date: 2022-04-15
HENAN AGRICULTURAL UNIVERSITY
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AI Technical Summary

Problems solved by technology

However, this requires the production of many intermediate representations, consuming a large amount of memory

Method used

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  • Image semantic segmentation method based on multi-receptive-field context semantic information
  • Image semantic segmentation method based on multi-receptive-field context semantic information
  • Image semantic segmentation method based on multi-receptive-field context semantic information

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Embodiment Construction

[0035] Such as figure 1 As shown, the image semantic segmentation method based on multi-receptive field context semantic information of the present invention comprises the following steps:

[0036] Step 1. Convert the input image into a pixel matrix through a convolution operation;

[0037] Step 2. Using dilated convolutions with different dilation rates to convert the same pixel matrix into multiple feature maps with contextual semantic information of multiple receptive fields;

[0038] The output of the dilated convolution is:

[0039]

[0040] Among them, y i Represents the i-th output of the expanded convolution, the convolution kernel size of the expanded convolution is k*k, and the expansion rate is r,x i is the i-th input feature map of the dilated convolution before the converter subnetwork, and m is the length of the filter matrix w[k] with a convolution kernel size of k*k.

[0041] The receptive field is very important in target detection and image segmentation....

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Abstract

The invention discloses an image semantic segmentation method based on multi-receptive-field context semantic information. The method comprises the steps of 1, converting an input image into a pixel matrix through convolution operation; 2, converting the same pixel matrix into a plurality of feature maps with multi-receptive-field context semantic information by adopting expansion convolution with different expansion rates; 3, performing feature extraction and down-sampling processing on the feature map with the multi-receptive-field context semantic information through converter encoders in different subnets to obtain a plurality of down-sampling feature maps with different receptive fields; 4, carrying out step-by-step up-sampling processing on the down-sampling feature map through a decoder to obtain feature maps with the same size and dimension, and generating a final feature fusion map; and 5, the feature fusion image completes image segmentation prediction through a convolutional neural network. The method can be effectively applied to image semantic segmentation, does not lose deep low-resolution features and fine-grained features, consumes a small memory, is remarkable in effect, and is convenient to popularize.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to an image semantic segmentation method based on multi-receptive field context semantic information. Background technique [0002] Image semantic segmentation is the basis of image analysis and the basis of many applications, such as object recognition in autonomous driving systems, drone applications, and wearable device applications. An image is composed of pixels, and "semantic segmentation", as the name implies, is to group or segment pixels according to the different semantic meanings expressed in the image. The goal of image semantic segmentation is to label the category of each pixel of the image. Therefore, semantic segmentation refers to the recognition of the target tissue in the image at the pixel level, that is, to mark the object category to which each pixel in the image belongs. [0003] In the development of image segmentation, many segmentati...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V10/26G06V10/77G06K9/62G06N3/04G06N3/08
Inventor 刘亮亮常靖
Owner HENAN AGRICULTURAL UNIVERSITY
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