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Image semantic segmentation method and device and electronic equipment

A technology of semantic segmentation and image, applied in the field of semantic segmentation method of image, device and electronic equipment, can solve the problems of local method without structure retention ability, reliability to be improved, ignoring image details and characteristics, etc.

Pending Publication Date: 2020-06-16
MEGVII BEIJINGTECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the current image semantic segmentation technology, it can be generally divided into two categories according to the range of perception: local and global. Traditional local methods expand the receptive field by cascading conventional convolutions or their variants (such as hole-punched convolutions). However, local The method has no detailed structure preservation ability; non-local methods mainly directly model long-distance feature dependencies, such as non-local operations, PSP and ASPP modules and graph-based neural networks, however, non-local methods ignore the details of the image feature
Therefore, the reliability of the existing image semantic segmentation technology needs to be improved

Method used

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  • Image semantic segmentation method and device and electronic equipment
  • Image semantic segmentation method and device and electronic equipment
  • Image semantic segmentation method and device and electronic equipment

Examples

Experimental program
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Effect test

Embodiment 1

[0044] First, refer to figure 1 An example electronic device 100 for implementing a method, an apparatus, and an electronic device for semantic segmentation of an image according to an embodiment of the present invention will be described.

[0045] Such as figure 1 Shown is a schematic structural diagram of an electronic device. The electronic device 100 includes one or more processors 102, one or more storage devices 104, an input device 106, an output device 108, and an image acquisition device 110. These components pass through a bus system 112 and / or other forms of connection mechanisms (not shown). It should be noted that figure 1 The components and structure of the electronic device 100 shown are only exemplary, not limiting, and the electronic device may also have other components and structures as required.

[0046] The processor 102 can be implemented in at least one hardware form of a digital signal processor (DSP), a field programmable gate array (FPGA), and a pr...

Embodiment 2

[0053] This embodiment provides a method for semantic segmentation of images, which can be executed by the above-mentioned electronic equipment such as a computer, and the electronic equipment is provided with a neural network model, see figure 2 The flow chart of the method for semantic segmentation of the image shown, the method mainly includes the following steps S202 to S208:

[0054] Step S202, extracting low-level features and high-level semantic features of the target image through the feature extraction network of the neural network model.

[0055] Among them, the resolution corresponding to low-level features is higher than that of high-level semantic features. In the image recognition of the neural network model or the forward propagation process of the neural network training, in order to improve the image segmentation performance, the network layer of the neural network model extracts features of different scales from the input target image, such as low-level feat...

Embodiment 3

[0102] On the basis of the foregoing embodiments, this embodiment provides two specific examples of semantic segmentation methods applying the foregoing images, for details, refer to the following embodiments:

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Abstract

The invention provides an image semantic segmentation method and device and electronic equipment, and relates to the technical field of machine vision, and the method comprises the steps: extracting low-level features and high-level semantic features of a target image through a feature extraction network of a neural network model; constructing the low-level features into a minimum spanning tree structure; inputting the constructed minimum spanning tree structure and high-level semantic features into a tree feature converter in a neural network model to obtain fusion features; and segmenting the target image based on the fusion features to obtain an image segmentation result of the target image. According to the invention, the reliability of image semantic segmentation can be improved.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to an image semantic segmentation method, device and electronic equipment. Background technique [0002] Image semantic segmentation is one of the important fields in computer vision. The main purpose of image semantic segmentation is to identify images at the pixel level and mark the object category to which each pixel in the image belongs. In the current image semantic segmentation technology, it can be generally divided into two categories according to the range of perception: local and global. Traditional local methods expand the receptive field by cascading conventional convolutions or their variants (such as hole-punched convolutions). However, local The method has no detailed structure preservation ability; non-local methods mainly directly model long-distance feature dependencies, such as non-local operations, PSP and ASPP modules and graph-based neural networks, howe...

Claims

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Application Information

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IPC IPC(8): G06K9/34G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V10/26G06N3/045G06F18/213G06F18/253
Inventor 宋林李彦玮黎泽明
Owner MEGVII BEIJINGTECH CO LTD
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