Image semantic segmentation method based on local and global feature enhancement modules

A semantic segmentation and global feature technology, applied in image analysis, image enhancement, image coding, etc., can solve the problems of error-prone pixel classification and low segmentation accuracy of small-scale objects, so as to improve the segmentation effect, stabilize the segmentation performance, and achieve good segmentation. The effect of robustness

Active Publication Date: 2020-05-29
CHONGQING UNIV OF POSTS & TELECOMM
View PDF7 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Semantic segmentation technology has been widely used in artificial analysis, virtual reality, automatic driving and other fields. However, in some complex scenes, due to the large

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Image semantic segmentation method based on local and global feature enhancement modules
  • Image semantic segmentation method based on local and global feature enhancement modules
  • Image semantic segmentation method based on local and global feature enhancement modules

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] The present invention provides an image semantic segmentation method based on local and global feature enhancement modules, and the described embodiments are only part of the embodiments of the present invention. The flow process of the inventive method is as figure 1 , the image data is extracted through the encoder composed of the residual stage and the local feature enhancement module, and the final segmented image is generated by the decoder composed of the global feature enhancement module and the feature fusion operation, which is specifically divided into the following steps:

[0019] 1) Obtain the Cityscapes dataset, select and make the training set images, verification set image samples and label pictures required for the semantic segmentation task, for training and evaluation of the segmentation model.

[0020] 2) Perform data enhancement on the training set images, standardize the sample images in the training set images and verification set images respective...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an image semantic segmentation method based on local and global feature enhancement modules. The image semantic segmentation method comprises the following steps: selecting andmanufacturing a training set image and a verification set image required by a semantic segmentation task, and corresponding label pictures; performing data enhancement on the training set image; standardizing sample images in the training set image and the verification set image respectively; encoding the corresponding label image, designing a convolutional neural network, taking the processed data as input data of the model, outputting a multi-channel feature map, optimizing parameters of the convolutional neural network, inputting a real scene image into the convolutional neural network with optimized parameters for semantic segmentation, and outputting an image with marked pixels. Important technical support is provided for subsequent operation in research such as scene analysis and reinforcement learning, and the method can be applied to the fields of virtual reality, automatic driving and man-machine interaction.

Description

technical field [0001] The invention relates to a convolutional neural network and image semantic segmentation technology, in particular to an image semantic segmentation method based on enhanced features of the convolutional neural network. Background technique [0002] Image semantic segmentation is one of the important research topics in computer vision, and its research results have an important impact on other vision tasks. Semantic segmentation technology has been widely used in artificial analysis, virtual reality, automatic driving and other fields. However, in some complex scenes, due to the large variety of segmentation targets and the diversity of scales, the segmentation accuracy of small-scale targets is relatively low. Pixel classification between objects is error-prone. [0003] With the steady development of deep learning and machine learning, the use of convolutional neural networks to learn target features has been greatly sought after by researchers. Ther...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06T7/11G06K9/00G06K9/62G06N3/04G06N3/08G06T9/00
CPCG06T7/11G06T9/00G06N3/08G06T2207/10016G06V20/13G06N3/045G06F18/25
Inventor 陈乔松段博邻隋晓旭李金鑫王郅翔周丽刘宇张珺涵边愿愿
Owner CHONGQING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap