Soft weighted multi-stage network model applied to semantic segmentation

A semantic segmentation and network model technology, applied in biological neural network models, character and pattern recognition, instruments, etc., can solve the problems of not making full use of multi-scale information, not alleviating the irreversible loss of coding, etc.

Pending Publication Date: 2022-05-31
XIDIAN UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these methods not only fail to make full use of the multi-scale information generate

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
  • Soft weighted multi-stage network model applied to semantic segmentation
  • Soft weighted multi-stage network model applied to semantic segmentation
  • Soft weighted multi-stage network model applied to semantic segmentation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0018] Technical terms:

[0019] Atrous Transformation Feature Pyramid Module (AT-FPM);

[0020] Stage Feature Attention Module (StageFeatureAttention, SF-Attention);

[0021] Feature Pyramid Module (Feature PyramidModule, FPM);

[0022] Atrous Spatial Pyramid Pooling.

[0023] The design concept of the present invention is to propose a soft weighted multi-stage feature network composed of an atrous transform feature pyramid module (AT-FPM) and a stage feature attention module (SF-Attention). In AT-FPM, a concept of adopting different transformation functions for different stage features is proposed. Specifically, for deep stage features, the adaptive feature transformation function of the feature pyramid module is replaced by hollow space pyramid pooling, which expands the overall receptive field of the network and better extracts the features and global information of large-scale targets; while for the shallow layer features, the adaptive transformation function is 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
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a semantic segmentation network model applied to a complex scene, which adopts a hole transformation feature pyramid module and a stage feature attention module to form a soft weighting multi-stage feature network, void space pyramid pooling and a self-adaptive transformation function are used as transformation functions of deep layer and shallow layer stage features in a module respectively, the whole receptive field of the network is expanded, and the features and global information of a large-scale target are better extracted; and the staged feature attention module is used for extracting different attention degrees on the multi-scale target from the features in different stages, so that the features with identification degrees in different stages are effectively stored. Compared with a feature pyramid module, the hole transformation feature pyramid module can better extract target features with overlarge scale difference from multiple stages. The staged feature attention module can extract different attention degrees on a multi-scale target from features of different stages.

Description

technical field [0001] The invention relates to the field of semantic segmentation, relates to a multi-scale target segmentation network for urban street view images, and in particular to a soft-weighted multi-stage network model applied to semantic segmentation. Background technique [0002] In recent years, with the rapid development of vehicle cameras, surveillance cameras and other equipment, a large number of images of urban street scenes have been generated. Understanding the semantic information contained in these images is an important basis for urban applications such as autonomous driving and intelligent services. [0003] Multi-scale object segmentation in complex scenes such as city streets has always been a challenging problem in semantic segmentation tasks. This kind of image has the problem that the size difference between objects is too large, which makes the network unable to extract the features of objects at various scales while maintaining timeliness. ...

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): G06V10/26G06V10/46G06V20/10G06V20/70G06V10/82G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 苗启广林鸿凯刘向增宋建锋刘如意卢子祥赵博程郗岳马卓奇
Owner XIDIAN UNIV
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