Image classification method based on RGB three-component grouping attention weighted fusion

A technology of weighted fusion and classification method, applied in instruments, biological neural network models, computing, etc., can solve the problems of difficult detection of small targets, low resolution, and difficult detection of image targets.

Active Publication Date: 2022-07-29
YANCHENG INST OF TECH +1
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In practical applications, the captured video images are often affected by factors such as illumination changes, target occlusion, and noise interference. The targets in the image are difficult to detect, especially due to factors such as low resolut...

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 classification method based on RGB three-component grouping attention weighted fusion
  • Image classification method based on RGB three-component grouping attention weighted fusion
  • Image classification method based on RGB three-component grouping attention weighted fusion

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0077] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, but not to limit the present invention.

[0078] The invention provides an image classification method based on RGB three-component grouping attention weighted fusion, such as Figure 1-4 shown, including:

[0079] Collect target images in complex environments, and perform noise reduction processing to obtain target images after noise reduction;

[0080] extracting the RGB three-channel component images of the denoised target image respectively;

[0081] Using the pre-built convolution kernel and according to the preset convolution rules, the convolution operation is performed on the RGB three-channel component image, and the feature maps of each intermediate convolution level in the RGB three-channel component image a...

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 provides an image classification method based on RGB three-component grouping attention weighted fusion, and the method comprises the steps: collecting a target image in a complex environment, and carrying out the noise reduction processing, and obtaining a denoised target image; and respectively extracting RGB three-channel component images of the denoised target image. According to the image classification method based on RGB three-component grouping attention weighted fusion, RGB three components are introduced, an attention mechanism is introduced into the residual network model, global and local color three-component features are fully utilized for feature extraction, and the classification effect on targets with noise interference and bright colors, especially small targets, is good.

Description

technical field [0001] The invention relates to the technical field of image classification and intelligent optimization, in particular to an image classification method based on RGB three-component grouping attention weighted fusion. Background technique [0002] With the development of computer vision technology, applications such as traffic video surveillance, automatic identification of crop diseases and insect pests, and automatic equipment detection of industrial products have put forward higher requirements for the quality of visual recognition. How to accurately classify the extracted object images has become a problems to be solved. In practical applications, the captured video images are often affected by factors such as illumination changes, target occlusion, noise interference, etc., and the targets in the images are difficult to detect, especially due to factors such as low resolution and small size, some tiny targets are even more difficult to detect. , the ex...

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): G06V10/56G06V10/44G06V10/80G06V10/30G06V10/764G06V10/82G06N3/04
CPCG06V10/56G06V10/454G06V10/806G06V10/30G06V10/764G06V10/82G06N3/048G06N3/045
Inventor 陈瑾杨国宇刘柱范浩楠史鸣凤
Owner YANCHENG INST OF TECH
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