Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Fruit segmentation method based on sparse convolution kernel

A convolution kernel and sparse technology, applied in the fields of computer vision and agricultural engineering, can solve the problems of lack of quantitative indicators and no neighborhood pixel information into consideration, so as to improve the accuracy rate, reduce the amount of calculation, and ensure the effect of segmentation

Active Publication Date: 2020-09-25
HUAIYIN INSTITUTE OF TECHNOLOGY
View PDF5 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, there are two shortcomings in this kind of method. First, the selection of color factors relies on experience and visual judgment, and lacks quantitative indicators; second, only the color information of a single pixel is considered in fruit segmentation, and the information of neighboring pixels is not included. consider

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
  • Fruit segmentation method based on sparse convolution kernel
  • Fruit segmentation method based on sparse convolution kernel
  • Fruit segmentation method based on sparse convolution kernel

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0042] A fruit segmentation method based on a sparse convolution kernel. In this embodiment, a sparse convolution kernel with a size of 5×5 is used to segment an apple image. The specific process is as follows figure 1 As shown, the following steps are included:

[0043] Step 1: Extract the main object samples in the apple image

[0044] Although the light environment in the apple image is complex and the fruit states are diverse, the composition of the main objects in the image is relatively fixed. Analyzing the image shows that the objects that make up the image can be mainly divided into five categories: fruit, leaves, branches, sky and soil. In order to analyze the color features of these five types of objects, 60 images were selected as sample images, and the pixel samples of these five types of objects were extracted from these images, and some sample areas such as figure 2 shown. When selecting samples, the difference and representativeness of sample pixels are full...

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 fruit segmentation method based on a sparse convolution kernel. According to the invention, the method comprises the steps: analyzing the distinction degree of a main objectin a fruit image under different color factors; selecting an appropriate color channel to reconstruct an image, and then proposing a sparse convolution kernel construction mode that elements are spaced from one another and are not adjacent to one another, and determining elements in a sparse convolution kernel by adopting a linear classifier; and finally performing convolution operation on the reconstructed image by adopting the sparse convolution kernel, so fruit segmentation is realized.

Description

technical field [0001] The invention relates to the fields of computer vision and agricultural engineering, in particular to an image segmentation method for field fruit recognition, and in particular to a fruit segmentation method based on a sparse convolution kernel. Background technique [0002] Fruit identification is an important step to realize automatic fruit picking and intelligent yield estimation. Color is the direct representation of fruit in vision, and it is also one of the easiest image features to extract, so it has been widely used in fruit recognition, especially for fruits with large differences between color and background, such as apples, tomatoes, citrus, etc. Wait. [0003] Some scholars proposed to use the a component of the Lab color space to realize the recognition of ripe citrus, and proposed to use the R-G color operator to realize the recognition of ripe apples or tomatoes. In order to further improve the effect of fruit segmentation, some schol...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/34G06K9/62
CPCG06V10/267G06V10/56G06F18/24G06F18/214
Inventor 刘晓洋张青春
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com