Grafting clamp posture recognition method based on machine vision

A machine vision and recognition method technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of not realizing the automatic sorting and recognition function of grafting clips, low degree of automation of the clip supply device, and high working noise, etc. Achieve the effect of improving work efficiency and automation, strong generalization and approximation capabilities, and reducing volume

Inactive Publication Date: 2020-01-14
ZHEJIANG SCI-TECH UNIV
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The above-mentioned clamping devices for grafting clamps are not highly automated, mainly based on passive vibration sorting. The vibration scheme has a simple structure and is easy to maintain, but it also has poor stability and high working noise. It is necessary to ensure the direction and direction of the grafting clamps. The shortcomings of the device in the placed state and the device to prevent the grafting clips from being connected to each other have not yet realized the active sorting and identification function of the grafting clips

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
  • Grafting clamp posture recognition method based on machine vision
  • Grafting clamp posture recognition method based on machine vision
  • Grafting clamp posture recognition method based on machine vision

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0048] First define a few concepts:

[0049] 1. Connected domain: The area where adjacent pixels of the same color gather together is a connected domain.

[0050] 2. Contour chain: The edge pixels of the connected domain are connected to form a contour chain.

[0051] 3. Span: the distance between two different pixel points on the contour chain.

[0052] 4. Contour chain angle: set the included angle of the vector formed by different span parameters according to the contour chain.

[0053] 5. Contour smoothness r; for the pixels of the extracted contour chain, take the same span to calculate the chain angle of each pixel point of the contour chain at this span, and obtain a pixel point number on the abscissa, and a chain on the ordinate The curve of the corner angle. The problem of finding the smoothness of the contour can be transformed into the problem of finding the smoothness of this curve.

[0054] The specific method of calculating the smoothness of the curve is as f...

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 relates to a machine vision identification method. According to the grafting clamp posture recognition method based on machine vision, the position and posture of a grafting clamp can beeffectively recognized, a subsequent flexible mechanical arm can conveniently, accurately and actively grab a grafted object, and therefore active clamp supply operation of the grafting clamp is achieved. According to the technical scheme, the grafting clamp posture recognition method based on machine vision comprises the following steps: 1, collecting an image; step 2, processing the image; 3, determining a grabbing point; step 4, extracting corner points; step 5, grabbing of a terminal grabbing device; 6, selecting the posture of a grafting clamp of the target object; and 7, selecting the same posture and different heights.

Description

technical field [0001] The invention relates to a machine vision recognition method, in particular to a machine vision-based grafting clip gesture recognition method. Background technique [0002] With the gradual improvement of people's living standards, people's demand for vegetables is also increasing, and the area of ​​vegetable planting is also expanding year by year; however, due to soil deterioration, insect pests have seriously affected the normal growth of vegetables, thereby reducing the quality and quality of vegetables. Yield. Grafting technology can effectively improve plant disease resistance and insect resistance, so grafting machines play a key role in agricultural development; grafting clips are an indispensable part of grafting machines, and during the grafting process, grafting clips are messy It not only affects the grafting efficiency, but also affects the quality of the grafting. [0003] Grafting clip posture recognition and positioning based on mach...

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): G06K9/00G06K9/40G06K9/46G06K9/62
CPCG06V20/00G06V10/30G06V10/44G06F18/2411
Inventor 喻擎苍费焕强赵晖
Owner ZHEJIANG SCI-TECH 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