Part defect detection interval clustering method based on feature selection and combinatorial optimization algorithm

A combination optimization and feature selection technology, applied in computer parts, computing, image data processing, etc., can solve problems such as not being optimal, complex setting interval combinations, and different optical surfaces.

Active Publication Date: 2021-10-22
CHANGZHOU MICROINTELLIGENCE CO LTD
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This leads to some interval combinations are not a better result
At the same time, the optical surface of the same defect is different, which makes the combination of setting intervals complicated

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
  • Part defect detection interval clustering method based on feature selection and combinatorial optimization algorithm
  • Part defect detection interval clustering method based on feature selection and combinatorial optimization algorithm
  • Part defect detection interval clustering method based on feature selection and combinatorial optimization algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0036] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0037] See Figure 5 and Figure 8 , a feature selection and combined optimization algorithm for component defect detection interval clustering method, the specific steps are as follows:

[0038] The first step, data collection: The equipment machine takes a picture of the workpiece, reads the contour points (pixel coordinates) in the original picture, and completes the data collection work. Among them, the equipment machine can be electronic 3C surface defect appearance inspection equipment. The workpieces are electronic 3C workpieces, such as mobile phone casings, n...

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 part defect detection interval clustering method based on a feature selection and combinatorial optimization algorithm, and the method comprises the following specific steps: 1, collecting data; step 2, performing data cleaning; 3, balancing data distribution; step 4, performing feature selection; 5, setting interval combinations, selecting a better interval combination, and performing expansion optimization on the better interval combination; and 6, removing the data in the combination from the original data set, repeating the step 5 for the rest data until all positive samples are selected by the rules, obtaining a series of rule descriptions, and ending the combinatorial optimization approximation algorithm. According to the method, positive and negative sample combination optimization clustering distinguishing is carried out on each optical surface with different defects of the industrial part, and certain robustness is achieved, so that multi-project defect accurate detection and division can be ensured.

Description

technical field [0001] The invention relates to the technical field of image data processing, in particular to a component defect detection interval clustering method based on a feature selection and combination optimization algorithm. Background technique [0002] At present, most methods based on image data processing select physical quantity intervals for clustering based on experience. Differences in physical quantity weights, optical surfaces, and defect types affect the accuracy of positive and negative sample division, and there are many limitations. The most obvious is that the weight of the length and width physical quantities of linear defects is relatively large, and the physical quantity of area is not considered; for massive defects, the weight of the physical quantity of area is relatively large, and the physical quantity of length and width is not considered. This leads to suboptimal results for some interval combinations. At the same time, the optical surfac...

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): G06K9/62G06T7/00G06T7/62
CPCG06T7/0002G06T7/62G06F18/23
Inventor 邱增帅王罡侯大为潘正颐
Owner CHANGZHOU MICROINTELLIGENCE CO LTD
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