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

Fuzzy clustering steel plate surface defect detection method based on pre classification

A technology of fuzzy clustering and defect detection, applied in image analysis, character and pattern recognition, instruments, etc., can solve the problem of low classification accuracy, eliminate low contribution rate, optimize feature vector, reduce missed judgment and misjudgment rate effect

Inactive Publication Date: 2015-07-22
CHONGQING UNIV
View PDF6 Cites 73 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The Canny algorithm can detect reasonable edge data and serve for the feature selection and extraction of copper strip surface defect images. When the discrimination degree of steel defect shape features is not high, the classification accuracy is not high

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
  • Fuzzy clustering steel plate surface defect detection method based on pre classification
  • Fuzzy clustering steel plate surface defect detection method based on pre classification
  • Fuzzy clustering steel plate surface defect detection method based on pre classification

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0041] specific implementation

[0042] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0043] see figure 1 , a method for detecting steel plate surface defects based on pre-classification fuzzy clustering, including the following steps:

[0044] S1: Take the real-time images taken by the steel plate production line as the source image of the steel plate surface defect detection, and directly extract the steel plate defect image under the conditions of the existing steel plate surface defect detection device;

[0045] S2: Process the defect image acquired in step S1, and determine the threshold interval of the image processing through the threshold segmentation method on the premise of the grayscale image, and complete the image pre-classification; specifically include the following steps:

[0046] S21: Gray-value the defect image acquired in step S1;

[0047] S22: Draw a histogram of the gray-scaled defect image, and...

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 the technical field of digital image processing and pattern recognition, discloses a fuzzy clustering steel plate surface defect detection method based on pre classification and aims to overcome defects of judgment missing and mistaken judgment by the existing steel plate surface detection method and improve the accuracy of steel plate surface defect online real-time detection effectively during steel plate surface defect detection. The method includes the steps of 1, acquiring steel plate surface defect images; 2 performing pre classification on the images acquired through step 1, and determining the threshold intervals of image classification; 3, classifying images of the threshold intervals of the step 2, and generating white highlighted defect targets; 4, extracting geometry, gray level, projection and texture characteristics of defect images, determining input vectors supporting a vector machine classifier through characteristic dimensionality reduction, calculating the clustering centers of various samples by the fuzzy clustering algorithm, and adopting the distances of two cluster centers as scales supporting the vector machine classifier to classify; 5, determining classification, and acquiring the defect detection results.

Description

technical field [0001] The invention relates to the technical field of digital image processing and pattern recognition, in particular to a method for detecting surface defects of steel plates based on pre-classification and fuzzy clustering. Background technique [0002] Steel plates have become an indispensable raw material for many industries. Accurate and efficient steel plate quality inspection and reasonable control of steel plate quality can improve productivity and reduce labor intensity, which is of great significance to improving the intelligent level of steel plate defect detection. With the rapid development of digital image processing and pattern recognition technology, digital image processing and pattern recognition technology has been applied to defect detection. [0003] The Chinese invention patent application with application number 201310210470.6 discloses an image recognition method for surface defects of hot-rolled steel sheets based on neighborhood inf...

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): G06K9/62G06T7/00
Inventor 鲜晓东李娇娇李晓龙苏航刘洋
Owner CHONGQING UNIV
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