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
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  • 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

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[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...

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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...

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Application Information

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IPC IPC(8): G06K9/62G06T7/00
Inventor 鲜晓东李娇娇李晓龙苏航刘洋
Owner CHONGQING UNIV
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