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Steel picture defect detection method in industrial production based on self-supervised contrast characterization learning technology

A defect detection and industrial production technology, applied in neural learning methods, image enhancement, image analysis, etc., can solve problems such as relying on a large amount of label data, and achieve excellent defect detection results

Pending Publication Date: 2022-03-25
HEFEI UNIV
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Problems solved by technology

[0007] The purpose of the present invention is to solve the defect detection of steel image defects in the prior art that needs to rely on a large number of label data, and provide a method for detecting steel image defects in industrial production based on self-supervised comparative representation learning technology to solve the above problems

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  • Steel picture defect detection method in industrial production based on self-supervised contrast characterization learning technology
  • Steel picture defect detection method in industrial production based on self-supervised contrast characterization learning technology
  • Steel picture defect detection method in industrial production based on self-supervised contrast characterization learning technology

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Embodiment Construction

[0094] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0095] Such as figure 1 As shown, a kind of steel picture defect detection method in industrial production based on self-supervised comparative representation learning technology of the present invention comprises the following steps:

[0096] The first step is the acquisition and preprocessing of industrial production steel pictures: obtain labeled and unlabeled industrial production steel pictures and perform preprocessing.

[0097] (1) Obtain pictures of steel produced in industry;

[0098](2) Carry out data enhancement processing on industrial production steel pictures:

[0099] Firstly, all the industrial production steel pictures are adjusted to the size of 224x224, and then random cropping and random horizontal flip are appli...

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Abstract

The invention relates to a steel picture defect detection method in industrial production based on a self-supervised contrast characterization learning technology. Compared with the prior art, the defect that steel picture defect detection needs to depend on a large amount of marked data is overcome. The method comprises the following steps: acquiring and preprocessing an industrial production steel picture; constructing an upstream steel surface defect representation learning model; training an upstream steel surface defect representation learning model; constructing a downstream steel surface defect detector; training a downstream steel surface defect detector; acquiring a picture of industrial production steel to be detected; and obtaining an industrial production steel picture defect detection result. According to the method, a large amount of marked data is not needed, and good representation of defect data is obtained by performing self-supervised representation learning on a large amount of unmarked data sets, so that steel picture defect detection in industrial production is realized by using a small amount of marked steel defect pictures.

Description

technical field [0001] The invention relates to the technical field of industrial image detection, in particular to a method for detecting defects in steel product images in industrial production based on self-supervised comparative representation learning technology. Background technique [0002] Defect detection is an important link in industrial production. Steel surface defect detection refers to the detection of scratches, cracks, foreign objects, corrosion, holes and other problems on the steel surface. Traditional detection methods relying on manpower have disadvantages such as low efficiency, and the detection results are easily affected by human subjective factors. With the advancement of science and technology, some nondestructive testing methods have been proposed and applied to various defect detection fields. In the field of steel defect detection, technologies such as magnetic particle testing, penetrant testing, eddy current testing, ultrasonic testing, and X...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T5/00G06K9/62G06N3/04G06N3/08G06V10/74
CPCG06T7/0008G06N3/088G06T2207/10024G06T2207/20081G06T2207/20084G06T2207/30136G06N3/045G06F18/22G06T5/90
Inventor 杨静胡学进何立新孙恒辉张召霞
Owner HEFEI UNIV
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