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Composite material component defect detection method and device based on generative adversarial learning

A composite material component and defect detection technology, which is applied in image data processing, instrumentation, computing, etc., can solve problems such as difficult convergence of the network, small number of defect samples, and unbalanced categories

Active Publication Date: 2020-10-27
XIDIAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present invention provides a method and device for detecting defects of composite material components based on generative confrontation learning, which is used to solve the problem of using deep learning models in the prior art. In the process of detecting defects in composite material components, due to the small number of defect samples in the construction data set and the severe imbalance in categories, it leads to technical problems that the network is difficult to converge and the rate of missed detection is high

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  • Composite material component defect detection method and device based on generative adversarial learning
  • Composite material component defect detection method and device based on generative adversarial learning
  • Composite material component defect detection method and device based on generative adversarial learning

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Embodiment 1

[0093] figure 1 It is a schematic flowchart of a defect detection method for composite material components based on generative adversarial learning in an embodiment of the present invention. like figure 1 As shown, the embodiment of the present invention provides a method for detecting defects in composite material components based on generative confrontation learning, the method comprising:

[0094] Step 110: constructing a generative confrontation model of encoding-decoding structure;

[0095] Further, the construction of the generative confrontation model of the encoding-decoding structure includes: adding an encoder E(x, θ e ), where x represents the image to be detected, θ e For network parameter coder E (x) output z=E (x) is the encoding of described image to be detected; Add confrontation network D ' (z, φ), φ is network parameter, adds to the encoding of described image to be detected constraints, so that the encoding z distribution of the image to be detected is c...

Embodiment 2

[0135] Based on the same inventive concept as the method for detecting defects in composite material components based on generative confrontation learning in the foregoing embodiments, the present invention also provides a device for detecting defects in composite material components based on generative confrontation learning, such as figure 2 As shown, the device includes:

[0136] The first construction unit 11, the first construction unit 11 is used to construct the generated confrontation model of the encoding-decoding structure;

[0137] A first obtaining unit 12, the first obtaining unit 12 is used to obtain a defect data set according to the image to be detected

[0138] The first training unit 13, the first training unit 13 is used to utilize the defect data set training the generative adversarial model;

[0139] A second obtaining unit 14, the second obtaining unit 14 is used to screen and obtain suspected defects according to the trained generation confrontatio...

Embodiment 3

[0174] Based on the same inventive concept as the method for detecting defects of composite material components based on generative confrontation learning in the foregoing embodiments, the present invention also provides a device for detecting defects of composite material components based on generative confrontation learning, on which a computer program is stored. When the program is executed by the processor, the steps of any one of the above-mentioned methods for detecting defects in composite material components based on generative adversarial learning are realized.

[0175] Among them, in image 3 In, bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 will include one or more processors represented by processor 302 and various types of memory represented by memory 304 circuits linked together. The bus 300 may also link together various other circuits, such as peripherals, voltage regulators, and power ma...

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Abstract

The invention provides a composite material component defect detection method and device based on generative adversarial learning, and relates to the field of composite material component defect detection, and the method comprises the steps: constructing a generative adversarial model of a coding-decoding structure; obtaining a defect data set according to a to-be-detected image, and training thegenerative adversarial model by using the defect data set; screening according to the trained generative adversarial model to obtain suspected defects; constructing a convolutional neural network model; training the convolutional neural network model by using the defect data set; and accurately segmenting the suspected defect according to the trained convolutional neural network model to obtain adefect detection result. The technical problems that in the prior art, due to the fact that the number of defect samples in a constructed data set is small and categories are seriously unbalanced, a network is difficult to converge, and the omission ratio is high are solved. The technical effects of accurately detecting the defects of the composite material component and greatly reducing the omission factor are achieved.

Description

technical field [0001] The invention relates to the field of defect detection of composite material components, in particular to a method and device for detecting defects of composite material components based on generative confrontation learning. Background technique [0002] Composite materials are generally composed of matrix and high-strength toughening fibers, which are characterized by heterogeneity and many pores. Since the matrix and fibers cannot be fully combined, the defects of composite components generally manifest as faults, pores, and uneven matrix density. Unlike surface defects, defects in composite components are generally internal and small in size. Non-destructive testing of composite materials is generally carried out using X-rays or computerized tomography (CT). In order to analyze the structure of defects, extremely high-resolution equipment is often required, and the testing cost is relatively high. [0003] However, in the process of implementing t...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/10
CPCG06T7/0004G06T7/10G06T2207/20081G06T2207/20084G06T2207/30108Y02P90/30
Inventor 齐飞安雄刘朝辉梅辉
Owner XIDIAN UNIV
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