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Light excitation infrared thermal imaging defect detection method based on structured sparse decomposition

A technology of infrared thermal imaging and sparse decomposition, which is applied in the field of light-excited infrared thermal imaging defect detection based on structured sparse decomposition, can solve the problems of poor accuracy and time-consuming variational Bayesian tensor decomposition method, and achieve improved Effects of detection rate, enhanced resolution, and enhanced contrast

Active Publication Date: 2019-08-09
四川沐迪圣科技有限公司
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Problems solved by technology

But these methods have poor accuracy for detecting defects on complex and irregular surfaces, among them, the variational Bayesian tensor decomposition method is very time-consuming

Method used

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  • Light excitation infrared thermal imaging defect detection method based on structured sparse decomposition
  • Light excitation infrared thermal imaging defect detection method based on structured sparse decomposition
  • Light excitation infrared thermal imaging defect detection method based on structured sparse decomposition

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Experimental program
Comparison scheme
Effect test

Embodiment

[0037] figure 1 It is a flow chart of the light-stimulated infrared thermal imaging defect detection method based on structured sparse decomposition of the present invention.

[0038] For the heat waves generated under the heating of the light source, when defects of different material properties are encountered inside the test piece, different heat densities will appear. By obtaining the surface temperature signal of the test piece, it will be displayed on the computer as a pseudo-color image. In the reflection mode (the thermal imager and the light source are on the same side), when there is a heat-insulating defect inside the specimen, the defect area will appear as a high-temperature area due to heat accumulation; when there is an endothermic defect inside the specimen, the defect area will appear as a It is a low-temperature area, and the distribution of defects is usually characterized by spatial sparseness. The location and number of defects can be determined by observi...

example

[0060] In order to evaluate the algorithm proposed by the present invention, five defect detection algorithms were selected for comparison, namely principal component analysis (PCA), independent component analysis (ICA), thermal signal reconstruction (TSR), pulse phase method (PPT) and variable Decibel Bayesian Tensor Factorization (EVBTF). In order to evaluate the defect detection effect and efficiency of each algorithm, three evaluation indicators are used, which are F-score, signal-to-noise ratio (SNR) and algorithm running time.

[0061] The definition of F-score is as follows:

[0062]

[0063] Among them, Precision is the precision rate, and Recall is the recall rate, which is defined as follows:

[0064]

[0065] Among them, TP represents the number of defects that are actually detected and detected, FP represents the number of defects that are actually not defective but are detected as defects, FN represents the number of defects that are actually not detected, ...

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Abstract

The invention discloses a light excitation infrared thermal imaging defect detection method based on structured sparse decomposition. Wavelet decomposition is carried out on each frame of image of a heat map sequence, only a low frequency part is kept, and a new heat map sequence is formed; heat maps are arranged successively to reconstruct a new matrix; the new matrix is decomposed into the sum of a low rank matrix, a sparse matrix and a noise matrix, wherein the low rank matrix represents the background of a heat image and the sparse matrix represents defects in the heat image; the sparse matrix is further decomposed into a product of a dictionary matrix and a weight matrix, wherein the dictionary matrix is used for representing different thermal modes of different defects on the same test piece and the weight matrix has sparse constraints and non-negative constraints; and a low rank matrix is calculated by using a singular value threshold decomposition method, a dictionary matrix iscalculated by using a vertex component analysis method, a weight matrix is calculated by using a multiplier alternating direction method, and then the sparse matrix is reconstructed into a defect image matrix to realize the defect detection of infrared thermal imaging.

Description

technical field [0001] The invention belongs to the technical field of non-destructive testing, and more specifically relates to a light-stimulated infrared thermal imaging defect detection method based on structured sparse decomposition. Background technique [0002] Non-destructive testing technology is an important means to control product quality and ensure the safe operation of in-service equipment. Infrared thermal imaging detection technology is to measure the temperature through the corresponding relationship between the change process of the radiation energy of the object and the temperature, so as to judge the physical characteristic information of the object. Optically stimulated infrared thermal imaging uses the active heating method of the light source to detect various defects on the surface and inside of the object, which can realize the rapid detection of defects in a wide range of different depths. In recent years, it has been widely used in the field of non...

Claims

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Application Information

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IPC IPC(8): G01N25/72
CPCG01N25/72
Inventor 高斌刘丽
Owner 四川沐迪圣科技有限公司
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