Multi-type damage detection feature analysis method for large-size test piece
A technology of damage detection and feature analysis, applied in image analysis, computing models, biological models, etc., can solve problems such as missing defects, affecting the accuracy of defect quantitative analysis, and reducing detection integrity, so as to improve detection capabilities and detail performance ability, and the effect of improving the performance of defect characterization
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Embodiment 1
[0227] Such as Figure 1-3 Shown: a kind of large-size specimen multi-type damage detection characteristic analysis method of the present invention, comprises the following steps:
[0228] Step 1. Perform multiple infrared inspections on large-size specimens to obtain multiple thermal image sequences of large-scale specimens, and use infrared feature extraction and infrared thermal image reconstruction algorithms to obtain large-scale specimens from multiple thermal image sequences Multiple reconstructed infrared thermal images, the specific methods include:
[0229] Step S11, using a three-dimensional matrix set {S 1 ,...,S i ,...,S |C|}, where S i Indicates the thermal image sequence obtained by the infrared thermal imager in the i-th infrared detection, |C| indicates the total number of thermal image sequences; S i (m,n,t) indicates the temperature value at the coordinate position of the mth row and nth column of the tth frame thermal image in the ith thermal image seq...
Embodiment 2
[0330] Such as Figure 16-19 Shown: a kind of multi-type damage detection image feature extraction recognition method of the present invention, comprises the following steps:
[0331] Step 1. Using infrared feature extraction and infrared thermal image reconstruction algorithm to obtain reconstructed infrared thermal images from the infrared thermal image sequence, the specific steps are:
[0332] Step S11, based on the transient thermal response data extraction algorithm with block-variable step size, a valuable transient thermal response data set X(g) is extracted from the thermal image sequence S obtained by the infrared thermal imager; where , S (i, j, t) represents the i (i = 1, ..., I) line (I is the total number of rows), the pixel value of the jth (j=1,...J) column (J is the total number of columns); the thermal image sequence is decomposed into K different data blocks by thresholding k S(i n , j m ,t) where k represents the kth sub-data block, i n , j m , t repr...
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