RPCA-FFT based imaging of impact damage defects in composite materials
Aiming at the problems of low contrast and easy loss of small defects in the traditional phase-locked thermal imaging defect feature extraction algorithm,a defect detection algorithm based on the combination of robust principal component analysis(RPC A)and FFT is proposed,and the RPC A model is solved by the inexact augmented Lagrange multiplier method(IALM).The original infrared thermal wave sequence vector is transformed into a two-dimensional matrix,and the data is decomposed into two parts by RPC A.The low-rank matrix that approximates the extraction of the non-uniform background,and the sparse matrix that reflects the defective information,and the magnitude and phase maps of the non-uniform background are obtained by using the FFT on the obtained sparse matrix,which is aimed at the problem that IALM needs to artificially introduce the initial value to solve the RPC A model,which affects the opti-mization results.Tyrannosaurus optimization algorithm(TROA)is used to construct the fitness function by selecting the signal-to-heterodyne gain and the background suppression factor,and to optimize the initial equilibrium parameters and the penalty factor.The experimental results show that the image obtained by this algorithm has outstanding con-trast,obvious information of small defects,and better objective evaluation indexes than other algorithms,in which the entropy value has been greatly reduced,effectively suppressing the non-uniform background of the heat wave image.
phase-locked thermographyinfrared image sequencesrobust principal component analysisTyrannosau-rus optimization algorithmdefect detection
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维普
