A multi-objective optimization infrared thermal image defect feature extraction method based on homogeneous evolution

Abstract

The invention discloses a multi-objective optimization infrared thermal image defect feature extraction method based on homogeneous evolution. The transient thermal response of a pixel point is selected by changing the step length of a thermal image sequence; FCM is used for classification. obtaining the category of the transient thermal response of each pixel point; secondly, considering the pixel value (temperature value) similarity of each type of pixel points and the same type of pixel points; meanwhile, considering the difference between the pixel point (temperature point) and different types of pixel points (temperature points), constructing a corresponding multi-objective function, obtaining a dimension reduction result of the thermal image sequence by utilizing a decomposition-based homoevolution multi-objective evolution algorithm, and finally, carrying out feature extraction by utilizing a pulse coupling neural network so as to extract defect features of the infrared thermalimage. Through the uniform evolution direction of the solution, the difference and the similarity are comprehensively considered, accurate selection of representative pixel points (temperature points)is realized, and the accuracy of defect feature extraction is ensured.

Description

technical field [0001] The invention belongs to the technical field of defect detection, and more specifically relates to a uniform evolution-based multi-objective optimized infrared thermal image defect feature extraction method. Background technique [0002] Infrared thermal image detection technology obtains structural information on and below the surface of the material by controlling the thermal excitation method and measuring the temperature field change on the surface of the material, so as to achieve the purpose of detection. When acquiring structural information, infrared thermal imaging cameras are often used to record the temperature field information of the surface or subsurface of the specimen over time, and convert it into a sequence of thermal images for presentation. Due to the huge amount of data and strong noise interference of the thermal image sequence obtained by the infrared thermal imager, in order to obtain better detection results, it is necessary to...

AI Technical Summary

Problems solved by technology

In this process, the representative temperature points of each category are obtained by using the correlation between different categories, but the similarity between the representative temperature points and the same temperature points is not studied, and the selected representative temperature points are not enough to characterize the characteristics of this category, so Goals that need to consider both differences and similarities

In addition, the method is to search for a regionally representative thermal response temperature point in each category, which is the distance from other cluster centers and the largest thermal response data in the corresponding category, and the thermal response data of the representative temperature points of all categories The response data constitutes a two-dimensional matrix, and these representative temperature points are incomplete for the information representation of the corresponding category, so the defect features extracted after linear transformation are inaccurate, thus failing to achieve a certain accuracy

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