Infrared thermal image defect feature recognition method based on dynamic multi-objective optimization

Abstract

The invention discloses an infrared thermal image defect feature recognition method based on dynamic multi-objective optimization. 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; the pixel value similarity of each type ofpixel points and the same type of pixel points is considered; the difference with different types of pixel points is realized; constructing a corresponding multi-objective function; meanwhile, afterthe environment is changed each time; Prediction mechanism, a guiding direction is provided for population evolution; a multi-objective optimization algorithm is helped to quickly respond to the new change; The method has the advantages that the dimension reduction result of the thermal image sequence is acquired by the aid of the feature extraction algorithm, the defect features of the infrared thermal image are extracted by the aid of the pulse coupling neural network, accordingly, accurate selection of transient heat representations (temperature points) can be realized, the accuracy of defect feature extraction can be guaranteed, and computing consumption of the transient heat representations for acquiring various types of information in dynamic environments can be reduced.

Description

technical field [0001] The invention belongs to the technical field of defect detection, and more specifically relates to a defect feature recognition method of an infrared thermal image based on dynamic multi-objective optimization. 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...

AI Technical Summary

Problems solved by technology

Under ideal conditions without considering environmental factors, representative temperature points that can fully characterize each category of information are obtained, but if in a dynamic environment, the entire calculation step is performed in each environment, the time consumption is large and the response is slow

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