A Method of Infrared Technology Defect Reconstruction and Feature Extraction Based on Additive Fuzzy

Abstract

The invention discloses an infrared technology defect reconstruction and feature extraction method based on additive fuzzy, which establishes a multi-region defect reconstruction model through the infrared thermal image sequence of the test piece to be tested, and uses variational Bayesian inference to reconstruct different Defects in the spatial region; since the signal representing internal defects in the collected data is weak, and noise may be introduced by parameter assumptions in the process of defect reconstruction, we need to highlight internal defect information and remove noise interference, so we pass The color extraction filter and the edge contour extraction filter extract the color features and contour features of the image, then optimize the defect color map, and finally fuse the optimized color map and contour features through inverse transformation, so that it can clearly describe different time and space area defects.

Description

technical field [0001] The invention belongs to the technical field of defect detection, and more particularly, relates to a method for defect reconstruction and feature extraction based on additive blurring in infrared technology. Background technique [0002] Infrared thermal imaging technology has been widely used in the automotive industry, shipbuilding industry, petrochemical industry and aerospace fields. Because it does not need to directly contact the test piece to be tested, the detection is fast and efficient, and it is portable, so it can effectively achieve non-destructive testing of damage. [0003] Infrared technology can be roughly divided into two categories, namely active heating and passive heating. For active heating, artificial energy or heat is required to be given to the test piece to be tested. According to the difference of heating methods, active heating can be divided into: optical excitation, electromagnetic excitation and mechanical excitation. ...

AI Technical Summary

Problems solved by technology

[0006] Secondly, the traditional defect method is mainly aimed at the defect characteristics of different regions in the same space, so the damage information in different spaces will be ignored, which will make a wrong judgment on the defect type of the material. In order to judge the defect situation of the material more accurately, The invention proposes an effective detection method, which can not only judge the damage of the surface space of the material to be detected, remove noise interference, but more importantly, obtain the damage situation of the inner space more accurately

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