Spatial correlation-based infrared thermal image processing method

Abstract

A spatial correlation-based infrared thermal image processing method disclosed by the present invention comprises the steps of firstly acquiring the video stream data at a heating stage in the real-time and online manners, then extracting the edge information, analyzing the correlation of the adjacent two frames, calculating a correlation coefficient and drawing a correlation coefficient curve changing along with time. The method of the present invention reflects the difference of a thermal response state of a tested piece after the multi-defect coupling and during a heating process, and finally, according to a preset value, several appropriate thermal images are selected to fuse and then carry out the subsequent defect detection.

Description

technical field [0001] The invention belongs to the technical field of infrared thermal image processing, and more specifically relates to a spatial correlation-based infrared thermal image processing method. Background technique [0002] At present, infrared thermal imaging non-destructive testing technology has become an important branch of non-destructive testing technology. In the non-destructive testing technology of infrared thermal imaging, the research on defect extraction and image enhancement has obtained many achievements. Infrared thermal imaging detection technology can have many detection methods. The excitation source of pulse thermal imaging non-destructive testing is active, and the commonly used excitation sources include hot water, thermal air flow, flash lamp, ultrasonic wave, microwave, laser and electric current. Different heating methods produce different forms of heat. For the current excitation heating method, the heating range is wide, the heat c...

AI Technical Summary

Problems solved by technology

[0008] From the above scientific research results, although the extraction of defect information has a good effect, a large number of current research methods still collect all thermal image sequences and then process them, or collect data in the heating stage or cooling stage separately. In this way, not only the amount of calculation is too large, but also there is information redundancy, which makes the detection efficiency not high

At the same time, the defect detection of the above research is still based on a single defect or the coupling effect of multiple defects can be ignored, and does not consider the simultaneous existence and mutual influence of multiple defects, and there is a lack of corresponding research on multi-defect detection

Although methods such as Fourier transform, principal component analysis, and independent component analysis are effective for single defect detection, their effectiveness needs to be verified when multiple defects exist and thermal fields are coupled with each other.

At the same time, the above numerical methods do not consider the physical meaning of the eddy current pulse thermal imaging data, and the mining of the physical information of the data itself is extremely limited

Images