Segmentation strengthening method for damage detection image of aerospace composite material

Abstract

The invention discloses a segmentation strengthening method for a damage detection image of an aerospace composite material. The segmentation strengthening method comprises the following steps: extracting typical transient thermal response of a defect; obtaining an infrared reconstruction image; utilizing multi-target to measure segmentation performance of three aspects ofnoise removal, detail reservation and edge maintenance, and solving the weight coefficient of each segmentation performance; constructing infrared image segmentation function data under the guidance of three purposes of noise removal, detail reservation and edge maintenance; obtaining a weight coefficient of the target function; carrying out image segmentation on the full-pixel infrared image obtained through reconstruction; and achieving infrared full-pixel image segmentation on the image segmentation layer to obtain a segmented image of the defect. According to the method, the multi-objective optimization theory is utilized to segment the damage defect area in the infrared reconstructed image, objective functions are respectively constructed for the noise problem and the edge blur problem, the area segmentation precision is improved, the false detection rate is reduced, the readability of the damage defect area is effectively enhanced, and quantitative research of complex defects is facilitated.

Description

technical field [0001] The invention belongs to the technical field of damage detection and maintenance support of aerospace vehicles, and more specifically, the invention relates to a method for segmenting and enhancing damage detection images of aerospace composite materials. Background technique [0002] With the urgent need for weight reduction of aerospace vehicles, lightweight structural materials with excellent mechanical properties, especially lightweight composite materials represented by high-strength / high-modulus carbon fiber composite materials and honeycomb structure materials, have increasingly become the focus of aerospace research. hotspot. At the same time, composite materials with special functional uses, such as stealth coating materials, carbon-based heat-resistant materials and other functional composite materials, are also more widely used in the aerospace field. However, in the process of manufacturing, assembly processing or real-time use of composit...

AI Technical Summary

Problems solved by technology

On the one hand, if the defect detection rate is improved to a certain extent under the premise of fully satisfying the preservation of details, the noise is also retained, which is easy to cause misjudgment of defect recognition, resulting in an increase in the false detection rate

On the other hand, if only the overall denoising of the image is satisfied, the size of the damage defects caused by the impact of tiny debris is small and the number is large, and these tiny defects similar to the noise will be removed together with the denoising process, reducing the damage of defects. Detection rate and detection accuracy

Therefore, the above-mentioned conventional segmentation method is applied to the object of the present invention, that is, the infrared reconstructed image of the defect. Since the false detection rate and detection rate of the defect cannot be weighed, the segmentation effect is not satisfactory.

Especially considering that the infrared thermal image reflects the thermal radiation information of the test piece, which is easily affected by the environment, imaging links, etc., so that the background noise of the obtained infrared reconstruction image of defects is large.

At the same time, due to the difference in surface thermal radiation capabilities between the defect area and the background area, the edges of the infrared reconstructed image of the defect are not smooth enough, and the division of edge areas is not clear enough, which is not conducive to image segmentation

Images