Method for estimating defect degree of carbon fiber surface based on region growing algorithm

Abstract

The invention discloses a method for estimating the defect degree of carbon fiber surface based on a region growing algorithm. Firstly, image preprocessing is performed on a carbon fiber profile imageto obtain a preprocessed surface defect enhancement image; then, an ROI region of the carbon fiber profile image is extracted based on the preprocessed image so that defect regions can be found out better; after that, the regional growth algorithm is used for segmenting the defect regions of the researched carbon fiber surface image from the image; finally, the proportion of defects in the imageto the entire image is calculated by using a matlab correlation program, and the number of the defects in the image can be counted. According to the method, not only can the tiny defects existing on the surface of a carbon fiber material be effectively segmented, but also the proportion of the tiny defects to the whole image can be calculated, a reliable measurement index is provided for evaluating the advantages and disadvantages of the carbon fiber material by professionals, and the effect of assisting in evaluation of the quality of the evaluated carbon fiber material is achieved.

Description

technical field [0001] The invention relates to the estimation of surface defects of carbon fiber materials, in particular to a method for estimating the degree of carbon fiber surface defects based on a region growth algorithm. Background technique [0002] Image defects are areas or content that affect the overall image effect or interfere with the expression of image information in ordinary images, so that researchers cannot accurately understand the information conveyed by the image, and then interfere with the computer's recognition of the image during the research process. Some are called image defects. In the processing of carbon fiber, defects will be caused on the surface of carbon fiber. There are many types of carbon fiber surface defects, and the reasons for their formation are also various. During the processing of carbon fiber composite materials, due to the difference in the strength of the interlayer binder and the strength of the fiber itself and the diffe...

AI Technical Summary

Problems solved by technology

The segmentation method based on mathematical morphology, such as the watershed algorithm, has a good response to weak edges, but the noise in the image and the subtle gray level changes on the surface of the object will cause over-segmentation; the segmentation algorithm based on edge detection is not suitable for multi-channel For images with little correlation between the image and the eigenvalues, it is difficult to obtain accurate results for image segmentation problems where there is no obvious grayscale difference in the image or the grayscale value range of each object has a large overlap. When there are many noise signals in the image, the grayscale of the target When the value is almost the same as the background, it is easy to lose part of the boundary information

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