Non-destructive testing method for weld defects based on computer vision

Abstract

The invention relates to a non-destructive detection method for weld defects based on computer vision, which includes the following contents: performing semantic segmentation on the X-ray film of the weld to obtain the edge of the weld, and performing connected domain analysis on the inner area of ​​the edge of the weld to obtain the connectivity inside the weld domain; segment the edge of each connected domain to obtain the outer edge near the base metal side and the inner edge near the center of the weld; respectively obtain the recognition, blackness and straightness of the inner edge and outer edge; Use the recognition and blackness to obtain the definition of the inner edge and the outer edge; get the unfused rate of the connected domain according to the definition difference and flatness difference between the inner edge and the outer edge; set the threshold, and connect the threshold and the connection The unfused rate corresponding to the domain is compared, and the connected domain is marked according to the comparison result of the threshold value and the unfused rate corresponding to the connected domain. According to the technical means proposed by the present invention, the non-fusion defect of the weld can be accurately identified, and the identification of easily confused types of the weld defect is more accurate.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a computer vision-based nondestructive detection method for weld defects. Background technique [0002] In the welding process, various welding defects will be generated due to various factors such as improper operation or unqualified welding materials. In order to detect these defects, the prior art uses X-rays to irradiate the weld seam from top to bottom According to the absorption of x-rays by the weld metal to obtain the x-ray base plate of the weld, non-destructive testing can be realized, that is, the internal defects of the weld can be detected without destroying the weld. When a weld is flawed, features such as shadows of different shapes are formed on it. Based on these features, the operator can identify defects in the weld. [0003] The defects on the existing x-ray film of the welding seam are often directly identified manually, which is inefficient. Other neural ne...

AI Technical Summary

Problems solved by technology

These two types of defects are similar in shape, and if they are confused, they will cause misjudgment of the degree of damage to the weld and cause danger.

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