A welding defect extraction method and a welding defect detection method

Abstract

The invention discloses a welding defect extraction method, which belongs to the technical field that a welding technology and a digital image processing technology are crossed. According to the method, an original welding image is dissected by NSST (non-subsampled shearlet transform); for a roughly approximate low-frequency component capable of embodying a defect, the rough region of the detect is extracted by a PCNN (pulse coupled neural network); then, inverse NSST is carried out to the low-frequency component and a high-frequency component subjected to background suppression to obtain a high-frequency characteristic image; after the high-frequency characteristic image is subjected to coarse segmentation, the outline of the defect is optimized by an improved CV (Chan-Vese) model to obtain the fine edge of the defect; and finally extracted results are blended to obtain a finally-extracted defect. The invention also discloses a welding defect detection method adopting the welding defect extraction method. A welding defect structure obtained by the method disclosed by the invention has the advantages of more integral structure and clearer detail and outline, and a more reasonable and accurate reference can be provided for further processing the defect.

Description

technical field [0001] The invention relates to a method for extracting welding defects, which is used for structure and contour detection of defects in digital welding images, and belongs to the technical field where welding technology and digital image processing technology intersect. Background technique [0002] Traditional welding defect detection methods mainly rely on manual judgment of digital welding images (such as X-ray welding images or ultrasonic welding images, etc.), which have problems of low efficiency and high false detection rate. With the development of image processing technology, defect detection on digital welding images has become an important means of quality evaluation of welding products. As a key step in defect detection, the accuracy of welding defect extraction directly affects the calculation of defect characteristic parameters and determines the performance of defect detection. [0003] Welding defect images usually have low contrast, large b...

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Problems solved by technology

Zhou Xinxing et al. studied a defect extraction method based on Non-Subsampled Contourlet Transform (NSCT) and PCNN (Zhou Xinxing, Wang Dianhong, Wang Hongliang, et al. Non-subsampled Contourlet Transform and PCNN surface defect automatic Identification method[J].Journal of Applied Fundamental and Engineering Science, 2013,21(1):174-183.), and achieved good results. However, the number of NSCT high-frequency directions used in this method is restricted by the number of decomposition layers. The direction information of the image cannot be expressed optimally, and the PCNN used for high-frequency component defect extraction is difficult to distinguish the small edges of noise and defects

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