Study on noise feature analysis and processing algorithm of magneto-optical image of welding defects

TIAN Meng; GAO Xiangdong; XIE Yuexuan; ZHANG Yanxi

doi:10.7510/jgjs.issn.1001-3806.2023.05.011

LASER TECHNOLOGY > 2023 > 47(5): 646-652. > DOI: 10.7510/jgjs.issn.1001-3806.2023.05.011

TIAN Meng, GAO Xiangdong, XIE Yuexuan, ZHANG Yanxi. Study on noise feature analysis and processing algorithm of magneto-optical image of welding defects[J]. LASER TECHNOLOGY, 2023, 47(5): 646-652. DOI: 10.7510/jgjs.issn.1001-3806.2023.05.011

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Study on noise feature analysis and processing algorithm of magneto-optical image of welding defects

Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou 510006, China

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Received Date: July 05, 2022
Revised Date: August 07, 2022
Published Date: September 24, 2023

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Abstract

Abstract

In order to solve the problem that it was hard to extract the outline information from the magneto-optical image of welding defects, an image denoising and contour detection method was proposed. The crack defect samples were obtained by laser spot welding, and the magnetic flux leakage field of defects was simulated by finite element method. The gray continuity, concentration, and noise characteristic of magneto-optical images were compared and analyzed. The fast non-local mean filtering algorithm was used to remove noise, and compared with the traditional filters. According to the distinction in gradient value between flaw information and background information in magneto-optical images, an operator was used to carry out Otsu threshold segmentation and the edge detection based on the first order gradient graph of the image. The results show that the standard deviation and image entropy of the magneto-optical image processed by this method reaches 30.0465 and 6.0395 respectively, and the image aggregation degree is better, which is closer to the simulation magnetic field curve, and the defect contour information could be better extracted. This result is helpful for defect recognition and target detection.
- sensor technique,
- magneto-optic imaging,
- welding defects,
- fast non-local mean filtering,
- image processing

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