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用于可视化不同磁化方向下漏磁检测的图像配准

Image Registration for Visualizing Magnetic Flux Leakage Testing under Different Orientations of Magnetization.

作者信息

Li Shengping, Zhang Jie, Liu Gaofei, Chen Nanhui, Tian Lulu, Bai Libing, Chen Cong

机构信息

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

China Petroleum Pipeline Insptection Technologies Co., Ltd., Langfang 065000, China.

出版信息

Entropy (Basel). 2023 Jan 13;25(1):167. doi: 10.3390/e25010167.

DOI:10.3390/e25010167
PMID:36673307
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9858165/
Abstract

The Magnetic Flux Leakage (MFL) visualization technique is widely used in the surface defect inspection of ferromagnetic materials. However, the information of the images detected through the MFL method is incomplete when the defect (especially for the cracks) is complex, and some information would be lost when magnetized unidirectionally. Then, the multidirectional magnetization method is proposed to fuse the images detected under different magnetization orientations. It causes a critical problem: the existing image registration methods cannot be applied to align the images because the images are different when detected under different magnetization orientations. This study presents a novel image registration method for MFL visualization to solve this problem. In order to evaluate the registration, and to fuse the information detected in different directions, the mutual information between the reference image and the MFL image calculated by the forward model is designed as a measure. Furthermore, Particle Swarm Optimization (PSO) is used to optimize the registration process. The comparative experimental results demonstrate that this method has a higher registration accuracy for the MFL images of complex cracks than the existing methods.

摘要

漏磁（MFL）可视化技术广泛应用于铁磁材料的表面缺陷检测。然而，当缺陷（尤其是裂纹）复杂时，通过MFL方法检测到的图像信息不完整，并且单向磁化时会丢失一些信息。因此，提出了多向磁化方法来融合在不同磁化方向下检测到的图像。这引发了一个关键问题：现有的图像配准方法无法用于对齐这些图像，因为在不同磁化方向下检测到的图像是不同的。本研究提出了一种用于MFL可视化的新型图像配准方法来解决这一问题。为了评估配准效果并融合不同方向检测到的信息，将通过正向模型计算得到的参考图像与MFL图像之间的互信息设计为一种度量。此外，采用粒子群优化（PSO）来优化配准过程。对比实验结果表明，该方法对于复杂裂纹的MFL图像具有比现有方法更高的配准精度。

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