The first step to quantifying complex defects is knowing its geometry, shape, and orientation. This is evidently challenging in electromagnetic nondestructive testing (ENDT), especially for subsurface complex defect detection, in cases where no information about defect or material is known or given. In this article, we propose a heuristic approach for the visualization, verification, and validation of complex defects in magnetic flux leakage (MFL) testing. This method is based on MFL experiment using magneto-optical images (MOIs) that are obtained from four different magnetization patterns. Using the proposed magnetization patterns, images of complex defect are captured with the aid of a charge-coupled device (CCD) camera, based on the interaction of magnetic field distribution and detected defects, from different angles and directions. An enhanced ant colony algorithm (EACA) is then used to reconstruct complex defect shapes from captured images using a mean image approach. The reconstructed image (mean image) reveals the defect shape with high precision for which the EACA is able to extract important defect features such as specific edges that might be hidden or blurred. This approach based on results has proved to provide promising solution to visually verifying complex defects in MFL, leading to a more simplified and faster way to characterize these defects as compared with conventional methods, using their shapes and orientation.

中文翻译：

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漏磁检测中缺陷形状特征重建和提取的启发式算法


量化复杂缺陷的第一步是了解其几何形状、形状和方向。这在电磁无损检测 (ENDT) 中显然具有挑战性，特别是在不知道或给出有关缺陷或材料的信息的情况下，尤其是对于地下复杂缺陷检测。在本文中，我们提出了一种启发式方法，用于漏磁 (MFL) 测试中复杂缺陷的可视化、验证和确认。该方法基于使用从四种不同磁化模式获得的磁光图像 (MOI) 的 MFL 实验。使用所提出的磁化模式，借助电荷耦合器件（CCD）相机，基于磁场分布和检测到的缺陷的相互作用，从不同角度和方向捕获复杂缺陷的图像。然后使用增强型蚁群算法 (EACA) 通过平均图像方法从捕获的图像中重建复杂的缺陷形状。重建图像（平均图像）以高精度揭示缺陷形状，EACA 能够提取重要的缺陷特征，例如可能隐藏或模糊的特定边缘。事实证明，这种基于结果的方法为目视验证 MFL 中的复杂缺陷提供了有前途的解决方案，与传统方法相比，使用缺陷的形状和方向可以更简单、更快速地表征这些缺陷。