基于自适应滑动窗口的漏磁数据灰度化增强方法

A Gray Enhancement Method with Magnetic Flux Leakage Signals Based on Selfadaptive Sliding Window

管道无损检测领域中传统的漏磁数据灰度可视化方法在缺陷曲线视图显示时具有延迟大、灰度视图辨识度低的缺点。针对该问题,本文提出了一种基于自适应滑动窗口的漏磁数据灰度化特征增强方法。首先,根据漏磁数据特点建立非等量分类标签并设置降采样比,实现对原始漏磁数据的降采样显示;然后,根据预处理后的漏磁数据设计自适应滑动窗口及灰度值补偿算法,实现漏磁数据的局部分段灰度映射;最后,基于漏磁数据分类标签设计自适应灰度映射方法,得到清晰的漏磁数据灰度视图。通过对比实验,验证了所提方法的先进性和有效性。

In pipeline nondestructive testing,the traditional gray-scale visualization method of magnetic flux leakage(MFL)data has the disadvantages of large display delay and low identification degree for the defect curve view and the gray-scale view,respectively. To solve these problems,an self-adaptive sliding window based gray feature enhancement method for MFL data is proposed. Firstly,according to the characteristics of MFL data,the unequal classification label is established and the down-sampling ratio is set to realize the down-sampling display of the original MFL data. Then,according to the preprocessed MFL data,the self-adaptive sliding window and gray value compensation algorithm are designed to realize the local gray mapping of MFL data. Finally,an self-adaptive gray mapping method is designed based on MFL data classification label to get a clear gray view of MFL data. Some comparative experiments are conducted to verify the advance and effectiveness of the proposed method.