改进Canny算子的小管径弯头漏磁缺陷图像量化方法

Image quantification method of magnetic flux leakage defect for small-diameter pipe elbow based on improved Canny operator

弯头作为管道的重要组成部件,受流体冲刷侵蚀等形成的缺陷对其安全运行构成威胁。漏磁检测是管道缺陷检测的有效技术,缺陷的精确量化具有重要意义。为了研究小管径弯头不同位置缺陷图像规律,并提高缺陷量化精度,提出一种改进Canny算子的小管径弯头漏磁缺陷图像量化方法。通过建立小管径弯头漏磁内检测仿真模型,分析了弯头不同位置处金属损失缺陷的图像规律。采用形态滤波和OTSU优化Canny算子,结合图像处理方法构建了缺陷图像量化模型,并对弯头不同位置缺陷深度量化做修正处理。实验结果表明,弯头不同位置缺陷在图像上呈现出明显的差异性,量化模型对缺陷长度和宽度的量化具有较高的精度,误差均小于2 mm。缺陷深度的量化误差较大,修正后深度量化的精度为86.34%,满足金属损失缺陷量化精度需求。该方法可实现缺陷漏磁图像量化处理,对管道漏磁缺陷量化具有一定意义。

The elbow is a crucial component of a pipeline and can be subject to fluid scour erosion and other defects that threaten its safe operation.A highly effective method for detecting pipeline defects is magnetic flux leakage(MFL)detection,and accurately quantifying these defects is of significant importance.In order to enhance comprehension of defect patterns in small-diameter pipe elbows and improve the measurement accuracy of defects,this paper proposed a novel image quantification method for MFL defects in small-diameter pipe elbows using an improved Canny operator.The image features of metal loss defects at different locations of the elbow were analyzed by establishing a simulation model for MFL detection in small-diameter pipe elbows.The defect image quantization model was constructed by using morphological filtering and OTSU optimized Canny operator,combined with image processing methods.This model corrected the depth quantification of defects from various positions on the elbow.The experimental results clearly showed that there are differences in the images of defects at different positions on the elbow.The accuracy of the quantification model in measuring defect length and width is precise,with an error rate of less than 2 mm.However,quantifying the depth of defects revealed a more significant error rate,with a precision of 86.34%post-correction.Nonetheless,this level of accuracy satisfies the necessary standard for detecting metal loss defects.The suggested approach allows for batch processing of defect images and therefore holds considerable importance in detecting MFL defects in pipelines.