The method of the pipeline magnetic flux leakage detection image formation based on the artificial intelligence

Lijian Yang, Meng Shi, Song-wei Gao
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引用次数: 4

Abstract

For long distance pipelines of oil and gas pipeline, the magnetic flux leakage detection in data storage and discriminant quantity is larger and the problem of recognition is slow. By using the convolution neural network, the detection data of the leakage magnetic field is processed to realize the detection of magnetic leakage and the intelligent processing of the data discriminant. The method of the pipeline magnetic flux leakage detection image formation based on the artificial intelligence achieving leakage magnetic detection imaging, and it is the earlier processing of the intelligence identification. The original image is analyzed and pretreated by the imaging processing method of image corrosion and grayscale. The method is highlights the image features, and it makes the pipeline features display clearly, which provides a clearly image feature for the intelligent identification of pipeline features and improves the efficiency of intelligent identification of the magnetic flux leakage data.
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基于人工智能的管道漏磁检测图像生成方法
对于油气管道长距离管道,漏磁检测在数据存储和判别量上较大,识别速度较慢。利用卷积神经网络对漏磁场检测数据进行处理,实现漏磁检测和数据判别的智能处理。基于人工智能的管道漏磁检测图像生成方法实现了漏磁检测成像,是智能识别的前期处理。采用图像腐蚀和灰度化的成像处理方法对原始图像进行分析和预处理。该方法突出图像特征,使管道特征显示清晰,为管道特征的智能识别提供了清晰的图像特征,提高了漏磁数据智能识别的效率。
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