X—Ray Digital Image Advanced Processing and Buffer Layer Defect Intelligent Identification of Power Cable

Abstract

With the rapid modernization of the city, power cable has been widely used in the process of urban construction. In the course of cable operation, cable faults become more and more frequent, which has a great impact on national economy and people’s life. The method of digital X-ray imaging can realize the nondestructive testing of power cable body and obtain clear and intuitive X-ray digital image. But it lacks the advanced processing and defect recognition method of X-ray digital image, and can not directly detect and identify the cable body and defect from the original digital image. Therefore, this paper studies the power cable X-ray digital image advanced processing and buffer layer defect intelligent identification technology. By using gray scale processing technology, the original image gray scale range is compressed to the human eye identifiable range, and then the defect identification is carried out. Then the traditional convolution neural network CNN and the full convolution neural network FCN are used to train the image data to realize the intelligent recognition of the power cable buffer layer defect. The research shows that compared with the traditional convolution neural network CNN, the full convolution neural network FCN proposed in this paper has more clear and intuitive recognition effect.

High-Voltage Cable Buffer Layer Ablation Fault Identification Based on Artificial Intelligence and Frequency Domain Impedance Spectroscopy

“…However, this method involves complex detection steps and requires offline testing. Additionally, reference [13] explores the use of deep X-ray digital image processing and intelligent identification technology for power cable detection. This approach achieves the identification of faults in cable buffer layers by combining X-ray digital imaging with intelligent algorithms.…”

A method based on rotated object detection to detect defects regarding distance in strain clamps

“…In recent years, there have been many studies on X-ray image-based detection of defects in strain clamps: Pengwu Li [5] pre-processed the image first and then detected the position of areas of interest using template matching, eventually verified flaws using cumulative value image of the marked flaw's grey value distribution. Sanwei Liu [6] compressed the original image's greyscale range to a range that is visible to humans using grey scale processing technologies and then utilized the complete convolution neural network FCN to find flaws. Yanwu Dong [7] used the critical point detection network HRNet32 to detect defects.…”

X-ray image enhancement based on gradient domain and illumination image estimation for complex castings

“…X-ray image has been used in modern industrial nondestructive testing widely. It can show the internal structures, composition and internal defects of the target clearly, accurately and intuitively [1][2][3][4][5]. Due to the uneven thickness of different structures, there are many regions with different thicknesses, and the edges of these regions have gray-scale value mutations, it causes the contrast of the image to decrease.…”