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

Abstract

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.