Power borehole image recognition technology based on convolution feature fine-grained optimization

Abstract

With the development of power system, the technology of power well is facing many problems. How to not only detect the power well information, but also characterize the measured data in an easy-to-explain way, so that the staff can understand and analyze the power well casing, is of great significance to the detection of pipeline damage and the development of the power industry. Nowadays, the development of informatioa n processing and microelectronics technology, especially the rapid development of image processing technology, provides a more theoretical basis for the realization of power well detection technology, and expands more possibilities for the study of detection data. A clutter suppression method based on fine-grained optimization decomposition of the convolution feature is proposed. A noise constraint is added into the non-negative matrix factorization, and a base matrix and a coefficient matrix to be factorized are assumed to be non-negative exponential distributions in probability. The non-negative matrix factorization is carried out by using a convolutional neural network to obtain the approximate inference of the base matrix and the coefficient matrix. The selection of parameter values and the input of horizontal gradients are determined. The low-rank matrix representation of clutter components is obtained so as to separate the clutter from the image. The experimental process is compared with the simulation data, which verifies that the model has good robustness for the image recognition in the power well.