Pipeline visualization based on RIR-RepVGG network

Abstract

Electrical Capacitance Tomography (ECT) is a process tomography technology based on capacitance-sensitive field, which is of vital importance to accelerate the development of industrial intelligence by measuring the required capacitance data through sensors to achieve the flow pattern detection of filled pipes and thus meet the controllability of pipe flow patterns. The traditional ECT flow pattern recognition method has low recognition rate and more complicated operation. In this paper, the detection method of filling pipeline is studied based on electrical capacitance tomography (ECT) technology. Aiming at the low imaging accuracy of traditional ECT reconstruction algorithms, an ECT image reconstruction method based on improved residual neural network was proposed. The RIR-RepVGG residual network structure is proposed and a nonlinear mapping network is designed. The improved residual network is used to establish the mapping relationship between the capacitance vector and the two-phase flow image. Through the static study by filling pipeline simulation experiments and static experiments, it is proved that the ECT image reconstruction method proposed in this paper can reduce the artifacts and deformation of the reconstructed image, improve the reconstruction accuracy, and has a better reconstruction effect for complex flow patterns. Application is of great significance.