Abstract: In order to meet the demand for high-precision monitoring of medium distribution in the construction of collaborative energy transmission corridors for new power systems, a complex-valued multi-frequency electrical capacitance tomography (CVMF-ECT) based on an end-to-end neural network is proposed as an image reconstruction method. First, a new background prediction network is constructed for predicting the background distribution of the medium to provide priori constraints for preliminary imaging. Second, an image reconstruction enhancement network is used to classify the preliminary reconstructed images at the pixel level, so that each pixel corresponds to a media type, accurately reflecting the media distribution and its characteristics in the pipeline, and realizing high-precision image reconstruction. Finally, the structural similarity is used to quantitatively assess the imaging quality, and compared with typical algorithms for verification. The simulation results show that the structural similarity between the reconstructed image enhanced by neural network and the target image are all higher than 0.85, and the high-precision media distribution image can still be output under the influence of noise interference and frequency change. This method provides a new technical idea for high-precision imaging of medium distribution under high conductivity.

Key words: end-to-end neural network, complex-valued multifrequency capacitance tomography(CVMF-ECT); image reconstruction, collaborative energy transmission, non-destructive testing