Electrical resistance tomography image reconstruction based on one-dimensional multi-branch convolutional neural network combined with attention mechanism

Abstract

Electrical resistance tomography (ERT) is an important branch of process tomography (PT), which has been developed for decades. Image reconstruction is a critical step in ERT, where the object of reconstruction is the conductivity distribution of the measured field. Traditional algorithms cannot accurately establish the mapping between the measured voltage and conductivity distribution. With the development of machine learning, the convolutional neural network (CNN) has become a new image reconstruction method. Specific results have been achieved in ERT image reconstruction using CNNs. This study proposes a one-dimensional multi-branch convolutional neural network (1D-MBCNN) for ERT image reconstruction, which could retain the 1D spatial structure of the measured voltage and adaptively and efficiently extract feature information. COMSOL software and the PyTorch framework are used to build the dataset and train the neural network model, respectively. The advantages of the multi-branch structure and the effectiveness of the attention mechanism in ERT image reconstruction are verified by RIE and CC. We also evaluated the practicality of this method in the ERT system. Based on the results of different experiments, the method proposed in this paper has good imaging accuracy, noise resistance, generalization ability, and robustness.