【摘要】 针对电阻层析成像(ERT)经典算法(LBP、Landweber等)在复杂流型情况下成像精度低的问题，提出一种基于多尺度卷积神经网络(Multi-scale Convolutional Neural Network, MS-CNN)的电阻层析成像图像重建算法。根据气液两相流流型特点，构建有限元模型得到20 000组包含“边界电压向量-电导率分布”的数据集。在典型卷积神经网络Resnet50和Vgg16的基础上，构建针对ERT图像重建问题的MS-CNN。仿真实验结果表明，与Landweber迭代算法和单尺度卷积神经网络算法相比，MS-CNN算法的ICC分别提升了0.715和0.023,RIE分别降低了0.812和0.057。抗噪性测试与静态测试结果表明，MS-CNN算法具有良好的图像重建结果和鲁棒性。更多还原

【Abstract】 Aimed at the problem of low imaging accuracy of classical algorithms(LBP, Landweber, etc.) for electrical resistance tomography(ERT) in complex flow patterns, an image reconstruction algorithm based on multi-scale convolutional neural network(MS-CNN) for electrical resistance tomography is proposed. According to the characteristics of gas-liquid two-phase flow pattern, a finite element model was built to obtain 20,000 data sets containing "boundary voltage vector-conductivity distribution". On the basis of typical convolutional neural networks Resnet50 and VGG16, MS-CNN for ERT image reconstruction was constructed. The simulation results show that compared with Landweber iterative algorithm and single-scale convolutional neural network algorithm, the ICC of MS-CNN algorithm is improved by 0.715 and 0.023, and the RIE is decreased by 0.812 and 0.057 respectively. The anti-noise test and static test results show that the MS-CNN algorithm has good image reconstruction results and robustness.更多还原