Imaging steel plate defects by planar electromagnetic tomography with deep convolutional neural network

The accurate detection and evaluation of metal material defects is of great significance to the current production and life. When the metal material is damaged, its internal magnetic permeability will change locally. The electromagnetic tomography (EMT) technique can be used to reconstruct the combined permeability and conductivity distribution of metal materials. However, the ill-posed and ill-conditioned nature of the EMT inverse problem, coupled with the high permeability and conductivity of ferromagnetic materials, poses significant challenges for defect detection. To address this, we propose an improved deep learning model, P-LeNet, based on a convolutional neural network for EMT defect detection and image reconstruction. By establishing a nonlinear mapping between induced voltage measurements and the combined permeability and conductivity distribution, the model extracts multi-scale features to enhance reconstruction accuracy and robustness. The correlation coefficient and image error are used as indicators to evaluate the quality of image reconstruction. In order to visually demonstrate the imaging effect of the proposed model, numerical simulations are performed. The imaging results show that the proposed P-LeNet model is superior to traditional algorithms in imaging accuracy, artifact suppression, and overall performance. At the same time, Gaussian white noise is introduced to evaluate the anti-noise ability of the model, and the random sample is used to test the generalization ability of the model to fully demonstrate the superiority and application potential of the method. Furthermore, experiments with a nine-coil planar EMT sensor are conducted to verify the effectiveness and superiority of the proposed model.