Defects detection based on sparse regularization method for electromagnetic tomography (EMT)

In this paper, we propose a new metal defect detection method using electromagnetic tomography (EMT) technique, which is used to measure the alternating magnetic signal modulated by defects in the metal, and then the distribution of defects is reconstructed. Due to the sparsity of the defect distribution, the l 1 regularization method for EMT reconstruction is presented to solve the sparse problem. As a result, the l 2 regularization can be over-smoothing effect of traditional avoided effectively. A simulation model is designed and the forward problem of the model is calculated using electromagnetic finite-element method. Furthermore, the laboratory experiment and simulation results indicate that the sizes and positions of defects can be effectively distinguished by the new method.