摘要
In the present paper,a method for reliable estimation of defect profile in CK45 steel structures is presented using an eddy current testing based measurement system and post-processing system based on deep learning technique.So a deep learning method is used to determine the defect characteristics in metallic structures by magnetic field C-scan images obtained by an anisotropic magneto-resistive sensor.Having designed and adjusting the deep convolution neural network and applied it to C-scan images obtained from the measurement system,the performance of deep learning method proposed is compared with conventional artificial neural network methods such as multilayer perceptron and radial basis function on a number of metallic specimens with different defects.The results confirm the superiority of the proposed method for characterizing defects compared to other classical training-oriented methods。