Defect detection in metallic structures through AMR C-scan images using deep learning method

Nowadays, nondestructive evaluations (NDE) techniques for the diagnosis of defects in the industrial components follow three steps: detection, location, and Determination of defect profile. Despite the fact that the NDE techniques available in the industry have fairly acceptable results for defect detection and localization, but accurate diagnosis of the shape, dimensions, and depth of the defect still remained a challenging task. In this paper, a method for reliable estimation of defect profile in conductive materials is presented using an eddy current testing (ECT) based measurement system and a post-processing technique based on deep learning approach. Accordingly, a deep learning method is used for defect characterization in metallic structures through magnetic field C-scan images which have been obtained by an anisotropic magneto-resistive (AMR) sensor. In this regards, after modeling and regulating the deep convolutional neural network (DCNN) to apply to the obtained C-scan images, the performance of the proposed deep learning method is compared with the conventional artificial neural networks (ANNs) such as multi-layer perceptron (MLP) and Radial based function (RBF) on a number of specimens with different known defects. Results confirm the superiority of the proposed approach relative to other conventional methods for defect profile estimation.