Development of magnetic image super-resolution model for nondestructive testing

Abstract

Electromagnetic testing (ET) is a method in the nondestructive evaluation (NDE) methodology for detecting and evaluating cracks in engineering structures by measuring the distribution of the electromagnetic field. With the development of advanced deep learning (DL) techniques, DL is getting attention in crack detection problems through the measuring signals of NDE systems. However, obtaining the necessary big data for such experiments is a time-consuming and expensive task. Moreover, the quality of the crack detection results heavily depends on the quality and spatial resolution of the input image data. In this research, we solve the above-mentioned problems by proposing a DL-based method on the magnetic image for both the image super-resolution and image denoise. We first build a magnetic image simulation framework for the ET system, and thus, it is possible to build a large dataset for training the DL model. Second, we build a DL model for enhancing the quality of the magnetic image in both the spatial resolution and the denoise signal. This approach helps to reduce the cost of experiments (i.e. reducing the number of sensors for each experiment), sensor fabrication process, and noise removal of the ET system. We evaluate the proposed approach on several cracks with different sizes and shapes of aluminum specimens. The evaluation metrics, such as the structural similarity index measure (SSIM) and root mean square error show good performance of the proposed approach and compared to traditional methods.