Automatic Detection of Local Obstacles in a Long Length RE-123 Coated Conductor by Deep Learning Based Image Classification in Reel-to-Reel Magnetic Microscopy

Abstract

We have succeeded in detecting local obstacles automatically in a 200-m-long RE-123 coated conductor (CC) by introducing deep-learning based image recognition in reel-to-reel scanning Hall probe microscopy (RTR-SHPM). Longitudinal critical current ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_c</i> ) homogeneity in CCs is one of the most important requirements for practical applications. Usually, such properties as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_c</i> variation as a function of longitudinal coordinate is characterized by magnetization measurements adopting Hall probe array as a de facto standard characterization method for ensuring uniformity in long CCs. In this measurement, local <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_c</i> drop indicates the existence of current blocking obstacles. The group of authors also developed a magnetic microscopy applicable to reel-to-reel continuous measurements, RTR-SHPM, which makes it possible to visualize two-dimensional critical current density ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J_c</i> ) in the tape plane because of its high-resolution imaging along the tape width. As a result, more elaborate defect detection has been enabled. However, in the conventional technique, the observation depends on the human eye in order for characterizing detailed features of the obstacles such as shape and size especially in case with a small <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> drop, therefore, there was a limit to analyze the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J_c</i> mapping with such detailed information extending to thousands of images in the long tape of several 100 of meters. In this study, the image classification based on the deep learning method was introduced in our magnetic microscopy. The analytical model classifies the input image into the defect position and the normal position, respectively, together with a heat map and a score of confidence in the recognition. As a result, we have succeeded in detecting obstacles automatically from more than 4,000 of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J_c</i> images with a high accuracy of 98.5%. Furthermore, we revealed the existence of the obstacles which were not distinguishable by the local <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I_c</i> criterion. This method allows us to clarify the origin of the instability of long CC wire and will have a strong impact as an evaluation technique for dramatically improving the reliability of the CCs.