Characterization of Magnetic Labyrinthine Structures Through Junctions and Terminals Detection Using Template Matching and CNN

• Published in: IEEE access Vol. 12; pp. 92419 - 92430
• Main Authors: Okubo, Vinicius Yu, Shimizu, Kotaro, Shivaram, B. S., Yong Kim, Hae
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Annotations; Artificial neural networks; Computer vision; Convolutional neural networks; Deep learning; Defects; Feature extraction; Junctions; Magnetic domains; magnetic labyrinthine patterns; Magnetic materials; Magnetic properties; Magnetic resonance imaging; Magnetic tunneling; Magnets; Material properties; material science; Materials science; Materials science and technology; Mechanical properties; Neural networks; object detection; Object recognition; Saturation magnetization; Structural analysis; Template matching
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3422259

Defects influence diverse properties of materials, shaping their structural, mechanical, and electronic characteristics. Among a variety of materials exhibiting unique defects, magnets exhibit diverse nano- to micro-scale defects and have been intensively studied in materials science. Specifically, defects in magnetic labyrinthine patterns, called junctions and terminals are ubiquitous and serve as points of interest. While detecting and characterizing such defects is crucial for understanding magnets, systematically investigating large-scale images containing over a thousand closely packed junctions and terminals remains a formidable challenge. This study introduces a new technique called TM-CNN (Template Matching - Convolutional Neural Network) designed to detect a multitude of small objects in images, such as the defects in magnetic labyrinthine patterns. TM-CNN was used to identify 641,649 such structures in 444 experimental images, and the results were explored to deepen understanding of magnetic materials. It employs a two-stage detection approach combining template matching, used in initial detection, with a convolutional neural network, used to eliminate incorrect identifications. To train a CNN classifier, it is necessary to annotate a large number of training images. This difficulty prevents the use of CNN in many practical applications. TM-CNN significantly reduces the manual workload for creating training images by automatically making most of the annotations and leaving only a small number of corrections to human reviewers. In testing, TM-CNN achieved an impressive F1 score of 0.991, far outperforming traditional template matching and CNN-based object detection algorithms.