Characterization of Magnetic Labyrinthine Structures through Junctions
  and Terminals Detection using Template Matching and CNN
Vinícius Yu Okubo Kotaro Shimizu B. S. Shivaram Hae Yong Kim
In material sciences, characterizing faults in periodic structures is vital
for understanding material properties. To characterize magnetic labyrinthine
patterns, it is necessary to accurately identify junctions and terminals, often
featuring over a thousand closely packed defects per image. 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 defects in magnetic labyrinthine patterns. TM-CNN was used to identify these
structures in 444 experimental images, and the results were explored to deepen
the 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 create 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 0.988, far outperforming traditional template matching and CNN-based
object detection algorithms.