"In this work, we presented a new algorithm named TMCNN to detect defects in magnetic labyrinthine patterns, contributing to a pioneering analysis in material science."
"TM-CNN employs a two-stage detection procedure, combining template matching for initial detection and a convolutional network classifier for refining misdetections, ensuring high detection accuracy."
"In our experiments, TM-CNN exhibited performance superior to other techniques, achieving an impressive F1 score of 0.988, which reflects its reliability in defect detection."
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