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, serve as the canonical targets of the research. 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.

翻译：缺陷通过影响材料的结构、力学和电子特性，决定了材料的多种性能。在众多具有独特缺陷的材料中，磁性材料展现出多种纳米至微米尺度的缺陷，并在材料科学领域得到了深入研究。具体而言，磁性迷宫图案中的缺陷——称为联合点与端点——是该研究领域的典型目标。尽管检测并表征这些缺陷对于理解磁性材料至关重要，但系统研究包含上千个紧密排列的联合点与端点的显微大尺度图像仍是一项严峻挑战。本研究提出一种名为TM-CNN（模板匹配-卷积神经网络）的新技术，旨在检测图像中的大量微小目标，例如磁性迷宫图案中的缺陷。利用TM-CNN在444张实验图像中识别出641,649个此类结构，并通过分析结果深化对磁性材料的理解。该技术采用两阶段检测方法：首先通过模板匹配进行初始检测，再结合卷积神经网络消除误检。训练CNN分类器通常需要标注大量训练图像，这一困难阻碍了CNN在许多实际场景中的应用。TM-CNN通过自动完成大部分标注工作，仅需人工复核少量修正，显著降低了创建训练图像的人力成本。在测试中，TM-CNN的F1分数达到0.991，远超传统模板匹配与基于CNN的目标检测算法。