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.

翻译：在材料科学中，表征周期性结构中的缺陷对于理解材料特性至关重要。为表征磁迷宫图案，需要精确识别连接点和端点，而每幅图像通常包含上千个紧密排列的缺陷。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，用于检测图像中大量微小物体（如磁迷宫图案中的缺陷）。该技术被应用于444张实验图像的结构识别，通过结果分析深化了对磁性材料的理解。该方法采用两阶段检测策略：首先利用模板匹配进行初始检测，再通过卷积神经网络消除误识别。训练CNN分类器需要生成大量训练图像，这一难点限制了CNN在众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，显著降低了训练图像的创建工作量。测试中，TM-CNN取得了0.988的优异F1分数，远超传统模板匹配和基于CNN的目标检测算法。