基于YOLOv3框架的高分辨电镜图像原子峰位置检测  

Detection of intensity peaks in high-resolution transmission electron microscopy image based on YOLOv3

作　　者：南虎 麻晓晶 赵海博 汤少杰 刘卫华[1] 王大威[1] 贾春林 Nan Hu;Ma Xiao-Jing;Zhao Hai-Bo;Tang Shao-Jie;Liu Wei-Hua;Wang Da-Wei;Jia Chun-Lin(School of Microelectronics,Faculty of Electronic and Information Engineering,Xi’an Jiaotong University,Xi’an 710049,China;School of Automation,Xi’an University of Posts and Telecommunications,Xi’an 710121,China;Ernst Ruska-Centrum für Mikroskopie und Spektroskopie mit Elektronen(ER-C),Forschungszentrum Jülich GmbH,52425 Jülich,Germany)

机构地区：[1]西安交通大学电子与信息学部微电子学院,西安710049 [2]西安邮电大学自动化学院,西安710121 [3]Ernst Ruska-Centrum für Mikroskopie und Spektroskopie mit Elektronen(ER-C),Forschungszentrum Jülich GmbH,52425 Jülich,Germany

出　　处：《物理学报》2021年第7期285-292,共8页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:11974268,61671368);陕西省重点研发计划(批准号:2020SF-377)资助的课题.

摘　　要：高分辨电镜图像中原子峰位置的检测具有十分重要的现实意义,通过精确定量化原子峰位置可以分析物质在微观尺度上的结构形变、电极化矢量分布等重要信息.近年来深度学习技术在图像目标检测领域取得了巨大突破,这一技术可用在高分辨电镜图像处理上,因为原子位置的检测可以看作是一个目标检测问题.本文利用先进的机器学习方法,通过制作高质量原子图像样本集,使用YOLOv3目标识别框架对原子图像进行自动检测,达到预期效果,实现了深度学习技术在高分辨电镜图像处理领域的应用.该方法的运用有望突破自动处理动态、大量电镜图片的瓶颈问题.The detection of intensity peaks,which correspond to atom positions,in high-resolution(scanning)transmission electron microscopy images is of great practical significance.By quantitatively determining the locations of these peaks,it is possible to obtain important information such as the structural deformation and the electric dipole distribution inside a material on the nanoscale.The detection of the peak positions in image processing can be regarded as a target detection problem,for which breakthroughs have been made with deeplearning neural networks.Comparing to the traditional target detection algorithms,which are based on specifically designed feature extractor and classifier,the deep-learning approach can obtain the features at multiple levels of abstraction automatically,thus improving the robustness of the detection process.In this paper,we realize the automatic detection of the intensity peaks in high-resolution electron microscopy images by building a high-quality atomic image sample set and using the YOLOv3 target detection framework.With its accuracy and speed,which are superior over other target detection neural networks,the YOLOv3 is suitable for image processing as the number of images increases explosively.The YOLOv3 network converges well in the training process using our atomic image sample set,with the loss function reaching a minimum after 500 epoches;the trained neural network can detect almost all the major atoms in the images,showing its excellent ability.With the aid of YOLOv3,we also develop a program to organize the detected atoms,enabling the detection of all the other atoms within each unit cell.It is found that,combining YOLOv3 with the newly developed algorithm,almost all the atoms can be successfully determined,showing its advantages over previous algorithms based on bravis lattice construction,especially for high-resolution transmission electron microscopy images with lattice defects.Our results show that this image processing technique has the potential in overcoming the bottlenec

关 键 词：高分辨电镜图像处理 深度学习 YOLOv3 网络 原子峰位置检测 

分 类 号：TN16[电子电信—物理电子学] TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]