Deep-learning-based nanowire detection in AFM images for automated nanomanipulation
Deep-learning-based nanowire detection in AFM images for automated nanomanipulation
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DOI：
10.1063/10.0003218
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发表时间：
2021-03-01
期刊：
影响因子：
--
通讯作者：
Wu, Sen
中科院分区：
文献类型：
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作者：
Bai, Huitian;Wu, Sen
基于原子力显微镜(AFM)的纳米操纵已被证明是将各种纳米颗粒组装成复杂图案和器件的一种可能方法。为了实现高效和全自动化的纳米操纵，基片上的纳米颗粒必须被精确和自动地识别。本文主要研究了一种基于深度学习的柔性纳米线自动检测方法。应用基于只看一次版本3(YOLOv3)和全卷积网络(FCN)的实例分割网络对AFM图像中的所有可移动纳米线进行分割。结合后续的图像形态和拟合算法，这使得能够在高抽象水平上检测纳米线的姿势和位置。得益于这些算法，我们的程序能够以纳米级的分辨率自动检测不同形貌的纳米线，并在测试数据集中具有90%以上的可靠性。与已有方法相比，检测结果受图像复杂度的影响较小，证明了该算法具有良好的鲁棒性。(C)2021年作者(S)。
Atomic force microscope (AFM)-based nanomanipulation has been proved to be a possible method for assembling various nanoparticles into complex patterns and devices. To achieve efficient and fully automated nanomanipulation, nanoparticles on the substrate must be identified precisely and automatically. This work focuses on an autodetection method for flexible nanowires using a deep learning technique. An instance segmentation network based on You Only Look Once version 3 (YOLOv3) and a fully convolutional network (FCN) is applied to segment all movable nanowires in AFM images. Combined with follow-up image morphology and fitting algorithms, this enables detection of postures and positions of nanowires at a high abstraction level. Benefitting from these algorithms, our program is able to automatically detect nanowires of different morphologies with nanometer resolution and has over 90% reliability in the testing dataset. The detection results are less affected by image complexity than the results of existing methods and demonstrate the good robustness of this algorithm. (C) 2021 Author(s).