基于语义分割的扫描电镜图像中纳米复合材料团聚体识别

IF 3.8 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Nanodielectrics Pub Date : 2022-03-04 DOI:10.1049/nde2.12034
Yu Bai, Yan Wang, Dayuan Qiang, Xin Yuan, Jiehui Wu, Weilong Chen, Sai Zhang, Yanru Zhang, George Chen
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引用次数: 0

摘要

这种结块现象给纳米电介质带来了巨大的挑战。扫描电镜(SEM)图像中团聚体的识别是解决这一问题的重要步骤。在计算机视觉图像识别技术快速发展的背景下,本文提出了一种基于语义分割的纳米电介质扫描电镜图像团聚体识别新方法,该方法比传统方法更高效、更准确。本文研究了基于卷积神经网络的三种模型,即像素块分类网络、数据增强的全卷积分割网络和无监督自编码网络。这三种网络都可以初步识别球形硅基共混聚乙烯纳米复合材料的团聚体。像素块分类网络的平均交联数(mIoU)为0.843,处理一幅图像所需时间为25 s。全卷积分割网络处理一个样本只需要0.059 s, mIoU为0.777。无监督自编码网络以5.806 s的速度达到0.747的mIoU。根据数据集的数量,以及对不同速度和精度的要求,可以灵活选择三种网络。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Identification of nanocomposites agglomerates in scanning electron microscopy images based on semantic segmentation

The agglomeration causes a significant challenge to nanodielectrics. Identification of agglomerates in scanning electron microscopy (SEM) images is an important step of solving this issue. Motivated by the fast development of image recognition in computer vision, we propose a new approach for agglomerates identification in SEM images of nanodielectrics by semantic segmentation, which is more efficient and accurate than traditional methods. Three models based on convolutional neural networks are investigated in this work, namely pixel blocks classification network, full convolutional segmentation network employed with data augmentation and unsupervised self-encoding network. All three networks can preliminarily identify agglomerates of spherical silica-based blend polyethylene nanocomposites. The mean intersection over union (mIoU) of pixel blocks classification network is 0.843 and it takes 25 s to process an image. Full convolutional segmentation network only needs 0.059 s to process a sample, with a mIoU of 0.777. Unsupervised self-encoding network can reach a mIoU of 0.747 at a speed of 5.806 s. According to the amount of data sets, and requirements for different speed and accuracy, three kinds of networks can be flexibly selected.

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来源期刊
IET Nanodielectrics
IET Nanodielectrics Materials Science-Materials Chemistry
CiteScore
5.60
自引率
3.70%
发文量
7
审稿时长
21 weeks
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