小而多样的SEM图像数据集:图像增强对AlexNet性能的影响

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

TEM Journal-Technology Education Management Informatics Pub Date : 2023-05-29 DOI:10.18421/tem122-34

Khairul Khaizi Mohd Shariff, Megat Syahirul Amin Megat Ali, Ahmad Ihsan Mohd Yassin, Noor Ezan Abdullah, Ali Abd Al-Misreb, Aisyah Hartini Jahidin

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引用次数: 0

摘要

到目前为止，扫描电子显微镜已经产生了纳米级分辨率的最复杂和最多样化的图像之一。从样品表面反射的高度放大后的背散射电子图像是不均匀的，即使对于同一类图像也是如此。该研究调查了拥有一个小而多样的数据集对AlexNet性能的影响。本研究共使用了来自EUDAT协作数据库基础设施的160个样本。与使用新的非增强样本来增加数据集的大小相比，图像增强显著提高了AlexNet的分类性能和泛化能力。

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Small but Diverse SEM Image Dataset: Impact of Image Augmentation on the Performance of AlexNet

To this date, scanning electron microscope has produced among the most complex and diverse images at nanoscale resolution. The highly magnified images of backscattered electrons reflected from the surface of samples are non-uniformed, even for the same class of images. The study investigates the impact of having a small but diverse dataset on the performance of AlexNet. A total of 160 samples from EUDAT Collaborative Database Infrastructure is used for the study. Compared to the use of new non-augmented samples to increase the size of dataset, image augmentation has been significantly improved classification performance and generalization ability of the AlexNet.

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来源期刊

TEM Journal-Technology Education Management Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

2.20

自引率

14.30%

发文量

176

审稿时长

8 weeks

期刊介绍： TEM JOURNAL - Technology, Education, Management, Informatics Is a an Open Access, Double-blind peer reviewed journal that publishes articles of interdisciplinary sciences: • Technology, • Computer and informatics sciences, • Education, • Management