基于机器学习的光学显微镜图像颜色校正石墨烯自动检测
Machine Learning-based Automatic Graphene Detection with Color Correction for Optical Microscope Images
摘要：
石墨烯在各个领域中具有关键的应用和研究目的。然而，制造高质量和大量的石墨烯是耗时的，并且需要大量的人力资源劳动成本。在本文中，我们提出了一种基于机器学习的自动石墨烯检测方法与颜色校正（MLA-GDCC），一个可靠的和自主的石墨烯检测从显微图像。MLA-GDCC包括白平衡（WB）来校正图像上的颜色不平衡，修改的U-Net和支持向量机（SVM）来分割石墨烯薄片。考虑到不同摄像机拍摄的图像会产生颜色偏移，我们采用WB校正来校正彩色像素的不平衡。一种改进的U-Net模型，一种用于快速和精确图像分割的卷积神经网络（CNN）架构，被引入到从背景中分割石墨烯薄片。为了提高像素级的精度，我们在改进的U-Net模型之后实现了SVM来分离单层和双层石墨烯片。MLA-GDCC实现了单层石墨烯和双层石墨烯的薄片级检测率分别为87.09%和90.41%，单层石墨烯和双层石墨烯的像素级准确度分别为99.27%和98.92%。MLA-GDCC不仅实现了石墨烯薄片的高检测率，还将石墨烯检测过程的延迟从数小时缩短到数秒。
英文摘要：
Graphene serves critical application and research purposes in various fields. However, fabricating high-quality and large quantities of graphene is time-consuming and it requires heavy human resource labor costs. In this paper, we propose a Machine Learning-based Automatic Graphene Detection Method with Color Correction (MLA-GDCC), a reliable and autonomous graphene detection from microscopic images. The MLA-GDCC includes a white balance (WB) to correct the color imbalance on the images, a modified U-Net and a support vector machine (SVM) to segment the graphene flakes. Considering the color shifts of the images caused by different cameras, we apply WB correction to correct the imbalance of the color pixels. A modified U-Net model, a convolutional neural network (CNN) architecture for fast and precise image segmentation, is introduced to segment the graphene flakes from the background. In order to improve the pixel-level accuracy, we implement a SVM after the modified U-Net model to separate the monolayer and bilayer graphene flakes. The MLA-GDCC achieves flake-level detection rates of 87.09% for monolayer and 90.41% for bilayer graphene, and the pixel-level accuracy of 99.27% for monolayer and 98.92% for bilayer graphene. MLA-GDCC not only achieves high detection rates of the graphene flakes but also speeds up the latency for the graphene detection process from hours to seconds.
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