  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.
