论文标题
基于树的机器学习算法的石墨烯检测中的像素分类
Pixel-wise classification in graphene-detection with tree-based machine learning algorithms
论文作者
论文摘要
石墨烯对石墨烯的机械去角质及其通过光学检查的识别是凝结物理学的里程碑之一，它引发了2D材料领域。从整个样本空间中找到感兴趣的区域和层数的识别是一种常规任务，这可能是自动化的。我们提出了有监督的像素分类方法，即使使用少数培训图像数据集，这些方法也需要短的计算时间，而没有GPU。我们介绍了四种基于树的机器学习算法 - 决策树，随机森林，极端梯度提升和轻梯度提升机。我们用石墨烯的五个光学显微镜图像训练它们，并使用多个指标和指数评估它们的性能。我们还讨论了三个单个分类器之间的组合机器学习模型，并评估其在识别和可靠性方面的性能。本文开发的代码向公众开放，并将在github.com/gjung-group/graphene_segnementation上发布。
Mechanical exfoliation of graphene and its identification by optical inspection is one of the milestones in condensed matter physics that sparked the field of 2D materials. Finding regions of interest from the entire sample space and identification of layer number is a routine task potentially amenable to automatization. We propose supervised pixel-wise classification methods showing a high performance even with a small number of training image datasets that require short computational time without GPU. We introduce four different tree-based machine learning algorithms -- decision tree, random forest, extreme gradient boost, and light gradient boosting machine. We train them with five optical microscopy images of graphene, and evaluate their performances with multiple metrics and indices. We also discuss combinatorial machine learning models between the three single classifiers and assess their performances in identification and reliability. The code developed in this paper is open to the public and will be released at github.com/gjung-group/Graphene_segmentation.
