Deep-learning algorithms enable precise image recognition based on high-dimensional hierarchical image features. Here, we report the development and implementation of a deep-learning-based image segmentation algorithm in an autonomous robotic system to search for two-dimensional (2D) materials. We trained the neural network based on Mask-RCNN on annotated optical microscope images of 2D materials (graphene, hBN, MoS₂, and WTe₂). The inference algorithm is run on a 1024 × 1024 px² optical microscope images for 200 ms, enabling the real-time detection of 2D materials. The detection process is robust against changes in the microscopy conditions, such as illumination and color balance, which obviates the parameter-tuning process required for conventional rule-based detection algorithms. Integrating the algorithm with a motorized optical microscope enables the automated searching and cataloging of 2D materials. This development will allow researchers to utilize a large number of 2D materials simply by exfoliating and running the automated searching process. To facilitate research, we make the training codes, dataset, and model weights publicly available.

深度学习算法可基于高维分层图像特征实现精确的图像识别。在这里，我们报告了在自主机器人系统中搜索二维（2D）材料的基于深度学习的图像分割算法的开发和实现。我们在带注释的2D材料（石墨烯，hBN，MoS ₂和WTe ₂）的光学显微镜图像上训练了基于Mask-RCNN的神经网络。推理算法在1024×1024 px ^2上运行光学显微镜图像持续200毫秒，可实时检测2D材料。该检测过程可抵抗显微镜条件的变化，例如照明和色彩平衡，从而避免了常规基于规则的检测算法所需的参数调整过程。将算法与电动光学显微镜集成在一起即可实现2D材料的自动搜索和分类。这一发展将使研究人员只需剥去并运行自动搜索过程即可利用大量2D材料。为了便于研究，我们将培训代码，数据集和模型权重公开提供。