▎ 摘　　要

Graphene has attracted a lot of interest since its discovery. However, graphene layers made by me-chanical exfoliation need to be carefully distinguished from multi-layer graphite and residues by expe-rienced experts, which is time consuming and requires significant experience. In this paper, an image segmentation method based on deep learning is developed to identify single-layer graphene (SLG) under an optical microscope. By introducing a modified UNet++ with an attention gate and a residue network (ResNet) for further classification as a two-level structure, we can distinguish SLG from graphite with high accuracy by using only a small amount of training images. The high accuracy of SLG identification and the short inference time make it a promising real-time detection tool besides traditional and technically more involved identification methods such as Raman spectroscopy and atomic force microscopy. (c) 2022 Elsevier Ltd. All rights reserved.