Deep Learning-Based Layer Classification and Identification of Graphene 2D N Anosheets

With the development of technology, the application of two-dimensional nanosheets has become increasingly widespread, particularly in the fields of materials science, nanotechnology, and electronic engineering, where the precise identification of graphene layers is of paramount importance. Nowadays, there are many ways to explore 2D material nanosheets in the field of materials science, but there is still the problem that the average target detection accuracy is not high at different categories and thresholds. Therefore, to solve this problem, this paper proposes a deep learning-based object detection method for different classes of graphene two-dimensional nanosheets. The accurate value of the method is 99.12%, and some performance indicators of the algorithm are also improved compared with the classical algorithm. According to the results, the algorithm has better advantages for graphene nanopayer classification.