Automatic center identification of electron diffraction with multi-scale transformer networks

Selected area electron diffraction (SAED) is a widely used technique for characterizing the structure and measuring lattice parameters of materials. An autonomous analytic method has become an urgent demand for the large-scale SAED data produced from in -situ experiments. In this work, we realize the automatic processing for center identification with a proposed deep segmentation model named the multi -scale Transformer (MSTrans) network. This algorithm enables robust segmentation of the central spots by combining a novel gated axial -attention module and multi -scale feature fusion. The proposed MS -Trans model shows high precision and robustness, enabling autonomous processing of SAED patterns without any prior knowledge. The application on in -situ SAED data of the oxidation process of FeNi alloy demonstrates its capability of implementing autonomous quantitative processing. (c) 2017 Elsevier Inc. All rights reserved.