US 11,908,118 B2
Visual model for image analysis of material characterization and analysis method thereof
Zuo Xu, Qinhuangdao (CN); Yuancheng Cao, Wuhan (CN); Wuxin Sha, Wuhan (CN); Zhihua Zhu, Qinhuangdao (CN); Hanqi Wu, Qinhuangdao (CN); and Fanpeng Cheng, Wuhan (CN)
Assigned to CITIC Dicastal Co., Ltd., Qinhuangdao (CN)
Filed by CITIC Dicastal Co., Ltd., Qinhuangdao (CN)
Filed on Jul. 28, 2021, as Appl. No. 17/387,955.
Claims priority of application No. 202110305450.1 (CN), filed on Mar. 19, 2021.
Prior Publication US 2022/0301139 A1, Sep. 22, 2022
Int. Cl. G06T 7/00 (2017.01); G06T 7/62 (2017.01); G06V 20/69 (2022.01); G06V 10/30 (2022.01); G06N 3/08 (2023.01); G06T 7/20 (2017.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01)
CPC G06T 7/0002 (2013.01) [G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 3/08 (2013.01); G06T 7/20 (2013.01); G06T 7/62 (2017.01); G06V 10/30 (2022.01); G06V 20/695 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30241 (2013.01)] 12 Claims
OG exemplary drawing
 
1. A method for analyzing images of material characterization, wherein it comprises the following steps:
collect and mark samples of material characterization images to establish an image data set of material characterization, wherein the said image data set of material characterization comprises a first data set composed of first material characterization images marked with atomic species and crystal structure parameters, and a second data set composed of second material characterization images marked with edges and center points of microscopic particles and a third data set composed of third material characterization images marked with microstructure features;
establish an initial neural network model, and use the image data set of material characterization to train it to obtain a neural network model based on deep learning; and establish a dynamic statistical model;
input the characterization image of the material to be analyzed into the deep learning-based neural network model, and identify and analyze the output results, complete atom identification and interplanar spacing annotation, microscopic particle morphology statistics; and
input the third data set or output data of the deep learning-based neural network model into the dynamic statistical model to complete tracking of microstructure motion trajectory.