US 11,881,015 B2
High-precision identification method and system for substations
Yadong Liu, Shanghai (CN); Yingjie Yan, Shanghai (CN); Siheng Xiong, Shanghai (CN); Ling Pei, Shanghai (CN); Zhe Li, Shanghai (CN); Peng Xu, Shanghai (CN); Lei Su, Shanghai (CN); Xiaofei Fu, Shanghai (CN); and Xiuchen Jiang, Shanghai (CN)
Assigned to Shanghai Jiaotong University, Shanghai (CN)
Appl. No. 17/433,994
Filed by Shanghai Jiaotong University, Shanghai (CN)
PCT Filed Jun. 8, 2020, PCT No. PCT/CN2020/094891
§ 371(c)(1), (2) Date Aug. 25, 2021,
PCT Pub. No. WO2021/248269, PCT Pub. Date Dec. 16, 2021.
Prior Publication US 2022/0343642 A1, Oct. 27, 2022
Int. Cl. G06V 10/94 (2022.01); G06V 10/82 (2022.01); G06V 10/776 (2022.01); G06V 10/764 (2022.01); G06V 10/22 (2022.01); G06V 10/40 (2022.01); G06T 7/73 (2017.01); G06T 7/60 (2017.01); G06V 10/32 (2022.01); G06N 3/044 (2023.01)
CPC G06V 10/95 (2022.01) [G06N 3/044 (2023.01); G06T 7/60 (2013.01); G06T 7/73 (2017.01); G06V 10/22 (2022.01); G06V 10/32 (2022.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06T 2207/20084 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A high-precision identification method for substations, comprising:
building a Mask RCNN objection recognition network model based on convolutional neural networks;
inputting acquired image information of an object into the Mask RCNN object recognition network model for preliminary recognition and outputting a recognition result of the object;
using an information entropy to create a semantic decision tree and correcting the recognition result of the object according to the principle of relative correlation between different objects and outputting a final recognition decision result;
reading the recognition decision result to obtain a true type of the object to be recognized.