公开(公告)号：US20220358334A1

公开(公告)日：2022-11-10

申请号：US17342592

申请日：2021-06-09

Applicant: QINGDAO TECHNOLOGICAL UNIVERSITY

Inventor： CHENG JUN CHEN ,  CHANG ZHI LI ,  DONG NIAN LI ,  JUN HONG

IPC: G06K9/62 ,  G06K9/00

Abstract: An assembly change detection method based on attention mechanism, including: establishing a three-dimensional model of an assembly body, adding a tag to each part in the three-dimensional model, setting several assembly nodes, obtaining depth images of the three-dimensional model under each assembly node in different viewing angles, and obtaining a change tag image of a added part at each assembly node; selecting two depth images at front and back moments in different viewing angles as training samples; performing semantic fusion, feature extraction, attention mechanism processing and metric learning sequentially on the training samples, training a detection model, continuously selecting training samples to train the detection model, saving model parameters with optimal similarity during training, completing training; and obtaining depth images of successive assembly nodes during assembling the assembly body, inputting depth images into trained detection model, and outputting change image of added part of the assembly body during assembly.