公开(公告)号：US20220391698A1

公开(公告)日：2022-12-08

申请号：US17748710

申请日：2022-05-19

Applicant: Hitachi, Ltd.

Inventor： Takashi OSHIMA ,  Goichi ONO ,  Akira KITAYAMA ,  Ming LIU

IPC: G06N3/08 ,  G06N3/04 ,  G06V10/77 ,  G06V10/776 ,  G06V10/82

Abstract: Provided is a training recognition device that implements training of a DNN for article recognition that does not require manual annotation for an image for training and can reduce power consumption, time, and hardware amount required for training. The training recognition device includes: an image conversion unit that inputs a simulation image and an actual site image into a generative adversarial network and converts the simulation image into an artificial site image; a pre-trained feature extraction unit that inputs the simulation image to a trained deep neural network trained using the simulation image and annotation data for the simulation image and outputs a feature point of the simulation image at time of re-training; a re-training feature extraction unit that inputs the artificial site image to a deep neural network for re-training, re-trains a difference between the simulation image and the artificial site image, and outputs a feature point of the artificial site image; an error calculation unit for feature extraction unit that calculates a difference between the feature point output by the re-training feature extraction unit and the feature point output by the pre-trained feature extraction unit; a coefficient update unit for feature extraction unit that updates a coefficient of the re-training feature extraction unit used for re-training based on the difference; and a re-training identification unit that re-trains a method for identifying an article based on a feature point output from the deep neural network for re-training of the coefficient updated by the coefficient update unit for feature extraction unit.