公开(公告)号：US20250095127A1

公开(公告)日：2025-03-20

申请号：US18580902

申请日：2022-08-01

Applicant: KABUSHIKI KAISHA F.C.C.

Inventor： Motoki OKAZAKI

IPC: G06T7/00 ,  G06V10/774 ,  G06V20/70

Abstract: As training data that is used in generation of a prediction model, training data for a normal product which is configured by assigning a normal product ground truth label including only a normal label indicating a possibility of correspondence to a normal product to a learning image of the normal product, and training data for a defective product which is configured by assigning a defective product ground truth label including only a plurality of weighted defect type labels indicating a possibility of correspondence to a plurality of defect types to a learning image of the defective product are used. According to this, it is possible to perform defect inspection with the prediction model in which a possibility of erroneously predicting the defective product as the normal product is further reduced by setting a loss value in a case of prediction as the normal product from a learning image of the defective product to which the defective product ground truth label is assigned to be larger than a loss value in a case of prediction as the defective product in a defect type other than a ground truth from the same learning image in machine learning.