公开(公告)号：US20220335588A1

公开(公告)日：2022-10-20

申请号：US17686474

申请日：2022-03-04

Applicant: Keyence Corporation

Inventor： Di HE

IPC: G06T7/00 ,  G06V10/764 ,  G06V10/82 ,  G06V10/94 ,  G06V10/70 ,  G06N3/04 ,  G06N3/08

Abstract: An image inspection apparatus includes a learned neural network storage storing a neural network that previously learns weighting factors between input, intermediate and output layers, and an inferer determining failure/no-failure of a workpiece and classify the workpiece to classes based on an image of the workpiece. The inferer performs first and second inferences. In the first inference, the inferer determines failure/no-failure of the workpiece based on failure/no-failure feature quantities that are obtained by providing the workpiece image to the neural network and a failure/no-failure determination boundary. In the second inference, the inferer define a classification boundary to be used to classify an inspection workpiece to the classes in a feature quantity space of the neural network based on classification feature quantities that represent the different-type classification workpiece images, and classifies a workpiece to the classes based on classification feature quantities of an image of the workpiece and the classification boundary.

公开(公告)号：US20200250801A1

公开(公告)日：2020-08-06

申请号：US16729529

申请日：2019-12-30

Applicant: Keyence Corporation

Inventor： Di HE

IPC: G06T7/00

Abstract: To suppress erroneous input in inputting a non-defective product image and a defective product image, thereby increasing accuracy of distinguishing between a non-defective product image and a defective product image. An additional image that is added with an attribute as either one of a non-defective product and a defective product by a user is plotted in a feature space, and the probability that the attribute of the additional image is wrong is estimated. In the case in which the additional image is expected to have a wrong attribute, this result is notified. Result of selection whether to correct the attribute of the additional image by a user who receives the notification is received. A classifier generator 22 determines the attribute of the additional image on the basis of the result of selection and corrects a classification boundary in accordance with the determined attribute.