METHOD AND SYSTEM TO AUGMENT IMAGES AND LABELS TO BE COMPATIBLE WITH VARIOUS MACHINE LEARNING MODELS

    公开(公告)号:US20240303975A1

    公开(公告)日:2024-09-12

    申请号:US18179613

    申请日:2023-03-07

    CPC classification number: G06V10/7753 G06V10/46

    Abstract: A method includes obtaining an authentic image of an assembly and a boundary label provided with the authentic image. The boundary label is associated with a selected region of the authentic image depicting a selected object. The method includes generating an augmented image based on the authentic image and an augmentation model employing one or more augmentation parameters, defining a model boundary label on a blank image at a region that correlates with the selected region of the authentic image, generating an augmented blank image based on the one or more augmentation parameters employed for the augmented image, identifying, as an augmented boundary label associated with augmented image, the model boundary label in the augmented blank image, and outputting an augmented image data, wherein the augmented image data incudes data indicative of the augmented image and of the augmented boundary label.

    STAMPING LINE DEFECT QUALITY MONITORING SYSTEMS AND METHODS OF MONITORING STAMPING LINE DEFECTS

    公开(公告)号:US20220126345A1

    公开(公告)日:2022-04-28

    申请号:US17078768

    申请日:2020-10-23

    Abstract: A method of inspecting stamped blanks on a stamping line includes identifying at least one target defect location for a given stamped blank configuration where a unique defect type is associated with each of the at least one target defect locations. One or more images of each of the least one identified target defect locations on blanks stamped per the given stamped blank configuration are acquired with one or more cameras assigned to each of the identified target defect locations. The method includes analyzing the one or more images of each of the least one identified target defect locations and detecting if the unique defect type associated with each of the at least one target defect locations is present. Also, each unique defect type is identified with a corresponding unique defect identification algorithm.

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