METHOD, APPARATUS, AND SYSTEM FOR PROVIDING QUALITY ASSURANCE FOR TRAINING A FEATURE PREDICTION MODEL

    公开(公告)号:US20190102656A1

    公开(公告)日:2019-04-04

    申请号:US15720974

    申请日:2017-09-29

    Abstract: An approach is provided for providing quality assurance for training a feature prediction model. The approach involves training the feature prediction model to label one or more features by using a training data set comprising a plurality of data items with manually marked feature labels. The approach also involves processing the training data set using the trained feature prediction model to generate automatically marked feature labels for the plurality of data items. The approach further involves computing precision data indicating a respective precision between the manually marked feature labels and the automatically marked feature labels for each of the plurality of data items in the training data set. The approach further involves initiating a quality assurance procedure on said each of the plurality of data items based on a determination that the precision data does not satisfy a quality assurance criterion.

    METHOD, APPARATUS, AND SYSTEM FOR DETECTING DEGRADED GROUND PAINT IN AN IMAGE

    公开(公告)号:US20200285862A1

    公开(公告)日:2020-09-10

    申请号:US16293328

    申请日:2019-03-05

    Abstract: An approach is provided for detecting degraded ground paint in an image. The approach, for example, involves performing semantic segmentation on the image to determine one or more pixels of the image that are classified in a ground paint category. The approach also involves generating a binary image that contains the one or more pixels of the image that are classified in the ground paint category. The approach further involves generating a hole-filled binary image by filling in the binary image to generate one or more curvilinear structures from the one or more pixels. The approach further involves determining a difference between the image and the hole-filled binary image to identify one or more degraded ground paint pixels of the image and providing the one or more degraded ground paint pixels as an output.

    METHOD, APPARATUS, AND SYSTEM FOR PROVIDING DATA-DRIVEN SELECTION OF MACHINE LEARNING TRAINING OBSERVATIONS

    公开(公告)号:US20200167689A1

    公开(公告)日:2020-05-28

    申请号:US16203087

    申请日:2018-11-28

    Abstract: An approach is provided for selecting machine learning training observations. The approach, for example, involves providing data for presenting a user interface displaying a plurality of training images and specifying a feature to label in the plurality of training images. The feature is selected based on an image selection criterion. The approach also involves receiving a set of feature labels for the plurality of training images via the user interface based on crowd-sourced input data. The approach further involves training a machine learning based image selector to select a plurality of images, a plurality of patches of a larger image, or a combination thereof for labeling based on the set of feature labels and the plurality of training images.

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