Method for improper product barcode detection
AssigneeZebra Technologies Corporation
InventorsRobert James Pang, Sajan Wilfred, Christopher J. Fjellstad, Przemyslaw Kowalewski
Life of this patent
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Abstract
Techniques are provided for detecting an improper barcode using a neural network trained to identify an object from physical features appearing in images of object, and without resorting to using a barcode or other indicia to identify the object. The neural network is self-training, updating itself with selected images obtained at a Point-of-Sale. That is, the neural network is capable of training itself while performing improper barcode detection operations, such as spoofing detection.
Claims17
A computer-implemented method for improper objection detection, the method comprising:
- receiving, at one or more processors, an image of an object and initial object identification data, the image captured by an image scanner;
- determining if the initial object identification data corresponds to a lower priority object, using an object priority heuristic;
- responsive to the initial object identification data corresponding to the lower priority object, excluding the received image from being provided to a trained neural network;
- and
- responsive to the initial object identification data not corresponding to the lower priority object, providing the image to the trained neural network for analysis,
- wherein the analysis includes:
- analyzing, in a trained neural network, physical features of the object and determining an object identification data associated with the object based on the physical features and determining a confidence level indication corresponding to the certainty of the object identification data determined by the trained neural network;
- and
- when the confidence level indication is in a first range of values, communicating an authorization signal having the object identification data, and when the confidence level indication is in a second range of values, communicating a failed authorization signal.
A computer-implemented method for improper objection detection, the method comprising:
- receiving, at one or more processors, an image of an object, the image captured by an image scanner;
- analyzing, in a trained neural network, physical features of the object and determining an object identification data associated with the object based on the physical features and determining a confidence level indication corresponding to the certainty of the object identification data determined by the trained neural network;
- when the confidence level indication is in a first range of values, communicating an authorization signal having the object identification data, and when the confidence level indication is in a second range of values, communicating a failed authorization signal;
- analyzing, at the one or more processors, the identified physical features of the object from the image and determining a diversity assessment of the image;
- when the diversity assessment is below a desired image diversity, generating, at the one or more processors, one or more renditions of the image of the object, where each rendition is generated by applying a different image-based transformation to the image;
- and
- identifying, at the one or more processors, physical features of the object in each rendition of the image and providing the one or more renditions with the identified physical features to the trained neural network.
Dependencies are inferred from the claim text.