Abstract: Self-checkout vehicle systems and methods comprising a self-checkout vehicle having a camera(s), a weight sensor(s), and a processor configured to: (i) identify via computer vision a merchandise item selected by a shopper based on an identifier affixed to the selected item, and (ii) calculate a price of the merchandise item based on the identification and weight of the selected item. Computer vision systems and methods for identifying merchandise selected by a shopper comprising a processor configured to: (i) identify an identifier affixed to the selected merchandise and an item category of the selected merchandise, and (ii) compare the identifier and item category identified in each respective image to determine the most likely identification of the merchandise.

Abstract: An automated checkout system accesses an image of an item inside a shopping cart and receives an identifier determined for the item inside the cart. The automated checkout system determines a load measurement for the item inside the cart using load sensors coupled to the cart. The automated checkout system encodes a feature vector of the item based at least on the determined weight, the accessed image, and the determined identifier. The automated checkout system inputs the feature vector to a machine-learning model to determine a confidence score describing a likelihood that the identifier determined for the item matches the item placed inside the cart. If the confidence score is less than a threshold confidence score, the automated checkout system generates a notification alerting an operator of an anomaly in the identifier.

Abstract: Self-checkout vehicle systems and methods comprising a self-checkout vehicle having a camera(s), a weight sensor(s), and a processor configured to: (i) identify via computer vision a merchandise item selected by a shopper based on an identifier affixed to the selected item, and (ii) calculate a price of the merchandise item based on the identification and weight of the selected item. Computer vision systems and methods for identifying merchandise selected by a shopper comprising a processor configured to: (i) identify an identifier affixed to the selected merchandise and an item category of the selected merchandise, and (ii) compare the identifier and item category identified in each respective image to determine the most likely identification of the merchandise.

Abstract: Disclosed are technologies for generating training data for identification neural networks. Series of images are captured of a plurality of merchandise items from different angles and with different background assortments of other merchandise items. A labeled training dataset is generated for the plurality of merchandise items. The series of captured images is normalized, where the merchandise occupies a threshold percentage of pixels in the normalized image. The training dataset is extended by applying augmentation operations to the normalized images to generate a plurality of augmented images. Each image is stored in the training dataset as a unique training data point for the given merchandise item it depicts. Labels are generated mapping each training data point to attributes associated with the depicted merchandise item. Input neural networks are trained on the labeled training dataset to perform real-time identification of selected merchandise items placed into a self-checkout apparatus by a user.