SYSTEMS AND METHODS FOR INVENTORY MANAGEMENT
A systems including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, perform: receiving a plurality of images from one or more devices, the images corresponding to a store shelf of a store; combining the plurality of images to generate a shelf image corresponding to the store shelf; encoding the shelf image into a first processing format; processing the shelf image in the first processing format with a neural network using pre-trained weights; determining positions in the shelf image that correspond to an out-of-stock detection based on outputs from the neural network; and generating a report for the out-of-stock detection, the report including an indication of coordinates of the out-of-stock detection and an item of the store that corresponds to the coordinates. Other embodiments are described.
1 . A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, perform:
receiving a plurality of images from one or more devices, the images corresponding to a store shelf of a store;
combining the plurality of images to generate a shelf image corresponding to the store shelf;
encoding the shelf image into a first processing format;
processing the shelf image in the first processing format with a neural network using pre-trained weights;
determining positions in the shelf image that correspond to an out-of-stock detection based on outputs from the neural network; and
generating a report for the out-of-stock detection, the report including an indication of coordinates of the out-of-stock detection and an item of the store that corresponds to the coordinates.
2 . The system of claim 1 , wherein the one or more devices comprise at least one of: a shelf-scanning robot, a drone, or a camera.
3 . The system of claim 1 , wherein combining the plurality of images further comprises combining the plurality of images based on a planogram indicating where items of the store are to be located, the planogram comprising horizontal-facing quantities and vertical-facing quantities.
4 . The system of claim 1 , wherein each of the plurality of images comprise metadata corresponding to a sequential order in which each of the plurality of images was captured.
5 . The system of claim 1 , wherein encoding the shelf image into the first processing format further comprises:
converting the shelf image to an encoded string format; and
processing the encoded string format via an application programming interface (API) based on horizontal facing quantities and vertical facing quantities of a planogram.
6 . The system of claim 1 , wherein the neural network comprises at least one of: (i) a region-based convolutional neural network (R-CNN), (ii) a Masked Region-Based Convolutional Neural Network, and (iii) Single Shot Detector (SSD).
7 . The system of claim 6 , further comprising calibrating the neural network using location loss and class loss.
8 . The system of claim 7 , wherein the computing instructions, when executed on the one or more processors, further perform:
training the neural network using a first set of training data corresponding to a portion of items in the store; and
processing the shelf image without retraining the neural network.
9 . The system of claim 8 , wherein the outputs of the neural network comprise a probability of a presence or absence of an out-of-stock detection.
10 . The system of claim 1 , wherein generating the report for the out-of-stock detection further comprises:
generating an alert; and
transmitting the alert to an employee, the alert comprising the coordinates of the out-of-stock detection and the item that corresponds to the coordinates.
11 . A method implemented via execution of computing instructions configured to run at one or more processors and configured to be stored at non-transitory computer-readable media, the method comprising:
receiving a plurality of images from one or more devices, the images corresponding to a store shelf of a store;
combining the plurality of images to generate a shelf image corresponding to the store shelf;
encoding the shelf image into a first processing format;
processing the shelf image in the first processing format with a neural network using pre-trained weights;
determining positions in the shelf image that correspond to an out-of-stock detection based on outputs from the neural network; and
generating a report for the out-of-stock detection, the report including an indication of coordinates of the out-of-stock detection and an item of the store that corresponds to the coordinates.
12 . The method of claim 11 , wherein the one or more devices comprise at least one of: a shelf-scanning robot, a drone, or a camera.
13 . The method of claim 11 , wherein combining the plurality of images further comprises combining the plurality of images based on a planogram indicating where items of the store are to be located, the planogram comprising horizontal-facing quantities and vertical-facing quantities.
14 . The method of claim 11 , wherein each of the plurality of images comprise metadata corresponding to a sequential order in which each of the plurality of images was captured.
15 . The method of claim 11 , wherein encoding the shelf image into the first processing format further comprises:
converting the shelf image to an encoded string format; and
processing the encoded string format via an application programming interface (API) based on horizontal facing quantities and vertical facing quantities of a planogram.
16 . The method of claim 11 , wherein the neural network comprises at least one of: (i) a region-based convolutional neural network (R-CNN), (ii) a Masked Region-Based Convolutional Neural Network, and (iii) Single Shot Detector (SSD).
17 . The method of claim 16 , further comprising calibrating the neural network using location loss and class loss.
18 . The method of claim 17 , further comprising:
training the neural network using a first set of training data corresponding to a portion of items in the store; and
processing the shelf image without retraining the neural network.
19 . The method of claim 18 , wherein the outputs of the neural network comprise a probability of a presence or absence of an out-of-stock detection.
20 . The method of claim 11 , wherein generating the report for the out-of-stock detection further comprises:
generating an alert; and
transmitting the alert to an employee, the alert comprising the coordinates of the out-of-stock detection and the item that corresponds to the coordinates.