US 11,734,813 B2
System and method for produce detection and classification
Issac Mathew, Bangalore (IN); Pushkar Pushp, Ranchi (IN); Viraj Patel, Ahmedabad (IN); Emily Xavier, Bentonville, AR (US); Gaurav Savlani, Bentonville, AR (US); Venkataraja Nellore, Rogers, AR (US); Rahul Agarwal, Bangalore (IN); Girish Thiruvenkadam, Bangalore (IN); and Shivani Naik, Seattle, WA (US)
Assigned to Walmart Apollo, LLC, Bentonville, AR (US)
Filed by Walmart Apollo, LLC, Bentonville, AR (US)
Filed on Jul. 19, 2022, as Appl. No. 17/867,922.
Application 17/867,922 is a continuation of application No. 16/521,741, filed on Jul. 25, 2019, granted, now 11,393,082.
Claims priority of provisional application 62/773,756, filed on Nov. 30, 2018.
Claims priority of application No. 201811028178 (IN), filed on Jul. 26, 2018.
Prior Publication US 2022/0351364 A1, Nov. 3, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G06F 18/24 (2023.01); G06N 3/08 (2023.01); G06N 20/20 (2019.01); G06V 20/68 (2022.01); G06N 3/045 (2023.01)
CPC G06T 7/0004 (2013.01) [G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 20/20 (2019.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30128 (2013.01); G06V 20/68 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
receiving, at a processor, an image of an item;
performing, via the processor using a first pre-trained neural network, feature detection on the image, resulting in a first feature map of the image;
performing, via the processor using a second pre-trained neural network, feature detection on the image, resulting in a second feature map of the image;
creating, via the processor, a combined feature map based on the first feature map and the second feature map;
performing, via the processor using a third pre-trained neural network, feature detection on the combined feature map, resulting in tiered neural network features; and
classifying, via the processor, the item based on the tiered neural network features, the classifying including implementing a set of pre-trained neural networks, the set of pre-trained neural networks having been produced based on the tiered neural network features, the classification being a combination of results of the set of pre-trained neural networks, and a result of each pre-trained neural network of the set of pre-trained neural networks being weighted based on a corresponding accuracy.