US 12,450,710 B2
Digital quality control using computer visioning with deep learning
Olivier Bonneau, Paris (FR); Sami Hamani, Nogent (FR); Aloïs Peter Prieur De La Comble, Maisons-Alfort (FR); and Patrick Cottereau, Saint Maurice (FR)
Assigned to Accenture Global Solutions Limited, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on May 27, 2022, as Appl. No. 17/826,962.
Application 17/826,962 is a continuation of application No. 16/223,336, filed on Dec. 18, 2018, granted, now 11,373,287.
Prior Publication US 2022/0292668 A1, Sep. 15, 2022
Int. Cl. G06T 7/00 (2017.01); G01N 21/94 (2006.01); G06F 18/211 (2023.01); G06N 3/04 (2023.01); G06T 7/62 (2017.01); G06T 7/90 (2017.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 30/19 (2022.01); G08B 21/18 (2006.01)
CPC G06T 7/0004 (2013.01) [G01N 21/94 (2013.01); G06F 18/211 (2023.01); G06N 3/04 (2013.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 30/1912 (2022.01); G08B 21/18 (2013.01); G06T 7/62 (2017.01); G06T 7/90 (2017.01); G06T 2200/24 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30128 (2013.01); G06V 2201/06 (2022.01)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method for quality control based on one or more samples of a product, the method being executed by one or more processors, and comprising:
receiving sample data of the product comprising digital data representative of a sample of the product;
obtaining a set of features by processing the sample data through multiple layers of a residual network;
identifying a set of regions of the sample of the product by processing the set of features using a convolution neural network (CNN), each region of the set of regions including at least one object;
in response to determining that a number of objects within the sample of the product satisfies a threshold,
adjusting the sample data of the product to generate adjusted sampled data, wherein adjusting the sample data of the product comprises splitting the digital data representative of the sample of the product into multiple overlapping parts, and
reprocessing the adjusted sample data to identify the set of regions and the at least one object in each region;
determining a type of the at least one object in each region; and
selectively issuing an alert at least partially based on the type of the at least one object in each region, the alert indicating contamination within the sample of the product.