US 12,094,228 B2
Method of identifying level of doneness of food, device, and computer storage medium
Sanjun Liu, Foshan (CN); Yusheng Li, Foshan (CN); and Linnan Zhu, Foshan (CN)
Assigned to GUANGDONG MIDEA WHITE HOME APPLIANCE TECHNOLOGY INNOVATION CENTER CO., LTD, Foshan (CN); and MIDEA GROUP CO., LTD, Foshan (CN)
Filed by Guangdong Midea White Home Appliance Technology Innovation Center Co., Ltd., Foshan (CN); and Midea Group Co., Ltd., Foshan (CN)
Filed on May 6, 2022, as Appl. No. 17/738,761.
Application 17/738,761 is a continuation of application No. PCT/CN2020/133452, filed on Dec. 2, 2020.
Claims priority of application No. 201911245147.6 (CN), filed on Dec. 6, 2019.
Prior Publication US 2022/0262143 A1, Aug. 18, 2022
Int. Cl. G06V 20/68 (2022.01); G06T 7/73 (2017.01); G06V 10/26 (2022.01); G06V 10/766 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01)
CPC G06V 20/68 (2022.01) [G06T 7/73 (2017.01); G06V 10/26 (2022.01); G06V 10/766 (2022.01); G06V 10/803 (2022.01); G06V 10/82 (2022.01)] 17 Claims
OG exemplary drawing
 
1. A method of identifying a level of doneness of food, comprising:
obtaining an initial food detection image;
obtaining a current food detection image;
performing a differential fusion operation on the initial food detection image and the current food detection image to obtain a fused food detection image, wherein the performing a differential fusion operation on the initial food detection image and the current food detection image to obtain a fused food detection image comprises:
obtaining a pixel value of the initial food detection image and a pixel value of the current food detection image; and
determining a difference value between the pixel value of the initial food detection image and the pixel value of the current food detection image to obtain a pixel value of the fused food detection image; and
inputting the fused food detection image into a predetermined neural network model to obtain the level of doneness of food as an output of the predetermined neural network model.