Fatter R-CNN food classification and GL value identification method based on attention mechanism SENet

A food classification and attention technology, applied in the field of GL value calculation, can solve problems such as not being prominent enough, difficult to extract effective features, and affecting classification accuracy

Pending Publication Date: 2021-02-12
BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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Problems solved by technology

In the existing food recognition, CNN is generally used for target detection and food classification, but in complex environments such as dim l...

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  • Fatter R-CNN food classification and GL value identification method based on attention mechanism SENet
  • Fatter R-CNN food classification and GL value identification method based on attention mechanism SENet
  • Fatter R-CNN food classification and GL value identification method based on attention mechanism SENet

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Embodiment Construction

[0026] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with examples. The specific examples are for illustration only and are not intended to limit the invention.

[0027] figure 1 and figure 2 It is a schematic flowchart of a food classification and GL value recognition method based on the Faster R-CNN of the attention mechanism SENet and the linear regression equation according to the embodiment of the present application. see figure 1 and figure 2 It can be seen that the food classification and GL value identification method and system based on the Faster R-CNN of the attention mechanism SENet and the linear regression equation provided by the embodiment of the present application may include:

[0028] Step S1: The user terminal takes a picture of the food containing the reference object (such as a finger) through the mobile phone and uploads...

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Abstract

The invention discloses a Faster R-CNN food classification and GL value identification method based on an attention mechanism SENet. The method comprises the following steps: acquiring a picture whichis uploaded to a server by a user and contains a reference object, and performing food detection and identification on a food picture through a Faster R-CNN algorithm based on an attention mechanismSENet; enabling the attention mechanism to enable the feature extraction network to use global information without being limited by local small-view-field information, and meanwhile the problems of gradient disappearance and degradation caused by increase of the network depth are effectively avoided; inputting the type and position of the food and the position of the reference object into a trained linear regression model, and predicting the volume of the food; calculating a GL (glycemic load) value of food according to the category of the food and a GI (glycemic index), judging the edible category of the food, and generating an edible suggestion of the food; and finally feeding back a result to the user.

Description

technical field [0001] This application relates to the field of food safety, the field of image recognition, and in particular to the calculation method of GL value. Specifically, it is a method for predicting changes in human blood sugar load value caused by dietary intake during meals. It mainly provides blood sugar load reference for diabetic patients, establishes and improves diet structure, and forms a virtuous circle of convenient diet. Background technique [0002] Introduction to existing technology [0003] Diabetics who want to measure blood sugar need to wash their hands with warm water and neutral soap first, and rub the finger that needs blood collection repeatedly until the blood supply is rich; use a blood collection pen to stick to the finger pulp, press the spring switch, and acupuncture the finger pulp; turn on the blood sugar Switch on the meter, take a test strip and insert it into the machine to get the blood sugar value. But this process is not only ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G16H20/60
CPCG06N3/08G16H20/60G06V20/68G06V2201/07G06N3/045G06F18/241
Inventor 刘瑞军王俊章博华张伦
Owner BEIJING TECHNOLOGY AND BUSINESS UNIVERSITY
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