Nutrition intervention analysis system and analysis method based on convolutional neural network

Abstract

The invention discloses a nutrition intervention analysis system and analysis method based on a convolutional neural network, and the system comprises a first convolutional neural network, a second convolutional neural network, a database, a control unit and an operation unit which are connected with one another. The first convolutional neural network is used for detecting a food image after training, judging whether food or a marker is detected or not, calculating marker box information of the food and the marker and intercepting an image of a food area under the condition that the food and the marker are detected at the same time, and the second convolutional neural network is used for identifying the input image after training, outputting the characteristic data of the food; the database is used for the control unit to control data calculation and operation of the first convolutional neural network and the second convolutional neural network, and the operation unit is used for operation of non-feature analysis; the first convolutional neural network and the second convolutional neural network are respectively formed by alternately connecting convolutional layers, maximum pooling layers and activation function layers.

Description

technical field [0001] The invention relates to a nutritional intervention analysis system and analysis method based on a convolutional neural network. [0002] technical background [0003] Image recognition technology is widely used in various fields. However, because the shooting of pictures is affected by conditions such as angles and lighting, it is not possible to directly apply pictures to object recognition, but it is necessary to extract its intrinsic features for specific problems. Based on the relative invariance of the internal characteristics of the object, the detection and classification of the object are completed. In recent years, with the rapid development of deep learning technology, neural network models have been partially applied to the identification of nutritional interventions. However, nutrition estimation not only needs to accurately identify food images, but also needs to estimate the quality of the food, combined with the nutrition list of this t...

AI Technical Summary

Problems solved by technology

Therefore, there is essentially no technology in the existing technology that can realize the detection and classification of food images and the means of estimating the corresponding nutritional components, let alone estimate the nutritional components of food

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