33 results about "Food recognition" patented technology

The invention discloses a personalized recipe recommendation system, a recommendation method and an interaction system, and relates to the technical field of diet recommendation, the system comprises a user data collection module, a food image recognition module and a personalized recommendation module, the user data collection module is used for obtaining health data and diet preference data of a user, the health data comprises basic information, health conditions and exercise habits, the basic information comprises regions, the health conditions comprise allergen lists, chronic diseases and diet targets, and the food image recognition module is used for obtaining food photos uploaded by a user, recognizing the food photos to obtain food recognition results and sending the food recognition results to the user. The food identification result comprises a food type corresponding to the food photo, and the personalized recommendation module is used for determining a recommended recipe recommended to the user based on the health data, the diet preference data and the food identification result. By considering more comprehensive influence factors, a more suitable and personalized recipe can be recommended to the user.

An automated food recognition platform comprises a memory, processor, and various submodules, utilizing artificial intelligence to analyze digital media (images, photos, audiovisual) for food identification and attributes. It includes an edible food database and recipe submodule, generating personalized recipes considering dietary preferences. The automated food recognition platform also features allergy and drug interaction submodules, informing users of relevant information. A marketplace submodule tracks inventory levels, enhancing recipe recommendations. Leftover identification and meal planning submodules offer creative cooking solutions. An interactive submodule fosters a chef-user community, incorporating user feedback and social media data to refine recipe recommendations. Chefs receive compensation based on a chef rating, determined by user interactions. The platform promotes chef recipes through paid advertising services within the interactive submodule.

The invention provides a food classification component identification method and system based on multi-scale feature attention synergy, and the method comprises the steps: constructing a multi-task neural network model, sequentially inputting a food image into a global feature module and a progressive local feature module, and respectively extracting global semantic information and refined local features of different scales; feature interaction between food category identification and component analysis tasks is promoted through a cross attention cooperation module, and key areas and important component information in images are dynamically selected; gradually activating different depth levels of the network by adopting a staged end-to-end training strategy, and introducing a KL divergence constraint to enhance the difference between feature scales; and simultaneously generating prediction results of food categories and component labels through a multi-task classifier. The technical problems of food identification and component analysis task splitting, insufficient local feature expression and insufficient cross-task information complementation are solved.

The embodiment of the invention discloses a multi-modal fusion food recognition large model construction and application method and system, and the method comprises the steps: obtaining a multi-modal training data set, carrying out the combined training of multi-modal training data in the multi-modal training data set, and obtaining a multi-modal fusion food recognition large model; obtaining multi-modal data of food to be identified; performing feature extraction and fusion on the multi-modal data through a multi-modal fusion food identification large model to obtain corresponding fusion feature information; and based on the fusion feature information, utilizing a multi-modal fusion food identification large model to carry out food type identification and nutritional ingredient analysis, and outputting an identification result and a nutrition suggestion. According to the embodiment of the invention, the method achieves the recognition of the to-be-recognized food based on the multi-modal data of the to-be-recognized food through the powerful learning and reasoning capabilities of a large model, achieves the more accurate and comprehensive recognition of the food, and improves the recognition accuracy and reliability of the food.

The invention discloses an intelligent food identification and nutrition analysis method based on deep learning, and belongs to the technical field of image analysis. The method comprises the following steps: 1, collecting input data, and preprocessing the data; 2, determining a position and a mask area of a target in the scene, and generating a dense depth map; 3, constructing a food three-dimensional point cloud, denoising the three-dimensional point cloud, and removing abnormal points; 4, obtaining a three-dimensional feature, extracting a two-dimensional feature from the image, and carrying out weight fusion; 5, splicing the obtained features, and reinforcing the fused feature information; 6, predicting the weight and calorie of the food by regression; and 7, optimizing model prediction and verifying a prediction effect. According to the method, feature fusion in a 2D + 3D modal mode is adopted, data enhancement is performed on an original image, 2D features are extracted, a 3D point cloud is generated at the same time, and multi-modal feature fusion is performed to predict the calorie and the weight of the food, so that consumers can be helped to accurately estimate indexes such as the calorie and the weight of the food and formulate scientific diet methods and suggestions.

To provide an updating method capable of improving identification accuracy of a food product by using a machine learning model.SOLUTION: An updating method is an updating method for a machine learning model, which is executed by an image processing system 40. The updating method includes: a step S13 of outputting identification result information for displaying an interior image in which inside of a storage having a function for cooling a stored object is displayed and a detection frame superposed on a region determined to display a food product through a machine learning model of the interior image, on an information terminal 60; a step S17b of acquiring a result of a determination as to whether or not a food product is displayed within the detection frame made by a user; and a step S21b of causing the machine learning model to learn first correct answer data that associates predetermined identification information indicating that a food product is not displayed within the detection frame with a learning image including at least an image within the detection frame when the determination result indicates that a food product is not displayed within the detection frame.SELECTED DRAWING: Figure 6

The invention provides an intelligent dinner plate based on multispectral thermal imaging fusion, and belongs to the technical field of intelligent dinner plates. The multi-mode sensing module collects physical and chemical information of food in the dinner plate; the edge calculation and main control module executes real-time data processing at an equipment end; the lightweight food identification and segmentation model is used for identifying food types based on multispectral image data and segmenting areas of the food; the multi-sensor data fusion algorithm is used for fusing the food types, the area proportion information and the total weight, estimating the initial weight of each kind of food based on a pre-trained density factor, and outputting a structured list containing the food types and the initial weights; the cloud nutrition calculation engine receives the structured list and the related hyperspectral feature data, and queries and identifies standard nutritional ingredients of the food from a nutrition database; and calibrating the initial weight by using the hyperspectral characteristic data, and calculating and summarizing the total nutritional ingredients and total calorie of the whole meal based on the calibrated weight and the finely-adjusted nutritional ingredients.

The invention provides a food information processing method and device, and the method comprises the steps: receiving a to-be-recognized food image uploaded by a user and a question message initiated by the user; generating an answer message corresponding to the question message according to the propaganda copywriting in the food image to be identified, and pushing the answer message to the user; and generating an evaluation report of the to-be-identified food, pushing a report viewing control to the user, and pushing the evaluation report to the user in response to the report viewing control triggered by the user. According to the embodiment, the question message of the user can be replied according to the image of the to-be-recognized food, and the complete evaluation report for the to-be-recognized food is generated, so that the food recognition efficiency is improved, and the user experience is improved while the food safety is guaranteed.

To provide an information providing system and a food product that allow a consumer to select a product with confidence.SOLUTION: A food information providing system 11 outputs information useful for understanding the quality of predetermined food in response to a request from a consumer's terminal, and comprises: production information recording means 14 that records production information that is at least one of the place of production and the period of production of the food; quality information searching means 16 that, on condition that a request for outputting information useful for understanding the quality of the food is received from the consumer's terminal 13, searches for the information useful for understanding the quality of the food, on the basis of information for specifying the food and the production information recorded by the production information recording means 14; and information output means 20 that outputs, to the consumer's terminal, the information useful for understanding the quality of the food searched for by the quality information searching means 16.SELECTED DRAWING: Figure 2

The invention discloses a refrigerator AI intelligent food fresh-keeping system, relates to the technical field of intelligent homes, and aims to solve the problem that in the prior art, although freshness can be predicted through AI, fresh-keeping parameters are not dynamically regulated and controlled, according to the technical scheme, the refrigerator AI intelligent food fresh-keeping system comprises a refrigerator body, and the refrigerator body comprises a refrigerating chamber partition and a freezing chamber partition; precise matching of food fresh-keeping modes is achieved by dividing differentiated fresh-keeping spaces (dry area / vacuum / controlled atmosphere / photosynthesis / -18 DEG C / -32 DEG C) and integrating an AI dynamic decision engine; the system further comprises a refrigerating system, a control system, a food recognition module, an AI decision module and a fresh-keeping unit. Space function customization: configuring exclusive fresh-keeping parameters for each space; the AI two-stage decision-making mechanism comprises the following steps: firstly, identifying a food full-applicable fresh-keeping mode, and selecting an optimal solution during conflict; man-machine cooperation prompting: guiding the placement position of a user through an alarm signal, and automatically optimizing parameters; and the modular expansion capability is realized, and function combination space can be added according to needs.

The invention discloses a food identification method and system based on multi-feature fusion, and relates to the technical field of food identification methods, and the method comprises the steps: generating a comprehensive feature vector based on the multi-feature fusion of a shape feature, a texture feature and a cutter feature, and determining a primary identification result of food according to the comprehensive feature vector, the technical effect of multi-feature fusion is realized, and the accuracy of the primary identification result of the food is ensured. Therefore, the advanced recognition result of the food is determined according to the outer contour of the food, the plurality of dynamic images and the primary recognition result of the food; the review area is determined based on the advanced recognition result of the food and the food image, and the final recognition result of the food is determined according to the features corresponding to the review area, the advanced recognition result of the food and the review mapping relation, so that the high accuracy of the final recognition result of the food is further ensured; and accurate multi-stage control of food identification is realized.

The utility model discloses a food processor system for food identification and heat energy intake control, and particularly relates to the field of food, the food processor system comprises a lifting mechanism, the bottom end of the lifting mechanism is fixedly connected with a weighing mechanism, the weighing mechanism comprises a base, the inner wall of the base is connected with a storage box in a sliding mode, and the storage box is connected with the lifting mechanism. The inner wall of the base is fixedly connected with a limiting frame, the upper surface of the limiting frame is fixedly connected with a weighing sensor, the upper end of the weighing sensor is fixedly connected with a placing frame, and the inner wall of the placing frame is provided with a limiting groove; according to the utility model, the heat of food can be accurately identified through the weighing mechanism, the weight of the food can be detected by the weighing sensor during use, the heat of the food can be accurately measured and calculated under the combination of the species and the weight of the food, and soil and water on the food can be collected into the storage box through the filter screen, so that the food can be conveniently stored. Therefore, the weighing accuracy can be ensured, then centralized processing is carried out, and the problem that a traditional detection table is inconvenient to clean is solved.

This display method is executed by an information terminal (60). The display method includes: an acquisition step of acquiring information on a detection frame applied to each of a plurality of foods, the detection frame indicating identification results, obtained by a machine learning model, on the plurality of foods appearing in an internal image of a refrigerator; a determination step (S18a) of determining a display order of the identification results on the plurality of foods on the basis of the acquired information on the detection frame; and a display step (S19a) of displaying a confirmation screen for the user to confirm whether or not the identification results on the plurality of foods are correct. On the confirmation screen, the identification results on the plurality of foods are displayed in order on the basis of the determined display order.

Cooking appliance (10) with a cooking chamber (12) in which several slots (E1, ... E9) are defined and a camera (30) which serves to detect at least one cooking parameter of food which is placed in one of the slots, characterized by a light guide (32) which extends between the camera (30) and the cooking chamber (12), wherein the light guide (32) has an optical interface (34) for each slot.

The application is suitable for the technical field of pre-prepared food reheating, and provides a pre-prepared food segmented reheating method and system based on real-time food recognition, which acquires food packaging recognition information, acquires food types and cooking requirements according to the recognition information, compares and corrects an optimal reheating curve with performance parameters of a current device, calculates a curve fitting degree that can be reached by the device, maximizes the approximation of the optimal reheating effect within the range allowed by the device for the corrected reheating curve, acquires individualized parameters through user input or historical preference data, converts the individualized parameters into correction coefficients, dynamically adjusts the reheating curve according to the correction coefficients, and determines target parameters of each stage, generates a feedback vector according to the score combined with operation data, updates parameters of the optimal reheating curve of the corresponding food according to the feedback vector, and optimizes a public optimal curve model according to common characteristics, which has the advantages of intelligence, individualization and self-optimization of the pre-prepared food reheating process.

An automated food recognition platform comprises a memory, processor, and various submodules, utilizing artificial intelligence to analyze digital media (images, photos, audiovisual) for food identification and attributes. It includes an edible food database and recipe submodule, generating personalized recipes considering dietary preferences. The automated food recognition platform also features allergy and drug interaction submodules, informing users of relevant information. A marketplace submodule tracks inventory levels, enhancing recipe recommendations. Leftover identification and meal planning submodules offer creative cooking solutions. An interactive submodule fosters a chef-user community, incorporating user feedback and social media data to refine recipe recommendations. Chefs receive compensation based on a chef rating, determined by user interactions. The platform promotes chef recipes through paid advertising services within the interactive submodule.

The invention belongs to the technical field of meal delivery system matching, and particularly relates to a food nutritional ingredient analysis method for a catering system. Comprising the following steps: establishing a food identification network model; food image samples are obtained to serve as training data to train a food recognition network model, and a fusion feature database of various kinds of food is generated; acquiring a real-time dinner plate image of the meal delivery system, inputting the dinner plate image into the food identification network model, and identifying the food category in the dinner plate; searching a food corresponding region in the feature map by using a sliding window based on a region suggestion network, and determining region coordinates; three-dimensional information is established based on image processing and pixel point matching, material object component analysis is completed, and nutritional ingredient data is obtained. The method is used for automatically analyzing and judging the component and the category of the food by acquiring the visual information of the food, a technical scheme is provided for automatic detection and output of nutritional ingredients of an automatic meal delivery system, and a technical basis is provided for subsequent work such as suggestion of the nutritional ingredients.

The invention relates to a food identification method, and belongs to the field of image identification and food identification. Comprising the following steps: respectively processing color, texture, shape and structural features of basic features through four parallel feature streams; carrying out dense block processing on a feature map output by each feature flow; inputting the feature map subjected to dense block processing into a transition layer for dimensionality reduction; inputting the feature map processed by the transition layer into a feature enhancement module, and further performing convolution processing on the feature map processed by the transition layer to obtain an enhanced feature; inputting the enhanced features into an attention mechanism for processing; fusing all the feature maps processed by the attention mechanism, and adjusting the contribution of each feature processed by the attention mechanism through an adaptive weighting module; and inputting the feature map subjected to feature fusion into a classification module, and performing final classification prediction. According to the invention, complex food images can be identified, confusion between similar food types is effectively reduced, and the food identification and classification effect is improved.

To provide a method for presenting food that allows a user who manages the diet of a child to easily recognize the food to be fed to the child for allergen examination.SOLUTION: A presentation system S includes: a known-allergen recognition unit 1d configured to recognize known allergens that are highly likely to cause an allergic reaction when ingested by a child; a cross-allergen recognition unit 1e configured to recognize cross allergens that are highly likely to cause a cross reaction related to the known allergens on the basis of the known allergens; a food recognition unit 1g configured to recognize cross foods that are foods containing the cross allergens on the basis of the cross allergens; and a test food presentation unit 1h configured to present the cross foods to a user.SELECTED DRAWING: Figure 2

A method for treating food in an appliance (14) by at least one treatment device of the appliance (14) comprises the steps of a. providing a food recognition system with at least one camera image of a food item to be treated by the appliance (14)(S1), b. picture data about the food item by extracting a number of at least one characteristic features of the food item from the at least one camera image (S2), c. assigning a food identification to the food item based on at least one of the number of characteristic features (S4, S6), d. executing a food treatment process or program allocated to the assigned food identification (S8). According to the invention, the camera image is provided during step a. (S1) by an external camera device (12, 14), wherein the camera image provision is executed—by data retrieval by the food recognition system, and / or—by data transfer by the mobile camera device (12, 20), and / or—on the operator's initiation. Further disclosed is a food treatment system comprising an appliance (14) for treating food and an external camera device (12, 20) as well as a software application.

This disclosure provides a method and apparatus for food ingredient identification, an electronic device, and a computer-readable storage medium, relating to the fields of image recognition and health technology. The method includes: receiving an image to be identified; identifying the food ingredients in the image using a pre-trained food recognition model to obtain a preliminary identification result; obtaining the feature vector of the image to be identified; determining the feature similarity between the feature vector of the image to be identified and the feature vectors of historical images, where historical images are images where food ingredients have been identified and the identification results have been corrected; obtaining similar images from the historical images based on the feature similarity; and correcting the preliminary identification result based on the correction data of the similar images to obtain the final identification result of the food ingredients in the image to be identified. This disclosure improves the accuracy of food ingredient identification by correcting the preliminary identification result of the food ingredients in the image using similar images.

The invention belongs to the technical field of flaky food recognition and sorting, and discloses a flaky food recognition and sorting device which comprises a supporting frame, a conveying mechanism and a fixing support are arranged on the supporting frame, the fixing support is of a frame type structure, a sorting space is formed between the interior of the fixing support and the conveying mechanism, and a sleeve block is movably arranged on the fixing support and movably connected with the conveying mechanism. A cylinder is fixedly mounted on the sleeve block, and a vacuum chuck is fixedly mounted at the output end of the cylinder; a storage box is arranged on the outer side of the fixing support, and the sorting space communicates with the storage box. A group of blowers are arranged at the top of the fixed bracket; and an identification sensor is also arranged in the fixed bracket. According to the device, through cooperation of the identification sensor, the air cylinder, the sleeve block and the vacuum suction cup, flaky food can be identified and collected, complete and incomplete food can be accurately identified, defective products can be effectively removed, and the product quality is ensured; and due to the automatic identification and collection process, the manual detection and sorting time is shortened, and the production efficiency is improved.

This invention discloses a method for food image segmentation and removal based on instance segmentation and spatial prior clustering, comprising three steps: Step 1, inferring from the photo using the YOLO instance segmentation model, extracting the segmentation mask, and cropping it into a black background image; Step 2, extracting the foreground from the image using the GrabCut algorithm, converting it to a transparent background, and generating an RGBA format image; Step 3, performing spatial partitioning on the transparent background image based on Euclidean distance transformation, using the outer perimeter as the plate prior and the center as the food prior, performing K-means clustering in the Lab color space, automatically identifying the plate and food clusters based on the spatial enrichment ratio of each cluster at the perimeter and center, and removing plate pixels based on external reachability connectivity verification, outputting a transparent background image containing only the food. This invention eliminates the need for manual region labeling or setting color thresholds, can adaptively handle plates of different colors, shapes, and sizes, is suitable for diverse dining scenarios, and supports tasks such as food recognition and nutritional estimation.

Food image recognition has been widely applied to scenes such as intelligent ordering, but the problems of insufficient recognition precision and low calculation efficiency of Chinese food due to the characteristics of changeable food material forms, similar appearances of different dishes and the like are still solved. The invention provides a Chinese food lightweight identification method based on improved YOLOv8n. A DCNv3 improved C2f module is introduced into a backbone network, so that the modeling capability of food form change details is enhanced; the CAA-HSFPN feature pyramid is fused in Neck, and the multi-scale feature fusion effect is improved, so that similar dishes are effectively distinguished, and the model parameter quantity is reduced. A DCA-YOLOv8n detection network is constructed on the basis of improvement, and the effectiveness of the detection network is verified through comparison experiments with various mainstream models on a self-established Chinese food data set. According to the method, the performance indexes of Chinese food identification accuracy and the like are remarkably improved while the light weight is kept, and the method is deployed in an actual Chinese food identification system.

A presentation system S includes: a user information recognition unit configured to recognize user information; a current emotion recognition unit configured to recognize a current emotion of a user; a recommended food recognition unit configured to recognize a recommended food item by using a prediction model which receives an input of the user information and the current emotion and outputs the recommended food item; and a food information presentation unit configured to obtain and present the user with food information about the recommended food item. The current emotion recognition unit is configured to estimate, as the current emotion, coordinates selected by the user from an emotion model in which coordinates are defined based on a plurality of basic emotions.

An embodiment of the present invention provides a food recognition method, device, storage medium, and electronic device. Among them, the method includes: obtaining a first picture containing a first food; using a first model to analyze the first picture to determine first region information of the first food contained in the first picture; cropping an image of the first food in the first picture based on the first region information, and using the cropped image as a second picture; using a second model to analyze the second picture to determine a first food name of the first food contained in the second picture; and outputting the first region information and the first food name. Through the present invention, the problem of low food recognition accuracy in the related art is solved, the effect of accurately recognizing food is achieved, and the accuracy of food recognition is improved.

The application discloses a method for intelligent food recognition and nutrition analysis based on deep learning, and belongs to the technical field of image analysis.The method comprises the following steps: 1, collecting input data and pre-processing the data; 2, determining the position of a target in a scene and a mask area, and generating a dense depth map; 3, constructing a food three-dimensional point cloud, denoising the three-dimensional point cloud, and removing abnormal points; 4, obtaining three-dimensional features, extracting two-dimensional features from the image, and performing weight fusion; 5, splicing the obtained features, and strengthening the feature information after fusion; 6, regressing and predicting food weight and heat; and 7, optimizing model prediction and verifying the prediction effect.The method adopts a 2D+3D modal feature fusion mode, performs data enhancement on an original image, extracts 2D features, simultaneously generates a 3D point cloud, and performs multi-modal feature fusion to predict food heat and weight, so that the method can help consumers accurately estimate food heat, weight and other indexes, and formulate scientific diet methods and suggestions.