Crowdsourced Cooking Image Annotation for Food Progress Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional cooking appliances require significant user expertise and manual monitoring, and existing AI systems for smart cooking appliances face challenges in accurately recognizing food items and determining cooking progress due to the vast variety of foods and imaging conditions, leading to poor recognition results and high costs for training data development.
Innovation Solution
A system and method for collecting and annotating cooking images using a user-friendly process that leverages crowd-sourced data, combining image processing with user input to create models for food recognition and cooking progress determination, utilizing a difference feature tensor to improve accuracy and efficiency, and allowing for simultaneous monitoring of multiple food items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional cooking appliances are used, then manual control is simple, but user expertise requirements are high and monitoring is time-consuming
Solution Approach 1:
The cooking appliance performs self-monitoring through integrated sensors that automatically detect food presence, cooking progress, and completion status without requiring user attention or expertise. The system serves itself by making autonomous cooking decisions based on sensor feedback.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor cooking parameters and feed this information back to the control unit, which adjusts cooking parameters in real-time. This closed-loop control eliminates the need for manual monitoring while maintaining cooking quality.
2Extent of automation
If AI systems are used for automatic food recognition, then monitoring is automated, but recognition accuracy is poor due to food variety and imaging conditions
Solution Approach 1:
The food recognition system is divided into multiple specialized components: a food detection model for presence detection, a food classification model for category identification, and a cooking progress estimation model for doneness assessment. Each segment handles a specific aspect of food monitoring to improve overall accuracy.
Solution Approach 2:
The system introduces intermediate processing steps including image preprocessing to normalize variations in lighting and角度, feature extraction to identify key food characteristics, and multiple neural network layers to progressively refine recognition accuracy before final classification.
3Measurement precision
If extensive training data is collected for AI models, then model accuracy improves, but data collection and annotation costs increase significantly
Solution Approach 1:
The training data serves multiple purposes simultaneously: it trains the food detection model, the food classification model, and the cooking progress estimation model. A single annotated image can contribute to all three models, maximizing the utility of each data point and reducing overall data requirements.
Solution Approach 2:
The system uses data augmentation techniques to create synthetic training examples by applying transformations such as rotation, scaling, color adjustments, and noise addition to existing images. This effectively multiplies the training dataset without requiring additional physical food samples or manual annotation efforts.
4Measurement precision
If image processing models are made larger for better accuracy, then prediction precision improves, but model size and training time increase
Solution Approach 1:
The overall image processing system is segmented into multiple specialized models of moderate size: a detection model for food presence, a classification model for food type identification, and a progress estimation model for cooking status. This division allows each model to be computationally efficient while collectively achieving high overall accuracy.
Solution Approach 2:
The system implements dynamic model selection and processing depth adjustment based on the specific cooking scenario and available computational resources. For simple cases, lighter processing is applied; for complex scenarios requiring higher precision, more computationally intensive processing is activated.
Data Source
AI summary
The method and system disclosed herein presents a food preparation system that provides a user-friendly data collection and data annotation process that leverages crowd-sourced data to reduce the prohibitive costs associated with developing an extensive training set using dedicated cooking experiments and specialized manual annotation efforts. The food preparation system and corresponding method collect one or more data types (e.g., structured data such as temperature and weight data, and unstructured data such as images and thermal maps) and allow the collected data to be annotated by human users in order to provide information regarding the process of cooking, as well as food item identity, location, and/or outline information. A combination of image processing and user input is utilized in order to create models with food recognition and determining cooking progress.