Food Allergen Detection via Image Segmentation and Classification
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Solution Overview
Problem
Individuals with food sensitivities face challenges in identifying and avoiding allergens due to their varied forms and hidden presence in food substances, making it difficult to manage reactions effectively.
Innovation Solution
A method that captures images of food scenes, segments and classifies food substances using computer vision and machine learning, cross-referencing them with an allergen database to predict user-specific risks, and displays augmented images highlighting potential allergens through a user-friendly interface on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification methods are used to detect food allergens, then users can identify allergens, but the process requires high user effort and time
Solution Approach 1:
The patent replaces manual mechanical identification methods with an automated computer vision system using image capture, segmentation, and classification algorithms to detect food substances and their allergens, eliminating the need for manual inspection while maintaining high accuracy
Solution Approach 2:
The system performs preliminary classification of food substances into categories (meat, seafood, vegetables, etc.) and pre-identifies potential allergens before user consumption decisions, allowing users to make informed choices without time-consuming manual analysis
2Measurement precision
If comprehensive allergen detection is performed on all food substances, then identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex task of allergen detection into distinct modules: image capture, image segmentation (dividing the image into food substance regions), classification (categorizing food types), and allergen identification (cross-referencing with allergen database), making the overall system more manageable and implementable
Solution Approach 2:
The patent introduces an intermediary classification layer that categorizes food substances into broad groups (meat, seafood, vegetables, etc.) before performing specific allergen detection, simplifying the detection process by reducing the search space and enabling more efficient cross-referencing with allergen databases
3Ease of operation
If traditional methods are used to identify food substances, then the process is simple, but accuracy and sensitivity in detecting hidden allergens is low
Solution Approach 1:
The patent replaces simple visual inspection with automated computer vision technology that captures images, segments food regions, and uses machine learning classification to identify food substances and their allergens with high sensitivity, maintaining ease of use through automated processing
Solution Approach 2:
The system provides feedback by displaying the identified food substance, detected allergens, and associated risk level to the user, enabling informed decision-making while maintaining operational simplicity through automated analysis and clear presentation of results
Data Source
AI summary
A method of displaying information relating to an allergen present in a food substance together with a user profile, the method including capturing an image of a scene, segmenting the image to determining at least a segmentation of the food substance in the image, determining a classification of the food substance using the segmentation, determining a presence of the allergen in the food substance using the classification of the food substance and a database of allergens and food substances, determining a risk to a user using the user profile specifying a user sensitivity to the allergen, and displaying an output including a view of the user profile and the image, wherein the image is augmented to identify the presence of the allergen.