Vending Machine Commodity Recognition via Image and Weight Fusion
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Solution Overview
Problem
In automatic vending machines, users lack information about commodities before purchase, leading to potential mistakes and inability to return items, thus necessitating an enhancement in the shopping experience.
Innovation Solution
An automatic vending method and apparatus that utilize image processing and weight detection to recognize and track commodities, allowing for pre-payment selection and settlement, using convolutional neural networks for image processing and a commodity feature library for accurate identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users purchase commodities without prior information in traditional automatic vending machines, then the settlement process is simple and fast, but users cannot make informed decisions and cannot return items by mistake
Solution Approach 1:
The system performs preliminary actions by capturing images of commodities before purchase, processing them through CNN models to extract features, and presenting commodity information to users before settlement. This allows users to make informed decisions about their purchases while maintaining automated settlement capability.
Solution Approach 2:
The patent introduces an intermediary information system that acts as a mediator between the user and the commodity. Image capturing apparatuses, CNN processing systems, and display interfaces serve as intermediaries to provide users with visual and textual commodity information without requiring direct physical interaction with the items.
2Measurement precision
If image processing and weight detection are implemented to recognize commodities, then accurate recognition and pre-payment selection are enabled, but the device complexity increases
Solution Approach 1:
The system segments the commodity recognition process into distinct functional modules: image capturing apparatuses positioned at specific locations, CNN processing units that extract features from images, weight detection systems that measure commodity mass, and a control system that integrates all data sources. This segmentation enables accurate recognition while making the complex system manageable and maintainable.
Solution Approach 2:
The patent implements multi-functionality by using a unified control system that handles both image processing and weight detection data, and by designing the CNN model to extract multiple types of features (visual appearance, text, patterns) from the same image input. This universal approach reduces overall system complexity despite the addition of multiple detection capabilities.
3Reliability
If multiple video stream capturing apparatuses are provided for comprehensive coverage, then all commodity selections are detected, but the device complexity and cost increase
Solution Approach 1:
The patent applies local quality by positioning video stream capturing apparatuses at specific strategic locations within the vending machine rather than uniformly distributing them. The control system adjusts detection parameters and attention focus based on the local characteristics of different shelves and commodity types, optimizing detection reliability while minimizing the number of apparatuses needed.
Solution Approach 2:
The system merges multiple detection functions into a unified control system that processes data from image capturing apparatuses, weight detection devices, and commodity information databases simultaneously. This consolidation reduces overall system complexity by eliminating redundant processing components and enabling coordinated operation of multiple apparatuses through a single control unit.
Data Source
AI summary
An automatic vending method and apparatus, and a computer-readable storage medium, relating to the field of automatic vending machines. The method includes: obtaining an image from a photographed video stream of commodities in an automatic vending machine; positioning an image of a commodity picked up by a user from the image; obtaining a change value of load weight of a shelf where the commodity is picked up by the user; determining a possible combination of commodities corresponding to the change value of load weight according to a weight of each commodity in the automatic vending machine; recognizing information of the commodity corresponding to the image of the commodity picked up by the user according to a pre-established commodity feature library and in conjunction with the possible combination of commodities; and performing settlement automatically for the user according to the recognized information of the commodity.