Object Recognition System with Pre-Registration Similarity Check
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Solution Overview
Problem
In commodity reading apparatuses, incorrect object recognition can occur when registering new commodities with feature values similar to existing ones, leading to potential misclassification during sales registration processing.
Innovation Solution
A commodity reading apparatus comprising a designation module, extraction module, storage module, calculation module, learning module, and notification module, which compares feature values and executes notification on recognition performance before learning processing, ensuring accurate registration by designating learning targets and calculating similarity degrees to prevent misclassification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the dictionary for collation is updated by registering a new commodity, then the recognition capability is improved, but the recognition accuracy deteriorates when the new commodity has similar feature values to existing commodities
Solution Approach 1:
The system performs preliminary similarity assessment between the new commodity's feature values and existing commodity feature values before completing the registration. By calculating similarity degrees in advance and comparing them against threshold values, the system identifies potential recognition conflicts before they occur, allowing preventive actions to be taken.
Solution Approach 2:
The system provides feedback to the user regarding the similarity degree between the new commodity and existing commodities in the dictionary. This feedback mechanism allows users to review the potential impact of registration on recognition accuracy and make informed decisions about whether to proceed with the registration.
2Measurement precision
If feature values are compared and similarity degree is calculated before registration, then recognition accuracy is maintained, but the processing time increases
Solution Approach 1:
The system changes the parameter of similarity threshold values to balance between recognition accuracy and processing efficiency. By adjusting threshold values, the system can control the stringency of similarity checks, allowing faster processing when lower precision is acceptable and more thorough checks when higher precision is required.
3Measurement precision
If the system notifies users about similarity issues before registration, then recognition accuracy is preserved, but the registration process becomes more complex
Solution Approach 1:
The system provides self-service functionality by automatically calculating similarity degrees and generating notifications about potential recognition issues. Users can review the automated assessment results and make registration decisions based on clear, system-generated information, reducing the need for complex manual evaluation procedures.
Data Source
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AI summary
A calculation module compares the first feature value extracted and a second feature value indicating feature of each commodity registered in a dictionary serving as a storage module to calculate a similarity degree therebetween. A notification module executes notification as to a recognition performance prior to a leaning processing in which the first feature value is registered as the feature value of the learning target article in the storage module by a learning module if a similarity degree between the first feature value and the second feature value of the registered commodities excluding the learning target article is equal to or greater than a first threshold value or a similarity degree between the first feature value and the second feature value of the learning target article contained in the registered commodities is equal to or smaller than a second threshold value smaller than the first threshold value.