Synthetic Image Evaluation for Retail False Recognition Risk
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Solution Overview
Problem
Existing image recognition systems in retail environments suffer from high false recognition rates due to variations in lighting conditions between learning and recognition environments, making it difficult to accurately evaluate the risk of misrecognition for other products.
Innovation Solution
An information processing apparatus and method that generates pseudo-evaluation image data by simulating different lighting conditions and object attributes to evaluate the accuracy of image recognition for misrecognized objects, using techniques such as deep learning and generative adversarial networks to adjust and reproduce the recognition environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image recognition is performed in retail environments with varying lighting conditions, then the system can identify products in different settings, but the false recognition rate increases due to environmental variations between learning and recognition phases
Solution Approach 1:
The system performs preliminary evaluation by generating pseudo-evaluation image data that simulates the recognition environment before actual product recognition. This allows the system to assess potential false recognition risks in advance by creating test images with different lighting conditions and object placements, then evaluating recognition accuracy without needing to physically test each environment.
Solution Approach 2:
The system creates pseudo-evaluation image data as a copy or simulation of the actual recognition environment. By generating synthetic test images that replicate various lighting conditions and object arrangements, the system can evaluate recognition performance across multiple environments without requiring physical presence in each setting, thus maintaining reliability while adapting to versatility.
2Measurement precision
If the system evaluates image recognition accuracy for multiple objects in the same environment, then false recognition risk can be assessed, but computational complexity increases
Solution Approach 1:
The system segments the evaluation process by first generating pseudo-evaluation image data for one misrecognized object, then using that same simulated environment data to evaluate multiple other objects. This segmentation allows efficient batch evaluation of multiple products in the same environmental conditions without repeating the complex environment simulation for each object.
Solution Approach 2:
The pseudo-evaluation image data serves multiple functions: it evaluates the misrecognized object, assesses false recognition risk for other objects, and characterizes the environmental conditions. This multi-functionality reduces computational complexity by using a single set of simulated environment data to perform multiple evaluation tasks simultaneously.
Data Source
AI summary
This information processing device comprises: a generation circuit that, on the basis of attribute information of first image data resulting from imaging a first object in a first environment, generates second image data that reproduces an image in which a second object differing from the first object is disposed in the first environment; an evaluation circuit that evaluates the accuracy of image recognition for the second object on the basis of the second image data; and an output circuit that outputs the accuracy evaluation results.