Real-Time Training Sample Capture for Retail Item Recognition
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Solution Overview
Problem
The collection of training data for machine vision technologies in retail environments is time-consuming and prone to errors due to the need for manual annotation and the variability of images captured in real-world conditions, which can differ from controlled conditions.
Innovation Solution
A method and system for capturing and validating training data samples in real-time using a computing device equipped with a sensor and processor, which generates region of interest bounding an item, obtains candidate label data, and receives validation inputs to create training samples for a classification model, including the region of interest and label data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used to collect training data, then label accuracy can be ensured, but the data collection process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary automated annotation using candidate label data before final validation, preparing the training data in advance with pre-generated labels that can be quickly validated rather than created from scratch during the collection process
Solution Approach 2:
The system enables self-service annotation by allowing the annotation process to validate and refine its own candidate labels through the validation interface, reducing the need for extensive manual intervention while maintaining accuracy through iterative self-correction
2Adaptability or versatility
If images are captured in real-world retail conditions, then the model can recognize items in dynamic environments, but the images vary significantly from controlled conditions reducing consistency
Solution Approach 1:
The system adapts to varying real-world conditions by adjusting parameters such as lighting compensation, perspective normalization, and scale variation handling during the annotation and model training processes, allowing consistent recognition across diverse retail environments
Solution Approach 2:
The system dynamically adjusts to changing environmental conditions by implementing real-time validation that accounts for variations in lighting, camera angles, and item positions, enabling the model to learn from and adapt to dynamic retail scenarios while maintaining recognition accuracy
3Measurement precision
If large volumes of training data are collected to improve model accuracy, then recognition precision increases, but the deployment process becomes more complex and resource-intensive
Solution Approach 1:
The system segments the training data collection and validation process into modular components including candidate label generation, validation interface, and training sample creation, allowing systematic processing and management of large datasets without overwhelming complexity
Solution Approach 2:
The system implements feedback loops where validation results from the validation interface feed back into refining candidate labels and improving training sample quality, enabling continuous improvement of recognition precision through iterative validation and model retraining
Data Source
AI summary
A method includes: capturing an image of an item; generating, from the image, a region of interest bounding the item; obtaining, from the image, candidate label data corresponding to the item; receiving a validation input associated with the candidate label data; and in response to the validation input, generating a training sample for a classification model, the training sample including (i) the region of interest and (ii) label data corresponding to the item.