Shelf Item Analysis Using Image Recognition and Price Tag Identification
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Solution Overview
Problem
Managers in supermarkets and shopping malls face inefficiencies in managing items on shelves due to the time-consuming process of physically checking and recording information, leading to labor waste and low management efficiency.
Innovation Solution
An analysis method and system using image acquisition and convolutional neural networks for primary and secondary item classification and price identification, which includes pre-trained models for item classification, price tag identification, and decay/fresh-item state assessment, enabling automated data collection and display of item information on shelves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual checking and recording of item information is used, then accuracy of item information collection is maintained, but labor time and management efficiency deteriorate
Solution Approach 1:
The patent replaces the mechanical manual checking system with an automated image recognition system using convolutional neural networks. The system captures images of shelf items and uses pre-trained models to automatically identify and classify items, extract price information, and detect decay states, eliminating the need for manual inspection while maintaining high accuracy.
Solution Approach 2:
The patent creates a digital copy of the physical shelf environment through image capture. Instead of physically examining each item, the system uses photographs of the shelf and applies image recognition algorithms to extract information from these visual copies, significantly reducing the time and labor required for inventory management.
2Productivity
If automated image recognition is used, then management efficiency and speed are improved, but system complexity increases
Solution Approach 1:
The patent employs pre-trained convolutional neural network models that have been previously trained on large datasets of item images. These pre-trained models can be directly applied to new shelf images without requiring retraining, reducing the complexity of system deployment and maintenance while enabling rapid processing of item information.
Solution Approach 2:
The patent uses a unified image recognition framework that performs multiple functions simultaneously: item classification, price extraction, and decay detection. This multi-functional approach reduces overall system complexity compared to implementing separate specialized systems for each function.
Data Source
AI summary
The present invention belongs to the technical field of visual identification, and discloses an analysis method and system for items on the supermarket shelf. The method comprises acquiring one shelf image, which contains the items on the supermarket shelf and price tags corresponding to the items, and one shelf image corresponds to one shooting angle; acquiring a primary item classification result corresponding to the items on the supermarket shelf and a primary price identification result corresponding to the price tag according to the shelf image, a pre-trained primary item classification model and price tag text identification model; displaying the primary item classification result and the primary price identification result on the shelf image. The system comprises an image acquisition device, a primary classification device and a display device. According to the above technical solutions, the present invention prevents the managers of the shopping place from consulting the item packaging one by one in front of the shelf when managing the item information on the shelf, and then recording the item information, thus improving the management efficiency and facilitating the managers to know all the item information on the supermarket shelf at a glance.