Shelf Image Registration for Current Product Recognition
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Solution Overview
Problem
Existing object scanning systems require tedious manual registration of learning images and product setting, and are prone to scanning products not currently on sale, leading to cumbersome operations.
Innovation Solution
An information processing device with an image sensor and central processing unit that automates the registration of learning images and product setting, allowing easy addition of images and designation of products as for-sale or not-for-sale, using product information storage and image recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual registration of learning images is performed for each target product, then image recognition accuracy is improved, but operation time and complexity increase significantly
Solution Approach 1:
The system performs self-learning by automatically capturing images of products on the shelf and registering them as learning images without requiring manual intervention. The image recognition device autonomously identifies products and accumulates learning data, eliminating the tedious manual registration process while maintaining recognition accuracy.
Solution Approach 2:
The system pre-registers learning images for all products in the database before actual scanning operations. By preparing learning images in advance through automatic shelf scanning and image accumulation, the system ensures recognition accuracy is established beforehand, avoiding time-consuming manual registration during operation.
2Adaptability or versatility
If all products in storage means are set as scanning targets, then comprehensive product coverage is achieved, but products not currently on sale are incorrectly extracted
Solution Approach 1:
The system dynamically updates the for-sale status of products based on real-time shelf scanning and detection. Products are automatically added or removed from the scanning target list according to their current sale status, allowing the system to adapt to changing inventory conditions while maintaining accurate product extraction.
Solution Approach 2:
The system continuously scans the shelf and uses image recognition feedback to identify products that should be added or removed from the for-sale list. This feedback mechanism ensures that only currently available products are included in scanning targets, improving extraction accuracy while maintaining comprehensive coverage.
3Reliability
If manual searching and setting of target products is performed, then precise for-sale control is achieved, but operation complexity and time increase
Solution Approach 1:
The system automatically performs for-sale setting by scanning products on the shelf and using image recognition to identify and register them as for-sale products. This self-service approach eliminates the need for manual searching and setting, maintaining accurate for-sale control while dramatically simplifying operations.
Solution Approach 2:
The system replaces manual mechanical operations of searching and setting products with automated image recognition and computer-based product identification. This substitution maintains precise for-sale setting accuracy while eliminating the cumbersome manual processes.
Data Source
AI summary
This information processing device includes: an image sensor that captures an image of a product; and a central processing unit (CPU) that: extracts product candidates to which a learning image is to be added by referring to a product information storage that stores product information, receives a designation of a product to which the learning image is to be added and registered from among the product candidates that have been extracted, and registers, in the product information storage, the image of the product captured by the image sensor as the learning image in association with the product that has been designated.