Image Analysis System for Product Identification via Area Segmentation
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Solution Overview
Problem
The existing techniques for product identification using images face high processing loads, leading to increased response times and reduced usability due to the large amount of arithmetic operations required for processing multiple products in an image.
Innovation Solution
An image analysis system that includes a product area detection unit, a same product area determination unit, and a product identification unit, which detects product areas, determines same product areas by comparing adjacent areas, and selects target areas for efficient product identification processing, thereby reducing the overall processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If product identification processing is performed on each of multiple products in an image, then identification accuracy is improved, but processing load increases
Solution Approach 1:
The image processing is segmented into two stages: first, the image is divided into multiple product areas corresponding to different products; second, only selected product areas (those with high identification failure risk) undergo detailed identification processing. This segmentation reduces the overall processing load while maintaining identification accuracy for critical products.
Solution Approach 2:
Different processing qualities are applied to different product areas based on their identification characteristics. Product areas with high identification failure risk receive intensive processing, while other areas receive standard processing. This local quality approach optimizes resource allocation and reduces unnecessary processing load.
2Measurement precision
If product identification processing is performed on each of multiple products in an image, then comprehensive product identification is achieved, but response time increases
Solution Approach 1:
The processing is segmented into rapid initial identification for all product areas followed by targeted re-processing only for areas with identification failures. This reduces response time by avoiding unnecessary re-processing of correctly identified products while maintaining accuracy for problematic areas.
Solution Approach 2:
Instead of performing comprehensive processing on all products, the system performs partial processing initially on all areas, then applies excessive (intensive) processing only where needed for failed identifications. This balance optimizes response time while ensuring accuracy.
Data Source
AI summary
An image analysis system (1) includes a product area detection unit (110), a same product area determination unit (120), and a product identification unit (130). The product area detection unit (110) detects a product area of each product from an image capturing a plurality of products. The same product area determination unit (120) determines, based on a result of comparing adjacent product areas with each other, a same product area being an area where a plurality of same products are displayed. The product identification unit (130) selects, as a target, at least one product area from a plurality of product areas included in the same product area, and identifies a product displayed in the same product area by processing the at least one product area selected as the target.