Product Shelf Image Comparison for Layout Change and Defect Detection
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Solution Overview
Problem
Existing shelf layout analysis systems face issues with defects in captured images and layout changes, leading to increased workload and inefficiency in analysis.
Innovation Solution
A system that acquires images of product shelves, generates first and second image group information, compares them to a reference image, and provides notifications when differences are detected, thereby reducing the workload of shelf layout analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If shelf layout analysis is performed manually to handle image defects and layout changes, then analysis accuracy is maintained, but workload and time consumption increase
Solution Approach 1:
The system performs self-comparison between captured images and reference images to automatically detect layout changes and image defects. The image acquisition unit captures images, the image group information generation unit processes multiple images, and the comparison unit automatically compares them with reference data to identify differences, eliminating the need for manual analysis while maintaining accuracy.
Solution Approach 2:
The system establishes a feedback mechanism where captured images are compared against reference images, and differences are automatically detected and notified. This closed-loop feedback system continuously monitors for layout changes and image quality issues, providing timely alerts without requiring manual review of each image.
2Reliability
If manual shelf layout analysis is performed to ensure accuracy, then detection reliability is maintained, but productivity decreases
Solution Approach 1:
The system enables self-service analysis where the image acquisition unit, image group information generation unit, and comparison unit work together to automatically detect layout changes and image defects. The system compares captured images against reference images and generates notifications automatically, eliminating manual analysis requirements while maintaining high detection reliability.
Solution Approach 2:
The patent replaces manual mechanical analysis with an automated information processing system. The comparison unit uses computational methods to compare image groups with reference images, substituting human visual inspection and manual verification with automated digital comparison algorithms that operate continuously without fatigue.
3Measurement precision
If comprehensive image comparison is performed to detect all defects and changes, then detection precision is improved, but processing time increases
Solution Approach 1:
The system segments the image comparison process into distinct functional units: image acquisition, image group information generation, comparison with reference images, and notification generation. Each unit handles specific aspects of the comparison, allowing systematic and efficient processing of multiple images without overwhelming computational resources.
Solution Approach 2:
The system performs preliminary actions by pre-processing images into image group information items and preparing reference images for comparison. The image group information generation unit prepares multiple captured images in advance for systematic comparison, enabling efficient automated detection without requiring complex real-time analysis during the actual comparison phase.
Data Source
AI summary
An image acquisition means acquires images in which the product shelf is included. An image group information generation means generates a first image group information that includes the plurality of images. A comparison means makes a comparison between the first image group information item and the second image group information item that includes the reference image. A notification means provides notification when the image group information items are different from each other as a result of the comparison by the comparison means.