Commodity Detection Terminal Using Image Segmentation
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Solution Overview
Problem
Current self-service retail systems face challenges in detecting commodity shortages and misplacements on shelves, as existing sensor technologies are demanding, have limited service life, and require additional infrastructure like RFID tags.
Innovation Solution
A commodity detection terminal that uses image segmentation and deep learning-based methods to determine the position and quantity of commodities on shelves by comparing images with preset conditions, employing a single shot multibox detector (SSD) algorithm and color channel processing to identify misplacements and shortages without additional sensors or tags.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor technologies (gravity sensors, RFID tags) are used for commodity detection, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical sensors (gravity sensors, RFID tags) with an optical detection system using cameras and image processing algorithms. The commodity detection terminal captures images of the shelf and uses computer vision technology to identify commodity positions, quantities, and anomalies, eliminating the need for complex sensor infrastructure while maintaining detection capability.
Solution Approach 2:
The system creates a digital copy (image) of the physical shelf and commodity arrangement, then analyzes this copy through image processing to detect commodity status. This approach allows detection without physically interacting with or attaching sensors to the commodities, reducing system complexity.
2Reliability
If sensor technologies are deployed on shelves, then commodity detection is enabled, but service life is limited and operational requirements are demanding
Solution Approach 1:
The patent substitutes fragile electronic sensors with a robust optical system. Cameras and image processing algorithms have no moving parts and are not subject to the same wear and environmental constraints as gravity sensors, thereby extending service life and reducing operational demands while improving reliability.
3Measurement precision
If additional infrastructure (RFID tags, sensors) is added to shelves, then detection accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system replaces the need for installing and managing physical infrastructure (RFID tags, sensors) with a non-contact optical detection method. The camera-based system requires no modification to existing shelves or commodities, making the system easier to deploy and operate while maintaining detection accuracy through advanced image processing.
Data Source
AI summary
A commodity detection terminal is disclosed, the commodity detection terminal includes: an image segmentation unit configured to obtain position information of each grid of a goods shelf in an image of the goods shelf based on a first image, which is an image of the goods shelf not placed with commodities; a detector unit configured to obtain a current grid where each commodity is located and a current quantity of each commodity based on a second image, which is a current image of the goods shelf placed with commodities; and a determination unit configured to compare the current grid and the current quantity with a preset grid and a preset quantity of the each commodity, so as to determine whether a status of each commodity satisfies a preset condition. A commodity detection method, an intelligent goods shelf system, a computer device, and a readable medium are also disclosed.