Self-Service Checkout Using Image Recognition and Weight Verification
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Solution Overview
Problem
Existing self-service shopping systems face inefficiencies due to the need for manual code scanning or high-cost RFID tags, which are tedious and costly, and can lead to errors and increased transaction times.
Innovation Solution
A self-service settlement method using image recognition and weight detection technologies to automatically identify and quantify items on a settlement counter, matching the information with preset rules and weights, enabling fast and accurate checkout processing without additional verification tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual code scanning is used for settlement, then commodity identification can be achieved, but transaction time increases and operational efficiency decreases
Solution Approach 1:
The patent replaces the mechanical code scanning process with an image recognition system using deep learning algorithms. The system captures images of commodities on the settlement counter and automatically identifies them through neural network processing, eliminating the need for manual scanning and significantly reducing transaction time while maintaining identification accuracy.
Solution Approach 2:
The system performs preliminary image capture and processing before the actual settlement is completed. By continuously monitoring the settlement counter and pre-identifying commodities as they are placed, the system prepares settlement information in advance, allowing for rapid checkout when the customer reaches the counter.
2Productivity
If RFID tags are used for automatic settlement, then transaction speed improves, but system cost increases significantly
Solution Approach 1:
The patent replaces expensive RFID tags with a cost-effective image recognition approach. Instead of requiring each commodity to have an embedded RFID tag, the system uses standard cameras and deep learning algorithms to identify commodities based on their visual appearance, dramatically reducing hardware costs while maintaining high settlement efficiency.
Solution Approach 2:
The system enables automatic self-service settlement by having the image recognition system independently identify and quantify commodities without requiring additional verification tools or manual intervention. The deep learning model automatically processes images to determine commodity types and quantities, achieving both high efficiency and low cost.
3Measurement precision
If code scanning is used, then commodity identification is possible, but operation complexity increases and user experience deteriorates
Solution Approach 1:
The patent implements a fully automated self-service system where the image recognition technology automatically identifies commodities and generates settlement information without requiring customer action. Customers simply place their items on the settlement counter, and the system handles the entire identification and checkout process, greatly simplifying the user experience while maintaining accurate recognition.
Solution Approach 2:
The system extracts the complex code scanning and identification process from the customer's task entirely. By using image recognition to automatically capture and process commodity information, the system removes the need for customers to manually scan codes or interact with complex interfaces, leaving them with a simple place-and-pay experience.
4Adaptability or versatility
If manual settlement processing is performed, then flexibility is maintained, but productivity decreases and waiting time increases
Solution Approach 1:
The patent replaces manual settlement processing with an automated image recognition and processing system. The deep learning model automatically analyzes captured images to identify commodities, determine quantities, and calculate totals, replacing the manual scanning and data entry processes while maintaining the flexibility to handle various commodity types and settlement scenarios.
Solution Approach 2:
The system performs continuous image capture and processing during the settlement process. Rather than discrete manual operations, the image recognition system continuously monitors the settlement counter, tracking commodities as they are placed and removed, and maintains up-to-date settlement information throughout the transaction, enabling both speed and flexibility.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces transaction time, eliminates the need for RFID tags, and enhances customer satisfaction by providing a fast, accurate, and cost-effective self-service shopping experience, while minimizing errors and operational costs.
Implementation Method 1
obtaining a monitoring image captured by an image capture device, wherein the monitoring image corresponds to commodities to be settled which are placed on a settlement counter
Implementation Method 2
obtaining a first weight corresponding to the commodities to be settled captured by a weight detection device arranged on the settlement counter
Data Source
AI summary
The present disclosure provides a self-service settlement method, apparatus and storage medium, and relates to the technical field of self-service shopping, wherein the method includes: obtaining a monitoring image acquired by an image capture device and corresponding to commodities to be settled which are placed on a settlement counter; obtaining information of the commodities to be settled through image recognition; obtaining a first weight acquired by a weight detection device, obtaining a second weight based on the information of the commodities to be settled, judging whether the information of the commodities to be settled is matched with commodities to be confirmed according to a weight comparison result; and obtaining purchased commodity settlement information under the condition that the information of the commodities to be settled is matched with the commodities to be confirmed, and performing checkout processing.