Goods Receipt Management Using Facial Recognition and Image Quantification
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Solution Overview
Problem
Current automated warehouse management systems are inefficient and prone to errors in tracking and counting large quantities of goods items, leading to inaccurate stock data and increased costs due to labor-intensive manual processes.
Innovation Solution
A goods receipt management system utilizing facial recognition, image processing, and voice-based interactions to authorize users, calculate the number of goods items, and adjust configuration variables for improved accuracy, reducing human error and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual counting and data entry methods are used, then labor flexibility is maintained, but productivity is extremely low and errors are frequent
Solution Approach 1:
The patent replaces manual mechanical counting and data entry operations with an automated optical-mechanical system consisting of image capture devices, processing units, and configuration files that automatically detect, count, and record goods items, eliminating the need for manual intervention while maintaining system simplicity through rule-based automated decision-making
Solution Approach 2:
The system performs self-service by automatically capturing images of goods items, processing the images to detect and count items, comparing counts with delivery orders, and recording results without requiring manual operation. The configuration files enable the system to self-adjust detection parameters based on different goods types, making the system autonomous and highly productive
2Measurement precision
If laser beam scanners with bar codes are used, then automation is improved, but measurement precision is limited by human errors in bar code reading
Solution Approach 1:
The patent creates optical copies (images) of the goods items and their packaging using image capture devices. These images are then processed to extract counting information, replacing the need for physical bar code scanning. The configuration files store templates and rules that enable accurate identification and counting from image copies, achieving high measurement precision without complex scanning hardware
Solution Approach 2:
The patent introduces configuration files as an intermediary between the image capture device and the counting process. These files contain detection rules, item templates, and comparison criteria that mediate the transformation of raw images into accurate counts, eliminating human error while keeping the system architecture simple and maintainable
3Productivity
If facial recognition and image processing systems are implemented, then productivity and accuracy are improved, but device complexity increases
Solution Approach 1:
The patent segments the goods receipt management system into distinct functional modules: facial recognition for user authentication, image capture for goods documentation, image processing for detection and counting, and database storage for record-keeping. Each module operates independently with defined interfaces, improving productivity through specialized processing while managing complexity through modular architecture
Solution Approach 2:
The system implements multi-functionality by using the same image capture device for both facial recognition (user authentication) and goods item documentation. The processing unit handles both biometric verification and commodity counting tasks, reducing the number of separate devices needed while maintaining high productivity across multiple functions
Data Source
AI summary
A goods receipt management system includes processors coupled to a non-transitory storage device and operable to execute routines that include a face recognition engine to use facial features of a user to recognize the user as an authorised person, a dialogue manager engine to obtain from the authorised person a stated number of goods items being delivered, a quantification engine to receive an image of the goods items and calculate the number of goods items appearing in the image, a comparison engine to compare the calculated number of goods items with the stated number of goods items, and in the event of a substantial match, to record the calculated number of goods items, and a performance improvement engine to use the image of the goods items to re-tune configuration variables, in the event of a mis-match between calculated number of goods items and stated number of goods items.