Object Recognition Warehousing Using AI Model
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Solution Overview
Problem
Traditional warehousing methods require a pre-created barcode database and manual barcode attachment, leading to high costs and time consumption, especially when dealing with physical objects or transparent packages.
Innovation Solution
The use of a continuously trained Object Recognition Model (ORM) for real-time object recognition, which captures images, provides labeling suggestions, and allows for automated storage and retrieval of objects using an image sensor and a pretrained AI model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a barcode database and manual barcode attachment are used for warehousing, then object identification can be achieved, but the system incurs high costs and time consumption
Solution Approach 1:
The patent replaces the mechanical barcode scanning system with an optical image recognition system. Instead of using barcode readers and physical barcodes, the system uses image sensors to capture images of objects and employs an object recognition model (AI/ML) to automatically identify and classify objects, thereby eliminating manual barcode attachment and reducing warehousing time while maintaining identification accuracy
Solution Approach 2:
The patent creates a digital representation (image) of the physical object instead of using a symbolic representation (barcode). The image sensor captures visual information of the object, and the recognition model processes this visual copy to identify the object, eliminating the need for physical barcode labels while preserving identification capability
2Reliability
If barcode readers and databases are deployed for each object type, then object tracking is reliable, but hardware costs and system complexity increase
Solution Approach 1:
The patent implements a universal image recognition system that can identify multiple types of objects using a single AI model, rather than requiring separate barcode readers and databases for each object type. The object recognition model is trained to recognize various object categories (electronics, clothing, groceries, etc.), making the system multi-functional and reducing overall system complexity
Solution Approach 2:
The patent extracts the identification function from physical barcode labels and concentrates it in a centralized AI-based object recognition model. Instead of distributing barcode scanning capability across multiple devices, the system uses a single image sensor combined with an intelligent model that performs all identification tasks, simplifying the hardware architecture
3Measurement precision
If manual barcode scanning and database lookup are performed, then object classification is accurate, but labor costs increase
Solution Approach 1:
The patent enables the system to perform object identification and classification automatically without human intervention. The image sensor captures images, the AI model processes them, and the system automatically determines object categories and attributes, eliminating the need for manual barcode scanning and database queries while maintaining high classification accuracy
Solution Approach 2:
The patent replaces manual operational processes with an automated AI-based system. Instead of workers manually scanning barcodes and looking up information in databases, the system uses image recognition technology to automatically identify, classify, and manage objects, reducing labor requirements and simplifying operations
Data Source
AI summary
Disclosed is an Object Recognition Warehousing Method for STORE or FETCH an object through a pretrained Object Recognition Model (ORM). Image of an object is taken by an image sensor for Object Recognition, and system automatically provides options of classification and labelling A physical object without package is recognized for STORE according to the present invention.