Contactless Sales Object Recognition via Image Processing
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Solution Overview
Problem
Conventional contactless sales systems rely on scanners and specific product identifiers like QR codes or barcodes, which can lead to delays, incorrect product identification, and inaccurate inventory management due to the need for precise orientation and potential wear and tear on products.
Innovation Solution
A contactless sales system that uses cameras and image processing to recognize objects based on their plain appearance, eliminating the need for additional QR codes and scanners. The system captures images from multiple angles, crops them to focus on the product, and uses machine learning models to identify and associate products with their respective metadata, such as SKU numbers and item names.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scanners and specific product identifiers like QR codes or barcodes are used, then product identification can be achieved, but delays and errors occur due to the need for precise orientation and potential wear and tear
Solution Approach 1:
The patent replaces the mechanical scanner system with an optical camera-based image processing system. Instead of using physical scanners that require precise orientation and contact with barcodes/QR codes, the system uses cameras to capture images from multiple angles and employs machine learning models to identify products based on their visual appearance, thereby eliminating the need for precise orientation and reducing identification delays
Solution Approach 2:
The patent creates digital copies (images) of products from multiple angles and uses these copies for identification purposes. The machine learning model learns from these image copies to recognize products, eliminating the need for physical interaction with barcodes or QR codes and allowing identification even when products are worn or damaged
2Extent of automation
If scanners and specific product identifiers are used, then inventory tracking can be performed, but the system complexity increases due to specialized hardware requirements
Solution Approach 1:
The patent makes the camera system universal by enabling it to perform multiple functions: capturing images for product identification, tracking inventory, and supporting various product types without requiring specialized scanners or identifiers. The machine learning model can recognize diverse products based on their visual characteristics, eliminating the need for different scanning hardware for different product types
Solution Approach 2:
The patent extracts the identification function from specialized scanning hardware and transfers it to a general-purpose camera system combined with software-based image processing and machine learning, thereby simplifying the hardware requirements while maintaining automated inventory tracking capability
Data Source
AI summary
Systems and methods are provided for adding a new item to a contactless sales system. The system can receive a plurality of images containing an object of interest located in virtual zones of the image. Each image can be cropped to contain the object of interest and associated metadata including localized zone and object information, such as SKU or name. The system can sort each image to a plurality of asset bins, wherein each asset bin corresponds to one of the one or more objects of interest. These asset bins can be added to a repository.