Image-Based Inventory Cart with Automated Shelf Detection
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Solution Overview
Problem
Current inventory systems lack location-specific inventory knowledge and require human input for image filtering and shelf analysis, leading to inefficiencies and increased labor costs.
Innovation Solution
An image-based inventory system using a cart-mounted optical imaging device and computing system that captures and processes images to automatically detect and track inventory quantities and locations, employing machine learning for image quality assessment and object identification, reducing the need for manual scanning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual image filtering and shelf analysis are used, then inventory tracking can be performed, but labor costs increase and efficiency decreases
Solution Approach 1:
The system enables automated inventory tracking where the imaging device and processing system perform the work independently without human intervention. The cart moves through shelves capturing images, and the processing system automatically filters, analyzes, and extracts inventory data, making the system self-sufficient and eliminating manual labor requirements
Solution Approach 2:
Manual mechanical processes of image filtering and shelf analysis are replaced with automated computational systems. The processing system uses algorithms to automatically filter images, detect shelves, identify items, and extract inventory information, substituting human cognitive and manual work with automated digital processing
2Extent of automation
If automated image processing is implemented, then labor requirements are reduced, but system complexity increases
Solution Approach 1:
The processing system is designed to perform multiple functions within a single integrated platform: image quality assessment, shelf detection, item identification, and inventory data extraction. This multi-functional approach consolidates what could be separate complex systems into one unified solution, managing complexity through functional integration
Solution Approach 2:
The automated processing system is divided into distinct functional modules: an image quality assessment module that filters suitable images, a shelf detection module that identifies shelving structures, and an item identification module that extracts inventory data. This segmentation allows each module to be optimized independently while working together as a coordinated system
3Loss of information
If comprehensive inventory data is collected, then location-specific knowledge is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary filtering of images based on quality criteria before detailed analysis. The image quality assessment module pre-screens captured images to identify only those suitable for inventory analysis, eliminating the need to process all captured images in detail and reducing overall processing complexity while maintaining complete inventory data collection
Data Source
AI summary
A system for image-based inventory determination including a cart. The cart includes a camera and a cart computing system in communication with the camera. The cart computing system includes a cart processor and a cart memory storing instructions that, when executed by the cart processor, cause the cart computing system to capture and store a set of images from the camera; process the set of images to identify an image of the set of images that displays a section of shelving having both a left vertical beam and a right vertical beam; and transmit the identified image to a remote server.