Material Cage Stacking Detection Using Dual Image Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In traditional warehousing scenarios, determining whether material cages can be stacked requires manual inspection by the driver, reducing efficiency.
Innovation Solution
A method and system using image recognition models to automatically detect and analyze stacking apparatuses of material cages, determining their location and feature information to assess stackability without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual verification by drivers is used to determine material cage stacking, then the stacking determination can be made, but the efficiency of the stacking process is reduced
Solution Approach 1:
The patent replaces the manual mechanical verification system with an automated image recognition system. The image obtaining device captures images of material cages, and the processing device uses detection models to automatically determine stacking eligibility, eliminating the need for drivers to manually inspect and extend their heads from the forklift.
Solution Approach 2:
The patent creates a digital copy (image) of the material cage stacking scene and analyzes this copy to determine stacking eligibility. The image serves as a representation of the physical scene, allowing automated processing without direct human intervention in the inspection process.
2Reliability
If drivers manually check material cage status, then stacking decisions can be made, but the driver's attention and safety are compromised
Solution Approach 1:
The patent replaces the driver's manual inspection process with an automated image processing system. The detection model analyzes images to determine stacking eligibility, removing the requirement for drivers to physically inspect material cages and extending their heads from the forklift, thereby improving safety.
Solution Approach 2:
The patent introduces an intermediary system (image obtaining device and processing device) between the driver and the material cages. This intermediary automatically performs the inspection function, allowing the driver to focus on operating the forklift without compromising safety.
3Measurement precision
If multiple detection models are used for comprehensive stacking analysis, then stacking determination accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the stacking determination task into multiple detection models, each responsible for specific aspects of analysis. The first detection model identifies stacking apparatuses and their locations, while the second detection model extracts feature information. This segmentation allows comprehensive analysis while maintaining manageable complexity through functional decomposition.
Solution Approach 2:
The patent employs multiple detection models that can be trained for different purposes but work together in a unified system. The models share common input (images) and processing frameworks, allowing them to perform specialized functions while reducing overall system complexity through reusability and integration.
Data Source
AI summary
A method for determining material-cage stacking, a computer device, and a storage medium are provided. The method includes the following. A material-cage image is obtained by photographing a first stacking apparatus of a first material cage and a second stacking apparatus of a second material cage. The stacking apparatuses of the two material cages in the material-cage image can be recognized respectively with two detection models. The first stacking result is obtained by obtaining location information of the stacking apparatuses of the two material cages with the first detection model, and the second stacking result is obtained with the second detection model.