Automatic Camera Grouping in Machine Vision Systems
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Solution Overview
Problem
Machine vision systems require manual setup and expertise to group cameras effectively for unified quality assessments, which is time-consuming and inefficient, especially when multiple cameras are needed to capture comprehensive images of a product.
Innovation Solution
An automated system that uses AI modules to identify and group cameras as a unified virtual device, combining their outputs for a unified quality assessment, allowing for either composite or separate image analysis based on machine learning models and user confirmation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual setup by subject matter experts is used to group cameras, then the system can achieve reliable quality assurance, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system enables automatic self-grouping of cameras through AI modules that autonomously identify cameras capturing the same object and group them without human intervention. The system performs self-configuration by analyzing image data, determining object correspondence, and creating virtual device groupings automatically, eliminating the need for manual expert setup while maintaining reliability.
2Area of stationary object
If multiple cameras are used to capture comprehensive product images, then the field of view and resolution are sufficient, but the system complexity increases
Solution Approach 1:
The system merges multiple physical cameras into a single virtual device through automatic grouping. The AI modules identify cameras that capture the same object and combine their outputs into unified image data, presenting them as one logical unit. This reduces operational complexity while maintaining the combined field of view and resolution benefits of multiple cameras.
Solution Approach 2:
The virtual device grouping mechanism provides multi-functionality by allowing the same set of cameras to be automatically grouped for different objects and inspection tasks. The system can dynamically reconfigure camera groupings based on what is being inspected, making the camera system adaptable and universal across different product types and inspection requirements.
3Ease of operation
If individual quality assessments are performed for each camera, then the assessment process is simpler, but the overall quality control effectiveness is reduced
Solution Approach 1:
The system combines image outputs from multiple cameras into unified image data for each object, performing quality assessment on the combined data rather than separate assessments. This maintains operational simplicity by treating the grouped cameras as a single unit while improving quality control effectiveness through comprehensive multi-angle or multi-region inspection of the same object.
Data Source
AI summary
Automatic grouping in machine vision systems is provided via identifying first and second cameras of a plurality of cameras associated with a machine vision system; displaying, in a graphical user interface, a proposal to operate the first and second cameras as a virtual device in performing a job in the machine vision system using the first and second cameras; and in response to receiving confirmation of the proposal: creating the job in the machine vision system; receiving a first analysis image from the first camera and a second analysis image from the second camera; combining the first analysis image and the second analysis image into a combined image; executing the job on the combined image; and rendering an outcome based on an analysis of the combined image according to the job.