Multi-Camera QA Architecture for Object-Level Inspection Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Advanced quality assurance techniques for configuring and implementing image analysis inspection tools on production lines face challenges in high-throughput environments, where frequent changeovers and increased manufacturing demands complicate the procurement, setup, and monitoring of automated camera-based quality inspection systems.
Innovation Solution
A multi-camera architecture that leverages computer vision and machine learning to analyze images from multiple inspection camera modules, correlating results on an object-by-object basis using timestamps, unique identifiers, and synchronization methods, allowing for remote storage and visualization of inspection results to simplify the implementation and monitoring of quality assurance systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple inspection camera modules are deployed to inspect objects on production lines, then inspection coverage and quality assurance capability are improved, but hardware complexity and system configuration difficulty increase
Solution Approach 1:
The patent combines multiple inspection camera modules into a unified multi-camera system that shares common infrastructure components including synchronized timestamp generation, centralized data correlation logic, and integrated machine learning model execution. This merging approach maintains comprehensive inspection coverage while reducing redundant hardware complexity and simplifying system configuration through shared resources across all camera modules.
2Productivity
If multiple inspection camera modules capture images simultaneously, then inspection speed and throughput are improved, but data correlation and synchronization complexity increase
Solution Approach 1:
The system performs preliminary synchronization by assigning synchronized timestamps to images captured by multiple camera modules at the moment of capture. This preliminary timing action enables subsequent data correlation operations to efficiently match images from different cameras to the same physical object without requiring complex real-time synchronization during the correlation process, thereby maintaining high throughput while simplifying data processing complexity.
3Measurement precision
If machine learning models are used for image analysis inspection, then inspection accuracy and defect detection capability are improved, but computational requirements and processing time increase
Solution Approach 1:
The patent segments the machine learning analysis process by executing different machine learning models on different camera modules in parallel, with each model specialized for detecting specific defect types or inspecting particular regions of interest. This segmentation enables simultaneous processing of multiple image streams without sequential bottlenecks, maintaining high detection accuracy while reducing overall processing time through concurrent execution of specialized analysis tasks.
4Measurement precision
If synchronized timestamps are assigned to images from multiple cameras, then object correlation accuracy is improved, but clock synchronization complexity increase
Solution Approach 1:
The system introduces a centralized timestamp server as an intermediary that provides synchronized time references to all camera modules. This intermediary component generates and distributes synchronized timestamps to multiple cameras, enabling accurate temporal correlation of images from different sources without requiring complex peer-to-peer synchronization protocols between cameras themselves, thereby maintaining correlation accuracy while simplifying the synchronization architecture.
Data Source
AI summary
Data is received that includes a feed of images of a plurality of objects passing in front of each of a plurality of inspection camera modules forming part of each of a plurality of stations. The stations can together form part of a quality assurance inspection system. The objects when combined or assembled, can form a product. The received data derived from each inspection camera module can be separately analyzed using at least one image analysis inspection tool. The analyzing can include visually detecting a unique identifier for each object. The images are transmitted with results from the inspection camera modules along with the unique identifiers to a cloud-based server to correlate results from the analyzing for each inspection camera module on an product-by-product basis. Access to the correlated results can be provided to a consuming application or process via the cloud-based server.