Camera Image Triggering for Quality Assurance Inspection
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Solution Overview
Problem
Advanced quality assurance techniques for camera-based inspection systems face challenges in simplifying hardware configurations and providing easy setup and monitoring in high-speed automation systems, particularly in manufacturing and supply chain processes, where frequent changeovers and high throughput complicate the implementation of quality inspection systems.
Innovation Solution
A video processing pipeline utilizing containerized image analysis tools with computer vision and machine learning algorithms generates quality assurance metrics, overlay images with color and transparency information, and selectively switches between co-located inspection camera modules with different focal properties to provide enhanced images for quality assurance analysis, allowing for remote configuration and visualization of anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple co-located inspection camera modules with different focal properties are used, then measurement precision and inspection capability are improved, but device complexity increases
Solution Approach 1:
The system employs multiple co-located inspection camera modules with different focal properties (e.g., wide-angle and telephoto lenses) that can be selectively activated based on inspection requirements. This multi-functional approach allows a single inspection system to handle various inspection scenarios without requiring separate hardware configurations for different inspection types.
Solution Approach 2:
The system dynamically switches between different camera modules based on real-time inspection needs and object characteristics. The software-controlled switching mechanism allows the system to adapt its hardware configuration on-the-fly, selecting the appropriate focal length and camera module for each inspection task, thereby optimizing measurement precision without permanent hardware changes.
2Measurement precision
If machine learning algorithms are implemented for quality assurance metrics, then inspection accuracy is improved, but ease of operation deteriorates due to complex setup requirements
Solution Approach 1:
The system performs preliminary actions by pre-processing images and automatically generating overlay visualizations that highlight anomalies. The machine learning models are pre-trained and configured to automatically detect quality assurance metrics, eliminating the need for operators to manually configure complex algorithmic parameters during setup.
Solution Approach 2:
The system introduces an intermediary layer that automatically translates complex machine learning outputs into intuitive visual overlays. This intermediary processing step simplifies the operator's task by presenting processed, easily interpretable results rather than raw algorithmic data, thereby improving ease of operation while maintaining high inspection accuracy.
3Loss of information
If overlay images with color and transparency information are generated, then information completeness is improved, but processing time increases
Solution Approach 1:
The system applies partial action by generating overlay images selectively based on inspection results. Overlay visualizations with color and transparency information are generated only when anomalies or quality metrics require highlighting, rather than processing every single image with full overlay generation. This approach maintains information completeness for critical cases while reducing unnecessary processing time for normal inspections.
Data Source
AI summary
Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Thereafter, it is detected whether there is an object within each image. Based on this detection, images in which each object is detected that meet predefined object representation parameters are identified (on an object-by-object basis, etc.). The identified images are provided to a consuming application or process for quality assurance analysis. Related apparatus, systems, techniques and articles are also described.