Machine Learning Quality Inspection with Vision Transformers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Advanced quality assurance techniques for high-speed automation systems face challenges in efficiently characterizing objects passing through inspection camera modules, particularly due to the complexity of hardware configurations and the need for frequent changeovers, which complicates the implementation and monitoring of quality inspection systems.
Innovation Solution
The implementation of a system that uses machine learning models, including vision transformers and neural networks, to generate representations of images from inspection camera modules, allowing for software-based triggers, focus adjustment, and multiple camera modules with different focal properties, enabling efficient image analysis and quality assurance without the need for physical hardware changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple camera modules with different focal properties are used to capture images of objects on a production line, then the versatility and quality of image capture is improved, but the hardware complexity and difficulty of configuration increases
Solution Approach 1:
The system employs multiple camera modules with different focal properties (wide-angle and telephoto) that can be selectively activated based on the inspection task requirements. This multi-functional approach allows a single inspection system to handle various object sizes and distances without requiring physical reconfiguration of hardware, thereby improving versatility while managing complexity through software control.
Solution Approach 2:
The system dynamically selects and switches between different camera modules based on real-time inspection needs. The camera module selector component enables the system to adapt its hardware configuration on-the-fly without manual intervention, transforming a static hardware setup into a dynamic, software-controlled system that optimizes image capture quality for different scenarios.
2Ease of operation
If software-based triggers and focus adjustment are implemented instead of physical hardware changes, then the ease of operation and reconfiguration is improved, but the computational resources and processing requirements increase
Solution Approach 1:
The system replaces manual hardware reconfiguration with software-based control mechanisms. Software triggers replace physical hardware triggers, and software-controlled focus adjustment replaces manual focus rings. This substitution enables rapid reconfiguration through code rather than mechanical adjustment, significantly improving ease of operation while the computational overhead is managed through efficient algorithm design.
3Measurement precision
If machine learning models generate detailed representations of each image, then the measurement precision and quality assurance accuracy is improved, but the processing time and computational cost increases
Solution Approach 1:
The machine learning processing is divided into two stages: first, a vision transformer generates comprehensive image representations; second, multiple specialized neural networks analyze different aspects of the image in parallel. This segmentation of the analysis task allows detailed examination of critical features while maintaining efficient processing throughput, balancing accuracy with speed.
Solution Approach 2:
The system applies full machine learning analysis only to images that require detailed inspection, while using faster, simpler methods for routine images. The ensemble of neural networks focuses computational resources on extracting only the most relevant features for quality assurance, avoiding unnecessary processing of all image data at maximum detail levels.
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. A representation is generated for each image using a first machine learning model. One or more second machine learning models are then used to analyze each image using the corresponding representation. The analyses can be provided to a consuming application or process for quality assurance analysis.