Visual Inspection Image Clustering for Variable Object Set-Up
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Solution Overview
Problem
Existing automated visual inspection systems for production lines are expensive, require expert integration, and struggle with object variability due to perspective distortion and occlusions, leading to false defect identification and laborious human intervention.
Innovation Solution
A system that groups images of objects into clusters based on appearance, creating a broad database for reference, allowing for efficient defect detection and minimizing false positives by comparing new images to similar clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If custom made automated visual inspection systems are used, then defect detection capability is improved, but system cost and complexity increase significantly
Solution Approach 1:
The patent creates a digital twin or virtual model of the inspection environment that can be reused across multiple inspection tasks. Instead of configuring new hardware and software for each inspection, the system copies and adapts the virtual inspection model, significantly reducing setup complexity and cost while maintaining detection capability.
Solution Approach 2:
The inspection system is designed to handle multiple object types and inspection scenarios using a single unified platform. The system can automatically adapt to different products and defect types without requiring custom configuration, making it universally applicable across various manufacturing contexts.
2Measurement precision
If manual adjustment of imaging parameters is performed for each object variation, then inspection accuracy is improved, but time consumption and labor cost increase
Solution Approach 1:
The system pre-configures multiple imaging parameter sets corresponding to different object types and inspection scenarios. When an inspection task begins, the system automatically selects and applies the appropriate pre-configured parameters without requiring manual adjustment, eliminating setup time while maintaining inspection accuracy.
Solution Approach 2:
The inspection system dynamically adapts imaging parameters based on the detected object characteristics. The system automatically adjusts parameters in real-time according to the specific object being inspected, eliminating the need for manual reconfiguration while maintaining optimal inspection accuracy for each object type.
3Reliability
If expert involvement is required for system setup, then inspection reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic self-calibration and self-configuration when deployed. It autonomously identifies object characteristics, selects appropriate inspection parameters, and optimizes imaging settings without requiring expert intervention. This self-service capability maintains inspection reliability while dramatically improving ease of operation.
Solution Approach 2:
The patent replaces manual expert configuration with automated algorithmic processes. Machine learning models and computer vision algorithms automatically perform tasks that previously required expert knowledge, substituting human expertise with intelligent software that is easier to operate and maintain.
4Measurement precision
If frequent human intervention is performed to handle object variability, then false defect detection is reduced, but productivity decreases
Solution Approach 1:
The system maintains continuous automated inspection operation without interruption for manual adjustments. The automated object recognition and parameter adaptation enable uninterrupted inspection flow, maintaining high productivity while reducing false defect detection through consistent algorithmic application.
Solution Approach 2:
The system incorporates automated feedback loops that continuously monitor inspection results and adjust parameters in real-time. When object variability is detected, the system automatically feeds back to adjust imaging and analysis parameters, reducing false defect detection without requiring human intervention and maintaining high inspection throughput.
Data Source
AI summary
Embodiments of the invention provide a system and method for a visual inspection process, in which images of objects on an inspection line are automatically grouped into clusters based on the appearance of the object in the images. The clustered images create a broad database of images that can be used as a reference for inspected items having different appearances, which ensures that all parts and appearances of an inspected object can be identified and inspected, thereby increasing the success of defect detection and substantially minimizing false detection of defects.