Flexible Container Particulate Detection via Motion Analysis
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Solution Overview
Problem
Existing automated detection methods for particulate matter in medical solutions are not suitable for flexible containers, as they are designed for specific container types like glass vials and syringes, and often result in false detections due to air bubbles or container features, making them less reliable than manual inspection.
Innovation Solution
A machine vision system that detects and classifies objects in flexible containers by analyzing sequential frames of image data, using motion parameters to differentiate between particles and bubbles, and employing a classifier module to accurately identify objects, thereby reducing false positives and improving detection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection methods designed for glass vials and syringes are applied to flexible containers, then detection speed and reproducibility are improved, but false detection rate increases due to air bubbles and container features
Solution Approach 1:
The system changes the detection parameters by analyzing motion characteristics of objects within the flexible container. Instead of using static image analysis suitable for rigid glass containers, the system tracks object movement over time, calculating motion parameters such as velocity and trajectory to distinguish between particles and air bubbles, thereby reducing false detections while maintaining high detection speed
2Reliability
If manual inspection is used for flexible containers, then false detection rate is reduced, but inspection time and labor requirements increase
Solution Approach 1:
The system replaces manual visual inspection with an automated machine vision system that captures sequential images and analyzes object motion. The machine vision system processes multiple images over time, calculating motion parameters to identify particles, thereby maintaining the reliability of manual inspection while significantly reducing inspection time and labor requirements through automation
3Measurement precision
If automated inspection systems use spin and brake techniques to suspend particulate matter, then detection capability is improved for rigid containers, but adaptability to flexible containers deteriorates
Solution Approach 1:
The system employs dynamic analysis by capturing sequential images of objects within the flexible container and tracking their motion over time. This dynamic approach allows the system to adapt to the flexible container format by analyzing how particles move relative to the container walls and each other, maintaining high detection precision while achieving versatility across different container types without requiring spin and brake techniques
Data Source
AI summary
Systems and methods for automated detection and classification of objects in a fluid of a receptacle such as, for example, a soft-sided receptacle such as a flexible container. The automated detection may include initiating movement of the receptacle to move objects in the fluid contained by the receptacle. Sequential frames of image data may be recorded and processed to identify moving objects in the image data. In turn, at least one motion parameter of the objects may be determined and utilized to classify the object into at least one of a predetermined plurality of object classes. For example, the object classes may at least include a predetermined class corresponding to bubbles and a predetermined class corresponding to particles.