Foreign Object Inspection in Medicine Packaging
Ensuring Safety, Compliance, and Quality Control
As a leader in vision inspection technology, we offer an advanced automated foreign object inspection system that utilizes state-of-the-art imaging technology and machine learning algorithms to detect and remove foreign objects with unprecedented accuracy. This solution is designed to integrate seamlessly into production lines, providing real-time inspection that meets stringent industry standards.
3D Depth Mapping and Stereo Vision
3D depth mapping and stereo vision use multiple cameras to capture images from different angles, creating a 3D depth map of objects. This helps differentiate overlapping contaminants or those partially covered by packaging. By analyzing depth and positioning, the system accurately identifies foreign objects, reducing false positives and improving inspection accuracy.
Machine Vision and AI-Based Pattern Recognition
The system uses advanced machine vision and AI-based pattern recognition to detect foreign objects in medicine packaging. By analyzing thousands of pixel data points per image, the AI continuously improves its detection accuracy and adapts to different packaging and contaminants.
High-Speed Inspection
integrates seamlessly into continuous production environments
Foreign object inspection system performs foreign object inspection at speeds of up to several hundred packages per minute, maintaining high accuracy. It integrates smoothly into continuous production, offering real-time feedback and reject mechanisms. By using advanced image analysis and machine learning, it reduces false rejects, minimizing product waste and downtime while boosting production efficiency.
Real-Time Defect Detection and Classification
The system analyzes it in real-time, classifying the detected objects based on predefined defect criteria. For foreign objects, the system uses deep learning models to assess the location, size, and nature of the contaminant, comparing it to known patterns of acceptable packaging. This ensures high accuracy in distinguishing between foreign objects and other packaging-related features (such as glue spots, air bubbles, or product contents).
Size and Shape
The algorithms distinguish objects based on their geometric properties, such as size, edges, and shape.
Color Contrast
Differences in color between the foreign object and the surrounding packaging material help in quick identification.
Surface Texture
Contaminants may have unique surface features, that can be detected by the system’s texture analysis capabilities.
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