Machine Vision Counter for Pharmacy Unit Detection
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Solution Overview
Problem
Current counting systems in pharmacies, such as manual counting, weighing scales, and optical beam pour through systems, face inefficiencies and inaccuracies due to sample-to-sample or batch-to-batch weight variations, lack of defect identification, and require manual interaction, calibration, and are not self-contained for unit detection and management.
Innovation Solution
A machine-vision-based counting system incorporating an illuminated stage, camera, image analyzer, touch-screen display, and communication link for self-contained unit detection and management, enabling accurate and efficient counting of discrete units with integrated control and security features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If optical beam pour through systems are used for counting, then counting speed is improved, but the system requires manual interaction and cannot identify damaged units
Solution Approach 1:
The system performs self-calibration by automatically capturing images of reference weights and computing calibration factors without manual intervention. The apparatus independently identifies units, calculates counts, and adjusts for weight variations without requiring operator input during the counting process.
Solution Approach 2:
The patent replaces manual visual inspection and physical handling with an automated image capture and analysis system. The camera and image processor substitute for human eyes and hands, automatically detecting unit presence, identifying defects, and calculating counts based on image data rather than manual counting.
2Productivity
If weighing scales are used for counting, then counting speed is improved, but accuracy deteriorates due to sample-to-sample weight variations
Solution Approach 1:
The system creates visual copies (images) of the units on the scale and analyzes these copies to determine count. By capturing images of reference weights and unknown samples, the system compares visual patterns rather than relying solely on weight measurements, enabling identification of individual units and detection of fragments or defects that would not affect overall weight proportionally.
Solution Approach 2:
The patent changes the measurement parameter from weight-only to visual characteristics. By analyzing image data such as unit shape, size, and arrangement patterns, the system compensates for weight variations and provides more accurate counting that is insensitive to sample-to-sample weight differences.
3Adaptability or versatility
If machine vision system is implemented, then defect identification capability is improved, but device complexity increases
Solution Approach 1:
The system uses a single camera and image processor to perform multiple functions: counting units, identifying damaged units, detecting fragments, and verifying proper placement. This multi-functional approach consolidates what would otherwise require separate devices into one integrated apparatus, managing complexity while expanding capability.
Solution Approach 2:
The image processor serves as an intermediary that translates raw image data into meaningful information about unit count and quality. This intermediary layer processes visual data to identify patterns, distinguish valid units from defects, and provide structured output, managing the complexity of image analysis through a dedicated processing component.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate, efficient, and automated unit counting, capable of identifying defects and managing contamination, with improved accuracy and reduced manual intervention, enhancing pharmacy operations and other applications.
Implementation Method 1
an image acquisition component configured to detect light having at least one wavelength, wherein the light provides discrimination between a background field and a quantity of imageable units
Data Source
AI summary
A machine-vision-based counter includes an image acquisition component (imager), wherein light provides discrimination between a background field and imageable units located away from the imager. The imager outputs data representing the field and units; an image processor receiving imager data finds countable units therein. An operator interface accepts command inputs and presents count output. A controller manages image acquisition, processor, and operator interface functions. A counting method includes configuring an imager to detect light, directing light from a source to units positioned to be detected by the imager, and directing the light to the imager. The method includes discriminating between a background field and imageable units; providing, as an imager output, data representing the field and units; configuring an image-processor to receive imager data; configuring the processor to interpret the data as counted units on a background field; and configuring an operator interface to present a count result.