Computer Vision Inspection for Meat Quality Control
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Solution Overview
Problem
Current food processing systems lack efficient methods for real-time detection of foreign objects, trim composition analysis, and defect identification in meat products, leading to potential quality control issues and increased rework rates.
Innovation Solution
A smart manufacturing system utilizing computer vision systems to capture and analyze image data from conveyor tables, detecting foreign objects, trim composition, and packaging defects, and activating diverter gates to divert defective products for repackaging, while computing rework rates and raising alerts for threshold exceedance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision systems are implemented for real-time detection of foreign objects and defects, then quality control and measurement precision are improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces manual inspection methods with computer vision systems that use cameras and image processing algorithms to detect foreign objects, trim composition, and packaging defects. This substitution of mechanical/manual inspection with optical/digital detection enables real-time monitoring while maintaining or improving measurement precision.
Solution Approach 2:
The system automatically detects defects, determines rework rates, and triggers alerts without requiring manual intervention. The computer vision system performs self-service inspection, analysis, and decision-making, reducing the need for human operators while improving detection consistency and precision.
2Reliability
If real-time image analysis is performed to detect defects and foreign objects, then product quality and reliability are improved, but processing time and productivity are affected
Solution Approach 1:
The computer vision system operates continuously along the conveyor table, performing real-time image capture and analysis without interrupting the production flow. This continuous inspection maintains product quality while minimizing impact on productivity compared to batch inspection methods.
Solution Approach 2:
The system performs preliminary detection of foreign objects, trim composition analysis, and packaging defects before products leave the production line. By identifying issues early in the process, defective products can be diverted for repackaging before they reach the customer, maintaining quality without requiring post-production interventions.
3Reliability
If diverter gates are activated to remove defective products, then rework rate decreases, but loss of time and production efficiency are increased
Solution Approach 1:
The system continuously monitors image data, determines rework rates, and provides feedback by activating diverter gates when defect thresholds are exceeded. This closed-loop feedback mechanism ensures that defective products are identified and diverted in real-time, reducing rework rates while minimizing production time loss through automated decision-making.
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
Enhances quality control by enabling real-time detection and diversion of defective products, reducing rework rates, and providing actionable insights for improving production efficiency and product consistency.
Implementation Method 1
receiving, by a controller, image data captured from a product traveling on a conveyor table in a food processing facility, determining, by the controller, at least one of a presence of a foreign object embedded within the product, a trim composition of the product, or an amount of meat on the product based on variations in texture and/or color identified from the image data
Implementation Method 2
receiving, by a controller, image data captured from a vacuum sealed package moving on a conveyor table in a food processing facility, determining, by the controller, a defect inside the vacuum sealed package based on the image data
Data Source
AI summary
A system and method include a first computer vision system to capture first image data from a product travelling on a first conveyor table in a food processing facility and a second computer vision system to capture second image data from a person working at the first conveyor table to detect a condition associated with the product from the first image data based at least on a variation in texture and/or color in the first image data, detect an actual cycle time associated with the person from the second image data based at least on body positions identified from the second image data, and take action based on the condition and the cycle time.