Optical Inspection System for Meat Foreign Object Detection
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Solution Overview
Problem
Conventional inspection systems, particularly in meat processing, fail to detect non-XR/MD detectable foreign objects and cannot effectively identify contaminants when meat is not being conveyed, leading to inefficiencies and increased costs due to reliance on human inspectors who are limited by visual inspection and prone to fatigue.
Innovation Solution
An inspection system with a background having specific electromagnetic radiation properties matched to the material being processed, using an image capturing device to detect foreign objects by subtracting background radiation from material radiation, enabling the identification of contaminants regardless of their position within the meat or conveyor system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional XR or MD techniques are used for inspection, then a wide range of contaminants can be detected, but non-XR/MD detectable foreign objects (such as cardboard, plastic, metal objects) cannot be detected
Solution Approach 1:
The inspection system combines multiple detection modalities (X-ray, Mahalanobis Distance analysis, and optical imaging) into a single universal system that can detect both XR/MD detectable contaminants and non-XR/MD detectable foreign objects, making the system adaptable to various types of contaminants without requiring separate inspection systems
Solution Approach 2:
The patent introduces an optical imaging system with specifically designed lighting as an intermediary detection method that complements XR and MD techniques. The lighting system uses multiple light sources with different characteristics to illuminate the material, creating optical contrasts that enable detection of foreign objects invisible to XR or MD alone
2Reliability
If human inspectors perform visual inspection to detect foreign objects, then some contaminants can be identified, but the process is labor intensive and effectiveness is limited by human constraints
Solution Approach 1:
The patent replaces the mechanical human visual inspection system with an automated optical imaging and image processing system. The system uses multiple light sources, cameras, and computer algorithms to automatically detect and classify foreign objects, eliminating the need for human inspectors while maintaining or improving detection accuracy and significantly increasing inspection speed and productivity
Solution Approach 2:
The inspection system performs self-analysis through automated image processing and machine learning algorithms that continuously learn from inspection data. The system automatically identifies foreign objects, classifies them by type, and adjusts detection parameters without human intervention, enabling continuous operation at high speeds while maintaining consistent detection accuracy
3Measurement precision
If human inspectors visually inspect meat, then foreign objects on the surface can be detected, but objects hidden within the meat or under the meat at the interface cannot be seen
Solution Approach 1:
The patent transitions from two-dimensional surface visual inspection to three-dimensional internal inspection by introducing X-ray imaging and optical imaging with multiple light sources from different angles. These techniques penetrate the material and reveal foreign objects hidden within the bulk or at interfaces, adding depth dimensionality to the inspection process without requiring physically disassembling or cutting the material
4Reliability
If conventional inspection systems are used, then they can detect contaminants in meat flow, but they cannot detect foreign objects being conveyed through the system in the absence of meat
Solution Approach 1:
The inspection system performs preliminary detection of foreign objects on the conveyor belt before meat is loaded. The optical imaging system continuously monitors the conveyor surface and identifies foreign objects in advance, allowing the system to maintain consistent detection capability whether meat is present or absent, and enabling proactive contamination prevention
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
This solution enhances the detection of foreign objects by accurately identifying contaminants within the meat or conveyor system, reducing reliance on human inspectors and improving efficiency by using image processing and machine learning techniques to differentiate between material and foreign objects.
Implementation Method 1
an image capturing device receives electromagnetic radiation (EMR) from the background and from the inspection zone
Implementation Method 2
The background has a background property defined by a background emission, a background absorbance, and a background reflectance
Implementation Method 3
The background has a background property defined by a background emission, a background absorbance, and a background reflectance
Implementation Method 4
The image capturing device is configured to detect a foreign object within material when transported into the inspection zone by deducting the background EMR from the material EMR
Data Source
AI summary
Disclosed is an inspection system having a background positioned adjacent an inspection zone; and an image capturing device configured to receive background electromagnetic radiation (EMR) from the background and from the inspection zone, the inspection zone being configured and arranged to receive material for transport into the inspection zone; wherein the background has a background property defined by a background emission, a background absorbance, and a background reflectance, the background property being matched in EMR to a material EMR of material to be transported into the inspection zone, the material having a material property defined by a material emission, a material absorbance, and a material reflectance; and wherein the image capturing device is configured to detect a foreign object within material when transported into the inspection zone by deducting the background EMR from the material EMR.