Multi-view Food Imaging for Internal Defect Detection
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Solution Overview
Problem
Current food processing systems lack the ability to efficiently detect and separate internal objects within food materials, particularly in solid-to-solid interfaces, due to limitations in existing imaging technologies that provide only two-dimensional information, which is insufficient for precise quality control and processing in the food industry.
Innovation Solution
An imaging system utilizing light sources and cameras with machine learning algorithms to capture and process image data, reconstructing three-dimensional models of internal objects within food materials by analyzing transmittance, interactance, and reflectance imaging data, allowing for the detection of boundaries between different components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional imaging systems are used for rapid screening of food material, then processing speed is maintained, but only two-dimensional surface information is obtained which is insufficient for internal defect detection
Solution Approach 1:
The patent transitions from two-dimensional surface imaging to three-dimensional internal imaging by implementing a volumetric imaging system that captures depth information and reconstructs internal structures of food materials, enabling detection of internal defects while maintaining processing speed through optimized capture and reconstruction algorithms
Solution Approach 2:
The system creates a three-dimensional digital copy or model of the internal structure of food materials through volumetric imaging and reconstruction algorithms, allowing inspection of internal features without physical intervention or slowing down the production line
2Measurement precision
If volumetric imaging technologies such as CT or MRI are used to obtain internal features, then measurement precision is improved, but processing speed decreases making them suitable only for random quality control rather than inline inspection
Solution Approach 1:
The patent implements a optimized volumetric imaging approach that captures essential three-dimensional internal features without requiring complete volumetric reconstruction of the entire food material, achieving sufficient inspection precision with reduced capture and processing time for inline application
Solution Approach 2:
The system adjusts imaging parameters such as light wavelength, penetration depth, and capture resolution to optimize the balance between internal structure visualization quality and processing speed, enabling rapid inline inspection while maintaining adequate measurement precision
3Manufacturing precision
If two-dimensional imaging data is used for quality control, then processing efficiency is maintained, but the ability to detect precise boundaries and geometry of internal objects is insufficient
Solution Approach 1:
The patent adds the third dimension to imaging data by implementing volumetric capture and reconstruction, providing depth information and three-dimensional geometry of internal objects that enables precise boundary detection and separation while managing system complexity through optimized hardware and software integration
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
Enables rapid and non-invasive three-dimensional imaging of food materials, improving the accuracy of quality control and processing by providing detailed internal geometry, which enhances the separation and removal of irregularly shaped objects, thereby increasing processing efficiency and reducing waste.
Implementation Method 1
one material forms an outer layer that allows the partial penetration of an arbitrary light spectrum, and a second material, or inner object, that is of particular interest is at least partially enclosed in the outer layer and allows for a different range of penetration or absorption by the arbitrary light spectrum
Data Source
AI summary
An inline vision-based system used for the inspection and processing of food material and associated imaging methods are disclosed. The system includes a conveyor belt, a transparent plate, and an imaging system, wherein the imaging system includes a light source and at least one camera. The imaging system produces image data from multiple views of light passing through an object on the transparent plate and captured by the camera. The image data corresponds to one of transmittance, interactance, or reflectance image data and is transmitted to a processor. The processor processes the data using machine learning to generate a three dimensional model of the geometry of a portion of material internal to the object so as to determine boundaries of the portion relative to the surrounding material.