Hyperspectral Image Analysis for Transparent Material Identification
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Solution Overview
Problem
Existing hyperspectral imaging methods struggle with identifying materials in transparent objects due to mixed spectral signatures and low Signal-to-Noise Ratio (SNR), leading to inaccurate material detection.
Innovation Solution
Utilizing hyperspectral imaging combined with machine learning, specifically deep neural networks like CNNs, to spectrally un-mix and classify spectral signatures from transparent objects, enabling accurate material identification even in low-SNR conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hyperspectral imaging is used to identify materials in transparent objects, then material composition can be detected, but the mixed spectral signatures from multiple materials and low Signal-to-Noise Ratio make detection inaccurate
Solution Approach 1:
The patent applies segmentation by dividing the mixed spectral signature into separate component signatures using machine learning algorithms. The neural network segments the combined spectral data from multiple materials into individual spectral contributions, enabling precise identification of each material component within the transparent object despite the mixing effect.
Solution Approach 2:
The patent uses machine learning models as an intermediary between the raw hyperspectral data and material identification. The neural network acts as a mediator that processes the complex mixed spectral signatures, extracting and separating the individual material signatures before final identification, thus resolving the difficulty of direct detection.
2Reliability
If traditional material detection algorithms are used, then processing is simpler, but they cannot overcome the challenge of mixed spectral signatures and low SNR
Solution Approach 1:
The patent replaces traditional mechanical or deterministic signal processing methods with machine learning-based computational approaches. Instead of using conventional filtering or spectral analysis techniques, the system employs neural networks that can adaptively learn and separate mixed spectral signatures, significantly improving detection reliability despite the increased computational complexity.
3Productivity
If hyperspectral data is processed in real-time for rapid sorting, then productivity increases, but data transmission and processing time may increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning models with extensive hyperspectral data before deployment. This preliminary training phase allows the system to learn optimal spectral separation strategies in advance, enabling rapid real-time processing during actual sorting operations without requiring complex computations during the critical sorting timeframe.
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
Achieves high-accuracy material identification in transparent objects, allowing for real-time analysis of up to 100% of the object's materials, suitable for applications requiring rapid sorting and quality control.
Implementation Method 1
illuminating the object with light (for example, full-spectrum light), wherein at least some of the light is reflected by the object; using a hyperspectral imaging sensor to capture, based on the reflected light, one or more hyperspectral images of the object
Implementation Method 2
using the trained machine learning model to spectrally un-mix the hyperspectral data so as to extract one or more spectral signatures from the hyperspectral data
Data Source
AI summary
Hyperspectral imaging is used to identify one or more materials in an object. The object is illuminated with light. At least some of the light is reflected by the object. A hyperspectral imaging sensor captures, based on the reflected light, one or more hyperspectral images of the object, the one or more hyperspectral images include hyperspectral data. The one or more hyperspectral images are input to a trained machine learning model. The trained machine learning model spectrally un-mixes the hyperspectral data so as to extract one or more spectral signatures from the hyperspectral data. Based on the one or more extracted spectral signatures, one or more materials comprised in the object are extracted. Another trained machine learning model is used to detect the shape of the object.