Multispectral Object Recognition for Similar-Looking Object Detection
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Solution Overview
Problem
Existing object recognition technologies rely solely on specific types of data, such as RGB images, which can be fooled by mannequins with similar appearances, leading to security issues and reduced accuracy in distinguishing objects with similar components or appearances.
Innovation Solution
An object recognition apparatus that combines multiple types of spectrum data, including Raman, fluorescence, and visible spectra, along with image data, using pattern recognition algorithms to enhance accuracy and security by leveraging different data characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only RGB images are used for object recognition, then the system is simple and fast, but the accuracy is low and security issues arise due to mannequins with similar appearances
Solution Approach 1:
The patent combines multiple types of spectral data (Raman, fluorescence, visible spectra) with image data to create a comprehensive object recognition system. This merging of different data types enables accurate differentiation between objects with similar appearances, such as real objects versus mannequins, thereby resolving the contradiction between maintaining system simplicity and improving recognition accuracy.
Solution Approach 2:
The patent transitions from two-dimensional RGB image data to multi-dimensional spectral data by incorporating Raman, fluorescence, and visible spectra. This dimensional expansion provides additional characteristics for object identification, enabling the system to distinguish objects that appear similar in standard images but have different spectral signatures.
2Reliability
If multiple types of spectrum data are combined, then the accuracy of object recognition increases, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent employs a single illumination device that can generate multiple types of light (first and second illumination light with different characteristics) to excite various spectral responses from the same object. This multi-functional approach allows the system to collect Raman, fluorescence, and visible spectral data using one device, reducing overall system complexity while maintaining high reliability through multi-modal data fusion.
3Measurement precision
If multiple pattern recognition algorithms are used, then the recognition accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies multiple pattern recognition algorithms to different aspects of the spectral and image data, rather than applying all algorithms to all data. This selective application of algorithms processes only the relevant portions of multi-dimensional data, reducing computational overhead and processing time while maintaining high identification accuracy through targeted algorithmic analysis.
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 combined use of spectrum and image data increases the accuracy of object recognition, enabling better differentiation between objects with similar components and appearances, and enhances security by reducing the risk of misidentification.
Implementation Method 1
a first type of spectrum data different from the second type of spectrum data from light scattered or emitted from the object using a first spectrometer
Implementation Method 2
a second type of spectrum data different from the first type of spectrum data from light scattered or emitted from the object using a second spectrometer
Implementation Method 3
image data about the object using an image sensor
Data Source
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AI summary
An object recognition apparatus includes a first spectrometer configured to obtain a first type of spectrum data from light scattered, emitted, or reflected from an object; a second spectrometer configured to obtain a second type of spectrum data from the light scattered, emitted, or reflected from the object, the second type of spectrum data being different from the first type of spectrum data; an image sensor configured to obtain image data of the object; and a processor configured to identify the object using data obtained from at least two from among the first spectrometer, the second spectrometer, and the image sensor and using at least two pattern recognition algorithms.