RGB–Hyperspectral Classification for Camouflaged Object Detection
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Solution Overview
Problem
Existing object recognition techniques using RGB images fail in environments with insufficient contrast, particularly in military settings where objects are camouflaged, leading to inaccurate detection.
Innovation Solution
A computer-implemented method that combines RGB and hyperspectral image classification using multiple classifier models to generate object classifications, incorporating both color and material information, and amalgamates these classifications to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If RGB image classification is used for object detection, then the detection process is simple and fast, but detection accuracy deteriorates in camouflaged environments with insufficient contrast
Solution Approach 1:
The patent combines RGB image data with hyperspectral image data to create a multi-modal classification system. The RGB classifier and hyperspectral classifier work together, merging their respective strengths to achieve both speed and accuracy in object detection, particularly in camouflaged environments where single-modality approaches fail.
2Measurement precision
If hyperspectral image classification is used for object detection, then detection accuracy improves in camouflaged environments, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the classification task into two independent parts: an RGB-based classifier for rapid initial assessment and a hyperspectral-based classifier for detailed material analysis. This segmentation allows the system to leverage hyperspectral data for improved accuracy without requiring the entire system to be complex, as each classifier can be optimized independently for its specific data type.
3Measurement precision
If multiple classifier models are used to amalgamate classifications, then object classification accuracy improves, but computational complexity increases
Solution Approach 1:
The patent introduces an intermediary amalgamation module that combines the outputs of the RGB classifier and hyperspectral classifier. This intermediary component integrates the classifications from both models, allowing the system to benefit from multiple classification perspectives while managing computational complexity through a structured combination approach rather than requiring all models to operate simultaneously at full complexity.
Data Source
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AI summary
The present disclosure relates to a computer-implemented method of classifying an object from a real-world environment in which an entity operates, the computer-implemented method comprises: receiving an image from a camera mounted to the entity and a hyperspectral image from a hyperspectral camera mounted to the entity; obtaining a plurality of object classifications from a plurality of different classifier models, respectively; and generating an object classification by amalgamating the plurality of object classifications, wherein the plurality of different classifier models includes at least one classifier model configured to generate an object classification using the image and at least one classifier model configured to generate an object classification using the hyperspectral image