Guided Multi-Spectral Inspection for Faster mmWave Scanning
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Solution Overview
Problem
There is a trade-off between mmWave imaging performance (speed, accuracy, signal-to-noise ratio, system complexity) and the size of the area of interest, as mmWave imagers have a wider field of view than cameras, necessitating a focus on only the most relevant portion of the scene for optimal results.
Innovation Solution
A guided multi-spectral imaging system that uses a first imaging system in the visible domain to detect regions of interest through machine learning, autonomously steering a second imaging system in a different spectral domain, such as mmWave radar, to capture detailed data on objects obscured by opaque materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mmWave imager scans the entire scene to maintain wide field of view coverage, then the field of view is improved, but the imaging speed and energy consumption deteriorate
Solution Approach 1:
The visible light camera performs preliminary scanning of the entire scene to identify regions of interest before the mmWave imager conducts detailed inspection. This preliminary action allows the mmWave system to focus only on relevant areas, improving imaging speed while maintaining the ability to cover the full field of view through coordinated operation
Solution Approach 2:
The scene is segmented into multiple regions based on visible light detection results. The mmWave imager divides its attention into specific regions of interest rather than scanning the entire scene uniformly, allowing selective high-resolution imaging of target areas while reducing overall scan time and energy consumption
2Measurement precision
If mmWave imager increases scanning resolution to improve measurement precision, then the measurement precision is improved, but the scanning time and energy consumption increase
Solution Approach 1:
The system applies different imaging qualities to different regions of the scene. High spatial resolution mmWave imaging is applied only to identified regions of interest, while other areas receive lower resolution or no imaging. This local quality approach maintains measurement precision for targets while reducing overall scanning time and energy requirements
3Use of energy by moving object
If the system scans only the most relevant portion of the scene to reduce energy consumption, then the energy efficiency is improved, but the field of view coverage deteriorates
Solution Approach 1:
The system combines multiple imaging modalities (visible light camera and mmWave imager) where each sensor type performs its specialized function. The visible light system provides broad field of view coverage for initial detection, while the mmWave system provides detailed inspection of specific regions. Together, they achieve both wide coverage and energy efficiency by leveraging the complementary strengths of different sensing technologies
Data Source
AI summary
An imaging system is provided. A first imaging system captures initial sensor data in a form of visible domain data. A second imaging system captures subsequent sensor data in a form of second domain data, wherein the initial and subsequent sensor data are of different spectral domains. A controller subsystem detects at least one region of interest in real-time by applying a machine learning technique to the visible domain data, localizes at least one object of interest in the at least one region of interest to generate positional data for the at least one object of interest, and autonomously steers a point of focus of the second imaging system to a region of a scene including the object of interest to capture the second domain data responsive to the positional data.