Multispectral Horizon Slope Analysis for Atmospheric Compensation
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Solution Overview
Problem
Atmospheric conditions interfere with the accuracy of object detection in threat detection systems, as these systems lack atmospheric sensors to account for variations in visibility, leading to false detections and missed threats.
Innovation Solution
A method and system using passive imaging sensors to collect multispectral intensity data from pixels surrounding the horizon, averaging intensity values, calculating slopes, and determining atmospheric conditions by comparing the differences between slopes above and below the horizon to set appropriate intensity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intensity thresholds are calibrated without atmospheric condition data, then the system operates simpler, but detection accuracy deteriorates due to false detections and missed threats
Solution Approach 1:
The imaging sensor system performs atmospheric condition measurement autonomously using its own captured images. The system processes image data to extract atmospheric information (humidity, temperature, visibility) without requiring external atmospheric sensors or additional measurement devices. This self-service approach enables accurate threshold calibration while avoiding the complexity of integrating separate atmospheric sensing subsystems.
2Reliability
If atmospheric sensors are added to the system, then detection accuracy improves, but device complexity and cost increase
Solution Approach 1:
The imaging sensor serves multiple functions: it detects threats/objects and simultaneously measures atmospheric conditions. By utilizing the same sensor for both object detection and atmospheric parameter extraction, the system achieves accurate detection without adding separate atmospheric sensors. The image data is processed to derive humidity, temperature, and visibility information, making the imaging sensor a multi-functional device.
Solution Approach 2:
The system uses its own imaging sensor to measure atmospheric conditions rather than requiring external atmospheric sensors. This self-service capability allows the system to calibrate intensity thresholds based on real-time atmospheric data from the images themselves, improving detection accuracy without increasing hardware complexity.
3Measurement precision
If intensity thresholds are not adjusted for atmospheric conditions, then the system is easier to operate, but measurement precision deteriorates due to visibility variations
Solution Approach 1:
The system performs preliminary processing of image data to extract atmospheric parameters before final threat detection and identification. By analyzing the image data in advance to determine humidity, temperature, and visibility conditions, the system can pre-calculate appropriate intensity thresholds for accurate object detection under varying atmospheric conditions, maintaining measurement precision without complicating operation.
Solution Approach 2:
The system continuously monitors atmospheric conditions by processing image data and uses this feedback to dynamically adjust intensity thresholds. The extracted atmospheric parameters (humidity, temperature, visibility) provide feedback that enables real-time calibration of detection thresholds, ensuring measurement precision adapts to changing environmental conditions while maintaining ease of operation through automated adjustment.
Data Source
AI summary
A mechanism for determining atmospheric conditions from an image is described. A mechanism for determining atmospheric conditions includes determining an intensity value for pixels above and below the horizon in an image, calculating a slope between the intensity values for the pixels above the horizon, and a slope between the intensity values for the pixels below the horizon, and determining a difference between the slopes to determine the atmospheric conditions.