Aircraft Imaging Device Real-Time Calibration via ML
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Solution Overview
Problem
Imaging devices in aircraft are sensitive to weather conditions and require real-time calibration to ensure accurate measurements and prevent false detections.
Innovation Solution
A system and method that utilize one or more imaging devices coupled to an aircraft, configured to generate images of calibration targets at different positions, and a machine learning algorithm to determine real-time environmental conditions and adjust calibration parameters accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time calibration is implemented to maintain measurement accuracy in varying weather conditions, then detection performance is improved, but device complexity increases due to additional calibration targets, machine learning algorithms, and real-time processing requirements
Solution Approach 1:
The system performs preliminary calibration by capturing images of calibration targets at multiple known positions before actual detection operations. The machine learning algorithm processes these calibration images in advance to establish environmental condition models and calibration parameters, so that when real detection occurs, the system already has pre-computed correction factors ready to apply, reducing real-time computational burden while maintaining accuracy
Solution Approach 2:
The patent introduces calibration targets with known patterns as intermediary objects between the imaging device and the actual detection targets. These calibration targets serve as mediators that allow the system to indirectly measure and compensate for environmental effects on the imaging device. The machine learning algorithm acts as another intermediary, translating raw calibration images into actionable calibration parameters that correct the imaging device's response to various weather conditions
2Measurement precision
If multiple calibration targets at different positions are used to account for environmental variations, then calibration accuracy is improved, but loss of time increases due to additional image capture and processing steps
Solution Approach 1:
The system implements periodic calibration by capturing images of calibration targets at scheduled intervals or at predetermined checkpoints during operation. Rather than continuously calibrating, the system performs calibration at regular periods or when specific conditions are met (such as entering a new environmental zone), balancing the need for accuracy with time constraints. The machine learning algorithm accelerates each calibration event by rapidly processing multiple calibration images in parallel
Solution Approach 2:
Multiple calibration targets are pre-positioned at different locations along the expected flight or operation path. The system captures calibration images in advance at these predetermined positions and pre-computes calibration parameters before actual detection begins. This preliminary calibration approach ensures accuracy across varying environmental conditions while minimizing real-time calibration time, as the heavy processing work is done beforehand
Data Source
AI summary
A method for determining performance of an imaging device in real-time is disclosed. The method may include, but is not limited to, receiving a first set of images of a first calibration target positioned at a first position from one or more imaging devices, determining a first environmental condition external to the aircraft at the first position based on the received first set of images using a machine learning algorithm; receiving additional set of images of an additional calibration target positioned at an additional position from the one or more imaging devices; determining an additional environmental condition external to the aircraft at the additional position based on the received additional set of images; and determining one or more real-time calibration parameters for the one or more imaging devices based on at least one of the determined first environmental condition and the determined additional environment condition.