Camera Health Monitoring Using Filtered Reference Images
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Solution Overview
Problem
Cameras in harsh environments suffer from performance degradation due to wear and tear, and manual inspection is difficult and risky, especially in hard-to-reach locations.
Innovation Solution
A health monitoring system that applies spatial filters to compare images captured by cameras with reference images to detect quality attributes like focus, obstruction, and field of view changes, without requiring machine learning, and can be integrated into cameras or remote computing platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used to detect camera issues, then detection capability is available, but accessibility difficulty and safety risks increase in hard-to-reach locations
Solution Approach 1:
The camera system performs self-diagnosis by automatically capturing test images and evaluating its own health metrics through image quality analysis, eliminating the need for manual inspection in hard-to-reach locations
Solution Approach 2:
The patent replaces manual mechanical inspection with automated digital image processing and analysis, using computational methods to assess camera health instead of physical examination by operators
2Measurement precision
If machine learning models are used for image analysis, then accuracy improves, but system complexity and computational requirements increase
Solution Approach 1:
The patent transitions from complex machine learning parameter spaces to simpler deterministic image quality metrics, using traditional image processing parameters that are computationally efficient while maintaining adequate accuracy for health monitoring
Solution Approach 2:
The system uses lightweight, computationally inexpensive image analysis algorithms that can be executed quickly on embedded hardware, replacing heavy machine learning models with simpler alternatives sufficient for the specific monitoring task
3Reliability
If multiple quality attributes are monitored, then comprehensive health assessment is achieved, but processing time and computational load increase
Solution Approach 1:
The patent divides comprehensive health monitoring into multiple independent image quality attributes that can be evaluated in parallel, allowing simultaneous assessment of focus, exposure, distortion, and other metrics without sequential processing delays
Solution Approach 2:
The system monitors multiple quality attributes simultaneously using efficient algorithms, performing slightly more analysis than strictly necessary for basic functionality to achieve comprehensive health assessment without excessive processing overhead
Data Source
AI summary
Provided is a system and method that can simultaneously check if a camera is subjected to lack of focus (e.g., blur), movement, obstruction, improper zoom, improper pan, or the like, using a single reference image. The system does not require deep learning or training thus keeping the system lightweight. In one example, the method may include receiving an image of a scene captured by a camera, applying a filter to the image corresponding to an image attribute of the camera to generate filtered image and applying the filter to a reference image of the scene to generate a filtered reference image, respectively, determining that a quality of the image attribute of the camera has degraded based on a comparison of the filtered image to the filtered reference image, and displaying an alert on a user interface with information about the degradation of the quality of the image attribute.