Optical Gas Imaging Plume Detection Using IR Frequency Changes
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Solution Overview
Problem
Existing optical gas imaging (OGI) technologies for detecting natural gas emissions rely heavily on human judgment, leading to issues with replicability, extensibility, and standardization, and hinder automation in emission inspection studies.
Innovation Solution
A frequency-based algorithm that uses a computing device to identify gas plumes by detecting high-frequency infrared (IR) changes in video data, separating pixels corresponding to the gas plume from background objects, and determining the plume's size and probability of detection (POD) without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human judgment is used for plume identification in OGI technology, then flexibility and adaptability are maintained, but replicability, extensibility, and standardization deteriorate
Solution Approach 1:
The patent replaces the mechanical system of human visual inspection and judgment with an automated image processing system that uses algorithms to detect and analyze gas plumes. The system processes OGI video data through computational methods including frame differencing, contour detection, and plume characteristic analysis, eliminating human subjectivity while maintaining detection capability. This substitution enables replicable and standardized emission inspection across different operators and locations.
2Ease of operation
If human judgment is used for plume identification, then complex plume patterns can be interpreted, but automation and productivity deteriorate
Solution Approach 1:
The patent implements a self-service system where the image processing algorithm automatically performs plume detection, characterization, and reporting without requiring human intervention for each inspection. The system independently processes OGI video data, identifies plume pixels through automated thresholding and contour analysis, calculates plume metrics, and generates inspection results. This automation dramatically increases productivity while the algorithm's design ensures it can handle various plume patterns through adaptive processing.
3Reliability
If automated algorithms are implemented for plume detection, then replicability and standardization improve, but algorithm complexity and computational requirements increase
Solution Approach 1:
The patent segments the plume detection process into distinct computational stages: frame preprocessing, plume pixel identification through thresholding, contour detection and filtering, plume metric calculation, and result generation. Each stage handles a specific aspect of the analysis with dedicated algorithms, making the overall complex system manageable and standardized. This modular segmentation allows the system to achieve high standardization while maintaining controlled algorithmic complexity through organized processing steps.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances replicability, extensibility, and standardization of emission inspection studies by providing a quantitative basis for plume detection, enabling automated large-scale testing and reducing reliance on human judgment.
Implementation Method 1
receiving video data that includes frames representative of infrared radiation (IR) within a scene
Data Source
AI summary
A method may include receiving video data that includes frames representative of infrared radiation within a scene. Each of the frames may include pixels The method may also include identifying pixels within the frames that correspond to a gas plume released by a gas source within the scene based on the infrared radiation. In addition, the method may include determining a size of the gas plume within each frame based on the identified pixels.