Thermal Sensor Emissivity Compensation via Dual-Image Overlay
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Solution Overview
Problem
Typical IR camera sensor devices fail to accurately measure the temperature of target objects due to their inability to account for the specific emissivity of the object's surface, leading to inaccurate temperature conversions.
Innovation Solution
A method and sensor device that process thermal image data by using a neural network to detect objects of interest, identify their surface characteristics, and apply respective emissivity-based conversion functions to accurately convert pixel values to temperature values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a predetermined conversion function with fixed emissivity value is used for temperature conversion, then the device complexity is reduced and operation is simplified, but the measurement precision deteriorates due to inability to account for actual surface emissivity
Solution Approach 1:
The system performs preliminary detection of surface characteristics (shiny, painted, rusty, etc.) before temperature conversion, and pre-selects appropriate emissivity values from stored lookup tables based on the detected surface type. This preliminary classification enables accurate temperature measurement without real-time complex calculations.
Solution Approach 2:
The system uses its own visible light imaging capability to detect surface characteristics of the target object, and automatically selects appropriate emissivity values without requiring external input or manual intervention. The sensor device serves itself by utilizing its dual imaging modes (visible and thermal) for automatic emissivity compensation.
2Measurement precision
If emissivity-based conversion functions are applied to different objects, then the measurement precision improves, but the device complexity increases due to need for multiple conversion functions and emissivity determination
Solution Approach 1:
The system applies different emissivity values to different regions of the thermal image based on local surface characteristics detected in the visible light image. Each detected object or region receives a customized emissivity value appropriate to its specific surface properties, enabling locally optimized temperature measurement accuracy.
Solution Approach 2:
The system dynamically changes the emissivity parameter based on detected surface characteristics. By identifying surface types (shiny metal, painted metal, rusty metal, etc.) and selecting corresponding emissivity values from lookup tables, the system adapts the conversion parameter to match actual physical conditions of different objects.
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 enables accurate temperature measurements that take into account the emissivity of the objects, improving the precision of thermal condition monitoring in sensor devices.
Implementation Method 1
an infrared (IR) camera sensor device may be positioned near a target object and may detect the infrared radiation emitted from the target object
Implementation Method 2
The second image capturing thermal distribution across the scene
Data Source
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AI summary
A first camera captures a first image of a scene, and a second camera captures a second image capturing thermal distribution across the scene. The first image is processed to detect one or more objects of interest in the scene and to identify respective surface characteristics of the objects. The first image is overlaid with the second image to identify regions of interest in the second image corresponding to the objects of interest in the first image. Values of pixels that belong to respective regions of interest in the second image are converted to temperature values using respective conversion functions that reflect respective emissivity values determined by the identified respective surface characteristics of the corresponding objects of interest in the first image. The temperature values are analyzed to monitor thermal conditions of the one or more objects of interest in the scene.