Flame Detection Using Controlled Exposure and Black Body Analysis
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Solution Overview
Problem
Current image processing-based flame detection systems suffer from high false positive errors and poor sensitivity due to saturation issues and interference from moving objects and changing illumination, making them inaccurate and costly.
Innovation Solution
A method and system that measure the color and intensity of images with controlled gain and exposure to eliminate saturation, using the emissivity of flames similar to black bodies to detect flames by comparing measured intensities with reference intensities, and employing multiple images to confirm hazard conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If image processing is used for flame detection to reduce cost, then cost effectiveness is improved, but detection accuracy deteriorates due to high false positive rates
Solution Approach 1:
The system changes the parameter of image capture by obtaining multiple images at different exposure settings (at least one unsaturated image). This allows the system to process images with varying intensity ranges, enabling accurate flame detection while avoiding saturation issues that cause false positives, thus maintaining both cost effectiveness and detection accuracy
Solution Approach 2:
The system captures more images than a conventional single-image system, obtaining a plurality of images with different exposure settings. This excessive action of capturing multiple images allows the system to find at least one unsaturated image for accurate analysis, improving reliability without significantly increasing system cost
2Illumination intensity
If camera sensitivity is adjusted to accommodate low light conditions, then sensitivity to low light is improved, but flame detection accuracy deteriorates due to saturation
Solution Approach 1:
The system dynamically adjusts exposure settings by capturing images at different exposure values. This dynamic approach allows the system to adapt to varying light conditions while ensuring at least one image remains unsaturated, maintaining both low light sensitivity and measurement precision for flame detection
Solution Approach 2:
The system changes the exposure parameter across multiple images, capturing at least one image with reduced exposure to prevent saturation. This parameter variation allows the system to maintain sensitivity to low light conditions while preserving the ability to accurately measure flame intensity in unsaturated images
3Productivity
If conventional image processing is used to detect motion and flicker, then detection capability is improved, but false positive rate increases due to moving objects and illumination changes
Solution Approach 1:
The system segments the detection process by first identifying candidate regions through motion and flicker detection, then applying more rigorous analysis (color temperature calculation, intensity comparison) specifically to those regions. This segmentation allows efficient detection while reducing false positives through targeted verification
Solution Approach 2:
The system uses color temperature and intensity ratio as intermediary parameters to verify flame detection. By comparing the measured intensity ratio against the color temperature-based expectation, the system creates an intermediary verification step that distinguishes actual flames from false positive sources like moving objects or illumination changes
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 significantly reduces false positive and negative errors, providing accurate and cost-effective flame detection by utilizing the physical properties of flames, ensuring reliable detection regardless of flame size and illumination conditions.
Implementation Method 1
taking advantage of a physical property of typical flames, that their emissivity is similar to that of a black body
Data Source
Figure 1
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Figure 4A
AI summary
A hazard detection system measures a color and intensity of a portion of an image of a scene. The image is obtained using a known gain and/or exposure such that the image substantially lacks any saturation. A black body brightness temperature and the corresponding block body intensity are determined based on the measured color. A hazard condition, such the presence of a flame, can be detected using a comparison of the measured intensity and the computed intensity. The gain and/or exposure can be selected such that only the pixels of intensity greater than a certain threshold generally saturate in the captured image. Hazard conditions, such as smoke, can be detected using images in which selective saturation is permitted.