Fire Detection via Smoke Density Classification
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Solution Overview
Problem
Existing smoke detection systems often generate false alarms due to low smoke density from sources like steam or dust, which can reduce the robustness and speed of fire detection.
Innovation Solution
A fire detection method using a machine learning system to analyze image data from a camera, where a warning is only issued if the expected smoke density exceeds a predetermined threshold, thereby distinguishing between actual smoke and false indicators like steam or dust.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If smoke detection is performed using traditional methods, then detection speed is improved, but false alarms increase due to low smoke density from steam or dust
Solution Approach 1:
The patent changes the detection parameter from binary smoke presence to continuous smoke density classification. The machine learning system evaluates smoke density as a continuous parameter and compares it against threshold values, allowing the system to distinguish between low-density false alarms (steam, dust) and high-density actual smoke, thereby reducing false alarms while maintaining detection speed
Solution Approach 2:
The patent replaces traditional mechanical/optical smoke detection mechanisms with a machine learning-based image analysis system. The neural network processes image data to classify smoke density, substituting physical detection thresholds with intelligent algorithmic evaluation, which enables both rapid detection and reliable discrimination between smoke and non-smoke particles
2Measurement precision
If smoke density threshold is lowered to detect all smoke, then detection sensitivity is improved, but false alarms from steam and dust increase
Solution Approach 1:
The system transforms the detection approach by introducing multiple smoke density classes (first class with lower threshold, second class with higher threshold) instead of a single threshold. This parameter differentiation allows the system to detect even low-density smoke in the first class while using the higher second-class threshold to filter out false alarms, achieving both sensitivity and reliability
Solution Approach 2:
The patent segments the smoke detection range into multiple density classes. The first smoke density class captures low-density smoke with higher sensitivity, while the second smoke density class provides a higher threshold for confirmation. This segmentation allows the system to maintain high detection sensitivity without sacrificing reliability, as false alarms are filtered out by the multi-class evaluation
3Reliability
If machine learning classification is implemented to classify smoke density, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex multi-sensor physical detection systems with a machine learning-based image processing system. The neural network, trained on smoke and non-smoke image data, performs classification by analyzing pixel characteristics in captured images. This substitution achieves high detection accuracy through intelligent pattern recognition while avoiding the complexity of multiple physical sensors and their integration
Solution Approach 2:
The system uses image copies (digital representations) of the smoke scene instead of direct physical measurement. The machine learning model analyzes copied visual information from camera images to determine smoke density classes, replacing complex physical detection mechanisms with simpler optical copying and digital processing, thereby improving accuracy without proportionally increasing system complexity
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 effectively reduces false alarms by classifying smoke density accurately, enhancing the robustness and speed of fire detection while improving the reliability of the results.
Implementation Method 1
derive a change in smoke density from a change in the color of the image pixels in the selected image area
Implementation Method 2
a change in smoke density leads to a change in the transparency of the smoke relative to the background and thus to a color change in the area between the background and the smoke color
Data Source
Figure 1
Figure 2a~2c
Figure 3
AI summary
The invention relates to methods for fire detection by means of an evaluation of a scene (12) captured by a camera (10) via a machine learning system, characterized in that in the event that a fire is suspected based on a first evaluation of the scene (12), a fire warning is only issued if an expected value for the smoke density and/or a change in smoke density exceeds a predetermined expected value for the smoke density.