Smoke and Fire Recognition via Color, Brightness, and Trained Models
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Solution Overview
Problem
Current smoke or fire detection methods based on heat detection or smoke detection are delayed, as they detect smoke or fire only after the concentration reaches a certain level, allowing the fire to spread widely before detection.
Innovation Solution
A method and device for smoke or fire recognition that acquires visible light and/or infrared images, recognizes suspected regions based on colors and brightness, and inputs these images into a trained model to accurately detect smoke or fire states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If heat detection or smoke detection is used, then the detection method is simple and reliable, but the detection timing is delayed until smoke concentration reaches a certain level
Solution Approach 1:
The patent applies preliminary action by detecting smoke in its early formation stage using color and brightness analysis before it accumulates to detectable concentration levels. The system performs preliminary filtering on monitoring images to identify suspected regions, then uses a trained model to recognize smoke states at very early stages, enabling detection before conventional methods would trigger.
2Reliability
If conventional heat or smoke detection is used, then the device structure is simple, but the fire is detected only after widespread spread
Solution Approach 1:
The patent applies segmentation by dividing the detection process into multiple stages: initial monitoring image acquisition, filtering to identify suspected regions based on color/brightness characteristics, and final recognition using a trained model. This segmented approach enables high reliability detection by analyzing specific visual features rather than relying on a single complex detection mechanism.
Solution Approach 2:
The patent transitions from conventional single-dimension detection (heat or smoke concentration) to multi-dimensional analysis by examining both color characteristics and brightness values in visual images. This dimensional expansion allows detection of smoke in its early visual manifestation before it reaches concentration thresholds detectable by traditional sensors.
3Productivity
If smoke concentration threshold detection is used, then the detection method is straightforward, but early warning capability is reduced
Solution Approach 1:
The system performs preliminary filtering on monitoring images to identify suspected smoke regions based on color and brightness characteristics before full recognition processing. This preliminary action enables the system to focus computational resources on potential smoke areas, maintaining high detection speed while achieving early warning capability by detecting smoke at lower concentration stages.
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 solution enables timely and accurate recognition of smoke or fire, improving the timeliness of detection compared to conventional methods and potentially reducing the severity of fire-related incidents.
Implementation Method 1
acquiring a to-be-recognized image in a smoke or fire monitoring region, the to-be-recognized image including a visible light image and/or an infrared image; recognizing a smoke or fire suspected region in the infrared image based on brightness
Data Source
AI summary
A method and device for smoke or fire recognition, a computer device and a storage medium are disclosed. The method includes: acquiring a to-be-recognized image in a smoke or fire monitoring region; recognizing a smoke or fire suspected region in the to-be-recognized image according to the to-be-recognized image, including recognizing a smoke or fire suspected region in a visible light image on the basis of colors, and recognizing a smoke or fire suspected region in an infrared image on the basis of brightness; and inputting the to-be-recognized image including the smoke or fire suspected region into a preset model, and recognizing a smoke or fire state in the to-be-recognized image according to an output result of the preset model, the preset model being obtained by training based on the visible light image pre-marked with a smoke or fire state or the infrared image pre-marked with a smoke or fire state.