A multi-factor flame identification method suitable for embedded platforms

Abstract

This application discloses a multi-factor flame identification method suitable for embedded platforms, including establishing a fire sample library, which comes from network fire pictures and combustion experiment pictures; respectively acquiring multiple live video frames; extracting each scene respectively One or more quasi-fire areas are obtained from the moving target in the video frame; fire confirmation is performed on one or more quasi-fire areas according to the fire sample library, and it is judged whether there is fire information in one or more quasi-fire areas; The information is divided into fire levels, and corresponding alarm signals are generated according to the divided fire levels. This application processes the obtained live video frames separately, analyzes in real time whether there is fire information that may develop into a fire in the on-site monitoring environment, and confirms the fire again to further judge whether there is really fire information, and also conducts fire rating on the fire information. Analyze and generate alarms concurrently, and identify fire information more accurately.

Description

technical field [0001] The present application relates to the technical field of electronic intelligent fire protection, and in particular, to a multi-factor flame identification method suitable for embedded platforms. Background technique [0002] At present, there are three mainstream methods of fire identification. The first one uses traditional fire detection sensors to detect fire information, which generally has the disadvantage of long detection time and low accuracy. The second is to use image recognition to detect fire information, that is, to use traditional digital image processing methods to manually set the feature dimensions of fire for fire identification, that is, to manually design multiple representative features to represent fire information. However, due to the limited characteristics of artificially representing fires, fire information in different scenes or backgrounds cannot be expressed normally, and there are generally disadvantages of high misjudgme...

AI Technical Summary

Problems solved by technology

[0002] At present, there are three mainstream methods for fire identification. The first method uses traditional fire detection sensors to detect fire information, which generally has the disadvantage of long detection time and low accuracy.

The second is to use image recognition to detect fire information, that is, to use traditional digital image processing methods to manually set the feature dimension of fire for fire identification, that is, to manually design multiple representative features to represent fire information. However, due to the limited characteristics of artificial fire representation, fire information in different scenes or different backgrounds cannot be expressed normally, and there are generally disadvantages of high misjudgment rate and low robustness.

like figure 1 As shown, it is a diagram of a traditional fire identification system. When a general-purpose deep learning method is used for fire identification, the on-site video collected by multiple video acquisition terminals is transmitted to the background server through the switch, and the background server performs centralized calculation, resulting in calculation Huge amount

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