Smoke detection method and system based on foreground and background analysis and electronic equipment

A technology of foreground background and detection method, applied in the field of smoke detection, can solve the problems of false negatives and false positives, and achieve the effect of reducing false negatives and false positives, and improving sensitivity and accuracy.

Pending Publication Date: 2020-12-29
WENZHOU UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Aiming at the deficiencies of the prior art, the present invention discloses a smoke detection method, system and electronic equipment based on foreground and background analysis, which are used to solve the problem that the existing smoke detect

Method used

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  • Smoke detection method and system based on foreground and background analysis and electronic equipment
  • Smoke detection method and system based on foreground and background analysis and electronic equipment
  • Smoke detection method and system based on foreground and background analysis and electronic equipment

Examples

Experimental program
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Example Embodiment

[0036]Example 1

[0037]In this embodiment, the smoke detection still uses deep learning, but the adversarial generation technology is added, including three sub-networks: smoke detection network D, smoke removal network G, smoke analysis network S, and 4 difference functions, smoke motion loss LS, Background difference LB, Scene recovery difference LRAnd background legacy LDS ,Such asfigure 1 Shown. Among them, the total network loss, extracting frame I from the video stream from the camerat, Generate foreground image through smoke detection network DGenerate background image through smoke removal network GForeground imageAnd backgroundImage synthesis new scene imageForeground image of current frameGenerate smoke motion loss L through smoke analysis network SS.

[0038]Background image of current frameBackground image with previous frameThe difference isAdd up and leave L for backgroundB,which is:

[0039]

[0040]Foreground image generated by smoke detection network DAnd smoke removal network...

Example Embodiment

[0049]Example 2

[0050]This embodiment discloses a smoke detection network D, which uses a codec network to generate a foreground image from the current frame ItThe network parameter is θD, This process can be described as:

[0051]

[0052]The encoder contains four convolutional layers, which transform the image It into high-dimensional features with a size of 1 / 24 and 512 channels. The decoder uses 4 symmetrical deconvolution layers to convert from high-dimensional features to smoke channel images of the original sizeThe size of the convolution kernel of each layer is 5, the stride is 2, using leaky ReLU and batch normalization operations. The corresponding layer of the decoder and the encoder is connected with a jump layer to store advanced information to ensure that the up-sampled pixels can resolve the smoke with high quality. The network structure is likefigure 2 As shown, the smoke is gradual, and the generated smoke channel image does not have a large gradient, so the smoke detectio...

Example Embodiment

[0055]Example 3

[0056]This embodiment discloses a smoke removal network G, which uses a codec network to obtain information from the current frame ItGenerate background imageThe network parameter is θG, The process is described as

[0057]

[0058]The encoder contains 8 convolutional layers, which transform the input into high-dimensional features of 512 channels, and the decoder uses 8 symmetrical deconvolutional layers to convert the high-dimensional features into original size background imagesIf the encoder keeps reducing the resolution, and then the decoder keeps increasing the resolution, the only way to deal with this will cause the gradient to disappear and the information imbalance of each layer. In fact, the background content of the surveillance camera rarely changes. The interlayer connection allows this part of information to be directly used for generation without the need to go through the calculation process of compression and decompression to avoid the loss of information....

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Abstract

The invention relates to the technical field of smoke detection, in particular to a smoke detection method and system based on foreground and background analysis and electronic equipment, and the method comprises the following steps: S1, actually deploying an application, and obtaining a video stream from a monitoring camera; S2, using a smoke detection network D to generate a foreground image foreach frame of image; S3, extracting smoke features in the smoke through a convolutional neural network C; S4, sending the smoke features in S3 and the smoke features of the first n frames of images to a recurrent neural network R, and generating smoke probability ps; and S5, judging whether the smoke probability ps is greater than a set threshold value or not, further determining scene smoke, andsending out a fire alarm. According to the invention, a scene is decomposed into a background and a smoke foreground, so that the whole process of smoke generation and development can be separated, and a smoke alarm is given out when the early stage is weak, so as to early warn a fire in advance. The method has the advantages that the sensitivity and accuracy of the algorithm are improved, smokesignals are provided as early as possible, and missing report and false report are reduced.

Description

technical field [0001] The invention relates to the technical field of smoke detection, in particular to a smoke detection method, system and electronic equipment based on foreground and background analysis. Background technique [0002] At present, there are roughly two types of methods for smoke detection based on deep learning: (1) The method of extracting static features based on convolutional neural network can avoid the dependence on artificial features in traditional methods, and can automatically obtain high-level features that are difficult to obtain by traditional techniques. (2) The method of extracting dynamic features based on the cyclic neural network can extract the development and changes of the fire from smoldering to erupting, can eliminate the static approximate smoke, and can achieve relatively high accuracy. Rate. There is also a combination of these two methods, using a convolutional neural network to extract the static features of each frame, and then...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06T7/194G08B17/10G08B17/12
CPCG06T7/194G08B17/10G08B17/125G06T2207/20081G06T2207/20084G06V20/40G06V20/52G06N3/045G06F18/214
Inventor 罗胜
Owner WENZHOU UNIVERSITY
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