CN111126293A - Flame and smoke abnormal condition detection method and system - Google Patents
Flame and smoke abnormal condition detection method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for detecting abnormal conditions of flame and smoke, which comprises the following steps: collecting original image information of a set area; extracting a flame candidate region; establishing a background block differential model, and extracting smoke candidate areas; and screening the extracted flame candidate region and smoke candidate region, and determining the specific position of the flame or smoke in the image. The invention has the beneficial effects that: identifying a flame area by adopting a color segmentation method, extracting a smoke image motion pixel by utilizing a background block difference model, and judging the characteristic of a smoke shielding object so as to detect the smoke area; the accuracy of flame and smoke detection can be guaranteed, the average detection time is the minimum, and the requirement of fire detection real-time performance can be met.
Description
Technical Field
The invention belongs to the technical field of image monitoring and processing, and particularly relates to a method and a system for detecting abnormal conditions of flame and smoke.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Fires are a common disaster in daily life, and are a disaster caused by combustion that is out of control in time and space. The "regularity" and "universality" of the threat of fire to humans has prompted a dramatic forward development in fire research. With the continuous breakthrough of scientific technology, the fire detection technology is gradually developing towards imaging and intellectualization. The real-time monitoring of the fire disaster can minimize the loss caused by the fire disaster, which is the key research content in the technical field of fire prevention and control. The occurrence of fire appears irregular in time and space, and is an action against human consciousness.
The traditional fire detection technology mainly utilizes a sensor to identify flame and temperature, each sensing point can only detect local space around a distribution control point, the function of the traditional fire detection technology is difficult to play for special occasions such as open space, and the like, and meanwhile, the situation of misinformation or missing report can also occur. Since the above method can sense the occurrence of a fire only when the fire spreads to a certain extent, some loss is inevitably caused. With the continuous development of digital image processing technology in the computer field, image-based fire detection technology is more and more widely applied to the field of fire detection.
The intelligent analysis of the video images is utilized to detect the fire condition, so that the fire source can be observed remotely, more fire information such as the burning time, the fire occurrence position and the fire intensity can be obtained, and an alarm signal can be sent out after the identification is finished. However, the existing image-based fire detection method is based on image processing and machine learning methods, and features extracted manually need to be used as a basis for fire identification, and meanwhile, the machine learning algorithm has good support for small sample problems, cannot exert the advantages of large-scale training samples, is relatively limited in application scenarios, often causes situations of false report or missed report, and is relatively low in identification accuracy.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for detecting abnormal conditions of flame and smoke, which are based on a fire detection method combining color features, wavelet analysis and a convolutional neural network and can realize accurate positioning of fire and smoke in a video.
In some embodiments, the following technical scheme is adopted:
a method of detecting flame and smoke anomalies, comprising:
collecting video sequence images of a set area;
extracting a flame candidate region from a video sequence image;
establishing a background block differential model, judging the characteristics of the smoke shielding object, and extracting a smoke candidate region;
and respectively screening the extracted flame candidate region and smoke candidate region, and determining the specific position of flame and/or smoke in the image.
In other embodiments, the following technical solutions are adopted:
a flame and smoke anomaly detection system comprising:
the device is used for acquiring original image information of a set area;
means for extracting a flame candidate region;
the device is used for establishing a background block differential model, distinguishing the characteristics of the smoke shielding object and extracting a smoke candidate region;
and the device is used for screening the extracted flame candidate region and smoke candidate region and determining the specific position of the flame and/or smoke in the image.
In other embodiments, the following technical solutions are adopted:
the video monitoring equipment adopts the flame and smoke abnormal condition detection method to detect flame and smoke.
In other embodiments, the following technical solutions are adopted:
the inspection robot adopts the flame and smoke abnormal condition detection method to detect flame and smoke.
Compared with the prior art, the invention has the beneficial effects that:
identifying a flame area by adopting a color segmentation method, and distinguishing the characteristics of a smoke shielding object by utilizing a background block differential model so as to detect the smoke area; the accuracy of flame and smoke detection can be ensured, the average detection time is minimum, and the real-time requirement of fire detection can be met;
in order to enhance the real-time performance of the flame smoke detection algorithm, the convolutional neural network model is used for reducing the false detection rate of the algorithm and further improving the accuracy of flame and smoke detection.
Drawings
FIG. 1 is a diagram illustrating the effect of flame detection according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating the smoke detection effect according to a first embodiment of the present invention;
fig. 3 is a diagram of a CNN network structure according to a first embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, a flame and smoke abnormal condition detection method is disclosed, wherein a video sample of a set area is acquired, for the acquired video sample, a candidate area of flame is extracted by using a color segmentation method, a candidate area of smoke is generated by using a background block differential model based on a full convolution neural network, then the candidate area is screened by using a trained CNN model, and the position of flame and smoke appearing in a picture is determined.
Finally, a large number of fire pictures in different scenes are used for testing the method. The test result shows that the method can accurately and quickly detect the positions of flame and smoke from the image or video, and can be practically applied to a fire detection task in a transformer substation scene.
In order to describe the algorithm, in this embodiment, a visible light camera is used to capture a flame video in an artificial simulated fire scene.
The method for detecting the abnormal conditions of the flame and the smoke specifically comprises the following steps:
(1) analyzing the collected flame and smoke videos into images, analyzing the color characteristics of the flame images, and establishing a color segmentation model of a candidate area for generating flame; obtaining a flame candidate region of the current image by using a color segmentation model;
flames typically appear red, and the RGB color model has less computational complexity than other color models. However, HIS and HSV color models are often used in flame image recognition because their way of describing color is more suitable for human perception of objective world color. The embodiment adopts a method of combining an RGB color model and an HSV color model to identify the flame region. Sampling flame images under different scenes, establishing an RGB space flame color characteristic model by analyzing R, G and B mean values of flame areas, and specifically comprising the following steps:
(1-1) since the R channel has a relatively large value and the B channel has a minimum value in most flame regions, the RGB color feature model of the flame in this embodiment is represented by the following formula:
(i)R>＝G>＝B
(ii)R>＝mean(R),G>＝mean(G),B>＝mean(B)
in the above formula, mean (r), mean (g) and mean (b) respectively represent the average values of RGB channels of all pixels in one picture.
(1-2) RGB color space is commonly used in display systems and is not suitable for image segmentation and analysis, and the segmentation of flames by using only an RGB color model generates a large number of false flame regions, and the flame segmentation effect is not ideal, so that a video image needs to be converted into HSV space for extracting the flame regions.
In order to reduce the loss caused by fire, find the fire in time and send out an alarm, it is necessary to detect the flame and smoke in the early stage of the fire occurrence and to extinguish the fire in the bud.
The flame detection proposed by the embodiment aims at the initial stage of fire occurrence, at this time, the flame area is small, and is represented as R > G > B in the RGB space, and is mainly represented as saturation in the HSI color space, that is, the S value satisfies a certain rule. Converting the color model of the smoke image in the RGB space into the HSI space to obtain a characteristic model of the flame image in the HSI color space, wherein the characteristic model is represented by the following formula:
0<＝H<＝60°
S＝1-3×(min(min(r,g),b)/(r+g+b))；
h represents an H component in the image HSI space, S represents an S component in the image HSI space, the number of flame candidate regions can be changed by adjusting the parameters rTh and sTh, and the smaller the ratio of the parameters sTh to rTh, the more flame candidate regions are obtained, and the more false detections are. In order to detect all flame regions, the present embodiment sets rTh to 200 and sTh to 5.
(2) The method comprises the steps of partitioning a smoke image, rapidly identifying the partitioned image by building a small-sized full-convolution neural network, and realizing coarse segmentation of the smoke region by multi-scale scaling of the image, so that a background region and a foreground region are separated, and background modeling is carried out by using background subblocks. Once the background construction is completed, the roughly divided motion area can be finely divided by a background subtraction method to detect a smoke area. And identifying the sub-block image through a full convolution neural network, and if the sub-block image is identified as a non-smoke area, judging the sub-block image as a background. The method comprises the following specific steps:
(2-1) constructing a small-sized full convolution neural network, wherein the network structure comprises 4 convolution layers, 3 activation layers and 1 pooling layer. There is no full connection layer. The last convolutional layer output is 2. The image blocks of 12 × 12 size are input into the network, and the output is a 2-dimensional vector, which represents the probability of whether the image block is smoke or not, respectively.
And (2-2) analyzing the collected smoke video into images and labeling one by one. And then extracting the smoke region according to the label file, and placing the smoke region in one folder, wherein the background of the video image is placed in the other folder. And scaling the intercepted smoke image into a 12 x 12 image block, taking the image block with the confidence coefficient smaller than 0.5 as a difficult sample of smoke, scaling the background image into a 12 x 12 image block, and taking the image block with the confidence coefficient larger than 0.6 as a difficult negative sample. And training the network after the sample is manufactured to obtain a training model.
And (2-3) in order to obtain a background image of the scene, roughly segmenting the smoke image sequence by adopting the fully-convolutional neural network trained in the previous step. Assume that the smoke sequence image size is W × H. Firstly, dividing a sequence image into m × n sub-blocks, then scaling the image to the size of (W/m, H/n), then inputting the scaled image into a full convolution neural network, and finally calculating the probability value of whether the m × n sub-blocks are smoke or not. For example, for the k frame image, the probability value of each small block after division is
i is 1,2, … m, j is 1,2 … n, and the threshold T is set to 0.7. If it is not
The sub-block is determined to be a foreground block, otherwise it is determined to be a background sub-block.
(2-4) background construction and update, and setting the background image to be constructed as B (x, y), similar to the previous image frame processing, the background image is correspondingly divided into m × n sub-blocks, each sub-block being represented as
At the beginning, the gray value of all pixel points of the background image is set to be-1, namely B0(x, y) — 1, indicating that the background is not updated. Using sub-blocks determined as background, based on the result of the coarse segmentation of the image in the video sequence
To construct or update the background image. Specifically, if the corresponding background sub-block is not updated, i.e. its pixel value is-1, the gray value determined as the background sub-block is directly substituted. Otherwise, the background sub-block of the current frame is used for proper updating.
Wherein, 0 is equal to or less than α is equal to or less than 1, which is the proportion of the current frame background sub-block in the background modeling, when α is equal to 1, the background model is not updated, and when α is equal to 0, the gray value of the background sub-block area of the current frame is directly used for replacing the corresponding area in the background model.
And (2-5) extracting a smoke area, subtracting and differentiating the foreground sub-blocks and the corresponding backgrounds to construct a binarization template of the motion area of each foreground sub-block of the kth frame image, merging the sub-blocks of the binarization templates of all the motion areas to obtain a motion target binarization template of the whole kth frame image, and performing AND operation on the template and the original image to extract the smoke area.
The foreground sub-block is an image block only containing smoke, and the background sub-block is an image area not containing smoke.
(3) Establishing a small convolutional neural network model for filtering out false candidate regions, wherein the convolutional neural network model comprises 3 convolutional layers, 3 pooling layers and two full-connection layers, and the size of the model is 391k, as shown in fig. 3;
due to the complexity of the image background, the extracted target candidate region may contain some misdetected image blocks, and the candidate regions of flame and smoke may be screened through the designed CNN classifier to filter out those false candidate regions. The construction process of the CNN network comprises the following steps:
(3-1) building convolutional layers, wherein the sizes of convolution kernels of the first two convolutional layers are 3 multiplied by 3, and the size of convolution kernel of the third convolutional layer is 2 multiplied by 2.
And (3-2) building pooling layers, wherein each convolution layer is followed by one pooling layer and a PReLu activation function. The first two pooling layers used Max pool (3 × 3) and the latter one used Max pool (2 × 2).
And (3-3) building a fully-connected layer, wherein the output characteristic dimension of the first fully-connected layer is 128, and the output characteristic dimension of the 2 nd fully-connected layer is 3, and respectively represents the category predicted values of the input image. The last softmax layer can calculate the probability of the input image block belonging to flame, smoke and background respectively.
(4) Respectively manufacturing flame and smoke samples, setting network parameters, and training a convolutional neural network model by using the manufactured samples; the method specifically comprises the following steps:
and (4-1) making training samples, wherein the training samples I are classified into three types, namely a flame sample, a smoke sample and a background. Firstly, marking flame and smoke areas of a collected fire image by using an image marking tool;
(4-2) randomly cutting image blocks with any size from the fire picture according to the labeling information, and scaling to a size of 24 x 24;
(4-3) the background sample is then randomly cropped a certain number of image blocks from the picture that does not contain flame and smoke and scaled to a size of 24 x 24. The training set contains 6 million images, and the number proportion of the flame sample, the smoke sample and the background sample is 1: 2: 3.
(4-4) training the network, wherein in the training process: 60% of the images were used as training set, 20% of the images were used as validation set, and 20% of the images were used as test set. The CNN network is trained using a random gradient descent (SGD) method, the size of the batch size is 256, and the weights in the network are initialized randomly. The initial learning rate was 0.01 and the momentum was 0.9. Meanwhile, in order to prevent the CNN network from generating an overfitting phenomenon in the training process, a Dropout layer is added behind the two fully-connected layers, and the value of Dropout _ ratio is 0.5. The network iterates a total of 10 ten thousand times during the training process.
(5) And inputting the images of the flame candidate area and the smoke candidate area into the convolutional neural network model trained by the corresponding sample to obtain accurate position information of flame and smoke in the current image.
Because the video data of ideal experiment is difficult to obtain, so gather the sample by oneself and test, concrete step includes:
(5-1) aiming at a flame detection experiment, a flame data set is automatically established, and 3000 flame pictures under different environments and scenes are collected totally, wherein the scenes comprise partial transformer substation flame pictures, forest flame pictures, grassland flame pictures, city flame pictures and the like.
(5-2) tests were performed on 8-segment smoke video for smoke detection experiments. 4 sections of videos contain smoke and are used for testing the recognition accuracy of the algorithm to the smoke, and the other 4 sections of videos do not contain the smoke and are used for testing the false detection rate of the algorithm.
(6) Testing the flame smoke sample collected by the visible light camera in real time according to the color segmentation model, the background block difference model and the Convolutional Neural Network (CNN) model, and checking the detection effect; fig. 1 and fig. 2 respectively show a flame detection effect diagram and a smoke detection effect diagram in different scenes obtained by using the method of the present embodiment, and it can be seen from the diagrams that the method of the present embodiment can accurately identify the positions of flame and smoke in the pictures.
Example two
In one or more embodiments, a flame and smoke anomaly detection system is disclosed, comprising:
means for acquiring video sequence information of a set scene;
the device is used for establishing a color segmentation model and extracting a flame candidate region by analyzing the motion characteristic and the color characteristic of the flame image;
the device is used for establishing a background block differential model, distinguishing the characteristics of the smoke shielding object by using the background block differential model and extracting a smoke candidate region;
and the device is used for screening the extracted flame candidate area and smoke candidate area through a convolutional neural network model and determining the specific position of the flame and/or smoke in the image.
EXAMPLE III
In one or more embodiments, a video surveillance appliance is disclosed that employs the flame and smoke anomaly detection method described in example one for flame and smoke detection.
In other embodiments, inspection robots are disclosed that use the flame and smoke anomaly detection methods described in example one to perform flame and smoke detection.
It should be noted that the above method for detecting abnormal conditions of flame and smoke is also applicable to equipment such as substation image monitoring equipment and inspection unmanned aerial vehicles, or other monitoring or inspection equipment that can be thought of by those skilled in the art, so as to detect flame and smoke.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A method for detecting abnormal conditions of flame and smoke, comprising:
collecting video sequence images of a set area;
extracting a flame candidate region from a video sequence image;
establishing a color segmentation model, judging the characteristics of the smog-sheltered object, and extracting smog candidate areas;
and respectively screening the extracted flame candidate region and smoke candidate region, and determining the specific position of flame and/or smoke in the image.
2. The flame and smoke abnormal condition detection method as claimed in claim 1, wherein RGB space flame color feature model and HSI space flame color feature model are respectively established for video sequence image, and preliminary candidate flame area is screened out by RGB space flame color feature model; converting color components of pixels of the preliminary candidate flame region in an RGB space into an HSI space; and determining a final candidate flame area by using the HSI space color characteristic model.
3. The flame and smoke abnormal condition detection method as claimed in claim 2, wherein the preliminary candidate flame area is screened out by the RGB space flame color feature model, specifically:
separately calculating R, G, B channel components for each pixel in the image;
screening out pixel points meeting the following conditions as a preliminary candidate flame area:
the component value of the R channel is not less than that of the G channel, and the component value of the G channel is not less than that of the G channel; and R, G, B the component values of all three channels are not less than the average value of all pixels in the image in the corresponding channel.
4. A method as claimed in claim 2, wherein the HSI spatial color feature model is used to determine the final candidate flame region, specifically:
converting color components of pixels of the preliminary candidate flame region in an RGB space into an HSI space to respectively obtain H, S, I components of the HSI space;
screening out pixel points meeting the following conditions as a final candidate flame area:
the S component value is greater than the product of the component value of the R channel and the set ratio.
5. The method for detecting abnormal conditions of flames and smoke according to claim 1, wherein a background blocking difference model is established, and smoke candidate areas are extracted, specifically:
partitioning a video image, identifying the partitioned image by building a full-convolution neural network, realizing coarse segmentation of a smoke region by multi-scale scaling of the image, and separating a background region and a foreground region so as to preliminarily determine an initial candidate region of smoke;
and dividing background sub-blocks according to the background area, performing background modeling by using the background sub-blocks, and after the background construction is completed, performing fine segmentation on the initial candidate area of the smog obtained by coarse segmentation by adopting a background subtraction method to finally detect the candidate area of the smog.
6. A method as claimed in claim 5, wherein background modeling is performed by a background sub-block, and then smoke is subdivided by background subtraction, in particular:
selecting the gray value of a certain background sub-block in the video sequence image as the gray value of the initial background model; updating the gray value of the background model according to the proportion of the current frame in the video sequence image in the background model;
and subtracting difference between the extracted smoke initial candidate region and the constructed background image to construct a binarization template of each foreground sub-block, combining all the binarization templates to obtain a binarization template of the whole image, and performing AND operation on the template and the video sequence image to extract a smoke region.
7. The method for detecting abnormal conditions of flame and smoke as claimed in claim 1, wherein a convolutional neural network model is established, the neural network model is trained through a flame training sample and a smoke training sample respectively, and the extracted flame candidate area and smoke candidate area are input into the corresponding trained neural network model to obtain specific position information of flame or smoke in the image.
8. A flame and smoke anomaly detection system, comprising:
means for acquiring video sequence images of a set area;
means for extracting a flame candidate region;
the device is used for establishing a background block differential model, distinguishing the characteristics of the smoke shielding object and extracting a smoke candidate region;
and the device is used for screening the extracted flame candidate region and smoke candidate region and determining the specific position of the flame and/or smoke in the image.
9. A video surveillance apparatus, comprising: -using the flame and smoke anomaly detection method according to any one of claims 1 to 7; alternatively, a flame and smoke anomaly detection system as claimed in claim 8 is included.
10. An inspection robot, comprising: -using the flame and smoke anomaly detection method according to any one of claims 1 to 7; alternatively, a flame and smoke anomaly detection system as claimed in claim 8 is included.
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