CN113989516A - Smoke dynamic identification method and related device - Google Patents
Smoke dynamic identification method and related device Download PDFInfo
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Abstract
The embodiment of the application provides a smoke dynamic identification method and a related device, wherein the method comprises the following steps: acquiring a target image of a to-be-detected area by a gray characteristic value method; extracting the features of the target image to obtain feature data of the target image; performing data modeling operation according to the characteristic data to determine smoke state information of the area to be detected; comparing the characteristic data of the target image to determine smoke motion parameters; and determining a first smoke type according to the smoke state information and the smoke motion parameters, so that the accuracy in smoke type judgment can be improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a smoke dynamic identification method and a related device.
Background
With the increasing development of industry, it imposes a great burden on the environment. People pay more and more attention to the protection of the environment, for example, smoke in the environment is identified to judge whether the smoke is industrial smoke, when the smoke is industrial smoke, corresponding processing needs to be performed on the smoke.
Disclosure of Invention
The embodiment of the application provides a smoke dynamic identification method and a related device, which can improve the accuracy in smoke type judgment.
A first aspect of an embodiment of the present application provides a smoke dynamic identification method, where the method includes:
acquiring a target image of a to-be-detected area by a gray characteristic value method;
extracting the features of the target image to obtain feature data of the target image;
performing data modeling operation according to the characteristic data to determine smoke state information of the area to be detected;
comparing the characteristic data of the target image to determine smoke motion parameters;
and determining a first smoke type according to the smoke state information and the smoke motion parameters.
With reference to the first aspect, in a possible implementation manner, the performing data modeling operation according to the characteristic data to determine smoke state information of the to-be-detected region includes:
determining the smoke content in the area to be detected according to the gray value;
and determining the smoke state information according to the smoke content.
With reference to the first aspect, in a possible implementation manner, the determining a smoke motion parameter according to the comparison of the feature data of the target image includes:
acquiring a smoke contour of a smoke region in the feature data in the target image;
and determining the smoke motion parameters according to the change of the smoke contour.
With reference to the first aspect, in one possible implementation manner, the determining the smoke motion parameter according to the smoke profile includes:
acquiring M contour convex areas of the smoke contour;
determining the smoke diffusion direction of each contour convex area in the M contour convex areas according to the M contour convex areas;
acquiring position information of the M contour convex areas in the smoke contour;
determining a first reference smoke movement speed of smoke in each contour convex area in the M contour convex areas according to the position information and the smoke generation points;
determining a second reference smoke movement speed of the smoke in each of the M contour-convex areas according to the contour shape of the M contour-convex areas;
determining a target smoke movement velocity of smoke in each of the M contour-salient regions according to a first reference smoke movement velocity of smoke in each of the M contour-salient regions and a second reference smoke movement velocity of smoke in each of the M contour-salient regions;
and determining the smoke diffusion direction of each of the M contour convex areas and the target smoke movement speed of the smoke in each of the M contour convex areas as the smoke movement parameters.
With reference to the first aspect, in one possible implementation manner, the method further includes:
acquiring morphological parameters of a wind power identifier in the target image;
determining the wind power parameters of the area to be detected according to the morphological parameters;
determining the environmental information of the area to be detected according to the target image;
determining first type correction information according to the wind power parameters and the environment information;
acquiring the illumination intensity of the area to be detected;
determining second type correction information according to the illumination intensity;
determining target correction information according to the first type correction information and the second type correction information;
and correcting the first smoke type according to the target correction information to obtain a second smoke type.
A second aspect of embodiments of the present application provides a smoke dynamic identification apparatus, the apparatus comprising:
the acquisition unit is used for acquiring a target image of a to-be-detected area;
the extraction unit is used for extracting the features of the target image to obtain feature data of the target image;
the first determining unit is used for determining smoke state information of the area to be detected according to the characteristic data;
the second determining unit is used for determining smoke motion parameters according to the target image;
and the third determining unit is used for determining the first smoke type according to the smoke state information and the smoke motion parameters.
With reference to the second aspect, in one possible implementation manner, the feature data includes a gray-scale value, and the first determining unit is configured to:
determining the smoke content in the area to be detected according to the gray value;
and determining the smoke state information according to the smoke content.
With reference to the second aspect, in one possible implementation manner, the second determining unit is configured to:
acquiring a smoke contour of a smoke region in the feature data in the target image;
and determining the smoke motion parameters according to the change of the smoke contour.
With reference to the second aspect, in one possible implementation manner, in the determining the smoke motion parameter according to the smoke profile, the second determining unit is configured to:
acquiring M contour convex areas of the smoke contour;
determining the smoke diffusion direction of each contour convex area in the M contour convex areas according to the M contour convex areas;
acquiring position information of the M contour convex areas in the smoke contour;
determining a first reference smoke movement speed of smoke in each contour convex area in the M contour convex areas according to the position information and the smoke generation points;
determining a second reference smoke movement speed of the smoke in each of the M contour-convex areas according to the contour shape of the M contour-convex areas;
determining a target smoke movement velocity of smoke in each of the M contour-salient regions according to a first reference smoke movement velocity of smoke in each of the M contour-salient regions and a second reference smoke movement velocity of smoke in each of the M contour-salient regions;
and determining the smoke diffusion direction of each of the M contour convex areas and the target smoke movement speed of the smoke in each of the M contour convex areas as the smoke movement parameters.
With reference to the second aspect, in one possible implementation manner, the apparatus is further configured to:
acquiring morphological parameters of a wind power identifier in the target image;
determining the wind power parameters of the area to be detected according to the morphological parameters;
determining the environmental information of the area to be detected according to the target image;
determining first type correction information according to the wind power parameters and the environment information;
acquiring the illumination intensity of the area to be detected;
determining second type correction information according to the illumination intensity;
determining target correction information according to the first type correction information and the second type correction information;
and correcting the first smoke type according to the target correction information to obtain a second smoke type.
A third aspect of the embodiments of the present application provides a terminal, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiments of the present application.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program makes a computer perform part or all of the steps as described in the first aspect of embodiments of the present application.
A fifth aspect of embodiments of the present application provides a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps as described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has at least the following beneficial effects:
the method comprises the steps of obtaining a target image of a region to be detected, extracting features of the target image to obtain feature data of the target image, determining smoke state information of the region to be detected according to the feature data, determining smoke motion parameters according to the target image, and determining a first smoke type according to the smoke state information and the smoke motion parameters.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a smoke dynamic identification method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of another smoke dynamic identification method according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a smoke dynamic identification device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to better understand the dynamic smoke identification method provided in the embodiments of the present application, a brief description is first given below of a scenario in which the dynamic smoke identification method is applied. The smoke dynamic identification method can be applied to electronic equipment, the electronic equipment can be a computer, a tablet computer, a mobile phone and the like, in an industrial production environment, smoke in the environment needs to be identified, for example, in a factory, smoke needs to be detected and identified, and for example, in a factory, exhaust gas is discharged, smoke needs to be detected and identified, so as to judge the type of the smoke and perform corresponding processing, specifically, the electronic device obtains a target image of an area to be detected through a camera, an industrial camera and the like, performs feature extraction on the target image to obtain feature data of the target image, the feature data can be a gray value and the like, the electronic device determines smoke state information of the area to be detected according to the feature data, the electronic device determines smoke motion parameters according to the target image, the electronic device determines the smoke state information and the smoke motion parameters according to the smoke state information and the smoke motion parameters, the first smoke type is determined, so that the smoke state information can be obtained according to the feature data extracted by the features, the first smoke type is determined according to the smoke state information and the smoke motion parameters, and the accuracy of smoke type determination is improved.
Referring to fig. 1, fig. 1 is a schematic flow chart of a smoke dynamic identification method according to an embodiment of the present application. As shown in fig. 1, the method is directed to an electronic device, the method comprising:
101. and acquiring a target image of the area to be detected by a gray characteristic value method.
When the target image is acquired, the target image may be acquired by an industrial camera, and the industrial camera may specifically be: the 6-pin Hirose joint provides power and I/O, comprises 1 path of light-isolation input, 1 path of light-isolation output and 1 path of bidirectional configurable non-isolated I/O, and the industrial camera can output gray images, so that the image acquisition efficiency is improved, and the data processing pressure of an industrial control host is reduced. The gray feature value may be obtained by segmenting the gray features of the acquired image to obtain the target image.
The industrial camera may be fixed to a mobile trolley, aircraft or fixed building in the area to be inspected. Can carry out image acquisition through K industry camera to the area of treating the detection, can all regard as the target image with the image that K industry camera was gathered to carry out the detection of smog type. The K industrial cameras and the industrial control host can realize automatic cross detection without mutual interference. Of course, the detection of the smoke type can also be carried out by acquiring images through 1 industrial camera to obtain a target image.
102. And performing feature extraction on the target image to obtain feature data of the target image.
When feature extraction is performed on a target image, the target image may be processed to obtain a processed image, and feature extraction may be performed on the processed image to obtain feature data.
The method for processing the target image can be as follows: performing optimization processing on the target image twice, which specifically may be:
first image optimization processing: and carrying out target image processing shading correction and image filtering to obtain a first reference image. Specifically, for example, the background segmentation and the target grabbing are performed according to the characteristics of smoke in the metallurgical industry, wherein the edge contour of the smoke is irregular and has a single color tone and a significant difference with the gray level of the surrounding environment.
Second image optimization processing:
and enhancing and image normalizing the first reference image to obtain a second reference image. The method specifically comprises the following steps: and carrying out edge sharpening and gray deepening on the background of the first reference image.
And performing feature extraction on the second reference image to obtain feature data. The characteristic data may comprise grey values. Therefore, when the feature extraction is carried out on the second reference image obtained after the secondary processing is carried out on the target image, the accuracy of background segmentation and target capture can be effectively improved.
103. And performing data modeling operation according to the characteristic data to determine smoke state information of the area to be detected.
When the characteristic data is a gray value, the smoke state information can be determined according to the smoke content determined by the gray value.
104. And comparing the characteristic data of the target image to determine the smoke motion parameters.
The smoke in the target image can be subjected to contour detection, and the smoke motion parameters are determined according to the detected smoke contour. The characteristic data comparison of the target image can be understood as comparing the characteristic data of the target image with the preset characteristic data of the image.
105. And determining a first smoke type according to the smoke state information and the smoke motion parameters.
The first smoke type can be determined according to preset mapping relations among the smoke state information, the smoke motion parameters and the smoke types. The mapping relationship may be set by historical data or empirical values, and may be obtained by training a network model.
In this example, a target image of a to-be-detected area is obtained, feature extraction is performed on the target image, feature data of the target image is obtained, smoke state information of the to-be-detected area is determined according to the feature data, smoke motion parameters are determined according to the target image, and a first smoke type is determined according to the smoke state information and the smoke motion parameters.
In a possible implementation manner, the feature data includes a gray value, and a possible method for performing data modeling operation according to the feature data to determine smoke state information of the region to be detected includes:
a1, determining the smoke content in the area to be detected according to the gray value;
a2, determining the smoke state information according to the smoke content.
The smoke content in the area to be detected can be determined through the mapping relation between the gray value and the smoke content, and specifically, the method can be as follows: mapping the gray value statistic value to the actual smoke content through the smoke change rule and the related function, for example, determining pixel points with gray values lower than or equal to a preset threshold value as smoke pixel points, and determining pixel points with gray values higher than the preset threshold value as background pixel points.
The smoke status information may be determined again from the mapping relationship between the smoke content and the smoke status information. The mapping relationship is set by empirical values or historical data.
In this example, the smoke content is determined through the gray value, and the smoke state information is obtained according to the smoke content, so that the accuracy and efficiency of the smoke state information determination can be improved.
In one possible implementation, a possible method for determining smoke motion parameters according to the comparison of the feature data of the target image includes:
b11, acquiring a smoke contour of the smoke area in the target image;
and B12, determining the smoke motion parameters according to the smoke contour.
The smoke contour can be determined according to the gray value or RGB value of the target image. Specifically, the smoke contour may be determined where the change in the gray value is large. The smoke profile may be determined in other ways, and is not limited in any way.
When determining the smoke motion parameters, the following may be specifically mentioned: and determining smoke motion parameters according to the relevant parameters of the outline convex area of the smoke outline. The smoke motion parameters can comprise the motion direction and the motion speed of smoke.
In this example, the smoke contour of the smoke region in the target image is obtained, and the smoke motion parameter is determined according to the relevant parameter of the contour convex region of the smoke contour, so that the accuracy of determining the smoke motion parameter can be improved.
In one possible implementation manner, another possible method for determining smoke motion parameters according to the comparison of the feature data of the target image includes:
b21, acquiring a smoke contour of a smoke area in the feature data in the target image;
b22, determining the smoke motion parameters according to the change of the smoke profile
For obtaining the smoke contour of the smoke region in the feature data in the target image, reference may be made to the method in step B11 in the foregoing embodiment, and details are not repeated here.
The change of the smoke profile can be characterized by the smoke movement direction and the movement speed of the smoke profile, so that the movement parameters of the smoke are determined according to the movement parameters of the smoke profile.
In one possible implementation, a possible method for determining the smoke motion parameter according to the smoke profile includes:
c1, acquiring M contour convex areas of the smoke contour;
c2, determining the smoke diffusion direction of each contour convex area in the M contour convex areas according to the M contour convex areas;
c3, acquiring the position information of the M contour convex areas in the smoke contour;
c4, determining a first reference smoke movement speed of smoke in each contour convex area in the M contour convex areas according to the position information and the smoke generation points;
c5, determining a second reference smoke movement speed of the smoke in each contour protruding area in the M contour protruding areas according to the contour shape of the M contour protruding areas;
c6, determining a target smoke movement speed of the smoke in each of the M contoured convex areas according to the first reference smoke movement speed of the smoke in each of the M contoured convex areas and the second reference smoke movement speed of the smoke in each of the M contoured convex areas;
c7, the smoke diffusion direction of each of the M contour-convex areas and the target smoke movement speed of the smoke in each of the M contour-convex areas are determined as the smoke movement parameters.
M contour convex areas can be obtained according to the contour change of the smoke contour. For example, the outwardly convex arc-shaped area in the smoke contour is determined as the contour convex area. The outward direction is the outer portion of the area enveloped by the smoke contour.
A tangent to the apex of the silhouette region may be determined, and a direction perpendicular to and outward of the tangent may be determined as the smoke diffusion direction. Of course, the smoke diffusion direction can also be determined by the outward direction perpendicular to the tangent of the contour point at all contour points of the contour convex area.
The position information of the contour convex region may be understood as position information of the vertex of the contour convex region. Specifically, a coordinate system is established, and a vertex of the coordinate system is a point at the lower left corner of the target image. Or position information of each contour point in the contour convex area.
The smoke generation point is the point where smoke is generated, which point is also understood here as the central point of the smoke generation area. The longer the distance between the position indicated by the position information and the smoke generation point is, the smaller the first reference smoke movement speed is; the shorter the distance between the position indicated by the position information and the smoke generation point is, the greater the first reference smoke movement speed is.
The sharper the contour shape of the contour convex area is, the larger the second reference smoke movement speed is; the smoother the contour shape of the contoured raised area, the less the second reference smoke movement velocity. The sharpness and smoothness of the contour shape may be understood as meaning that the steeper the contour shape, the sharper the contour shape tends to be, and the gentler the contour shape, the smoother the contour shape tends to be. The second reference movement speed corresponding to the contour point closer to the vertex of the contour convex area is smaller, and the second reference movement speed corresponding to the contour point farther from the vertex of the contour convex area is smaller.
Determining the average value of the first reference smoke movement speed and the second reference smoke movement speed as the target smoke movement speed; the maximum value of the first reference smoke movement speed and the second reference smoke movement speed can also be determined as the target smoke movement speed; the minimum value of the first reference smoke movement speed and the second reference smoke movement speed can also be determined as the target smoke movement speed.
In the example, the diffusion direction of the smoke and the position information and the shape of the rectangular outline convex area are determined through the obtained outline convex area to determine the movement speed of the target smoke, and the diffusion direction and the movement speed of the target smoke are determined as smoke movement parameters, so that the accuracy of smoke movement parameter determination is improved.
In a possible implementation manner, due to the influence of the environment on the smoke form, a certain error exists in the smoke type determination, and then the smoke type may be corrected, specifically including:
d1, acquiring morphological parameters of the wind power identifier in the target image;
d2, determining the wind power parameters of the area to be detected according to the morphological parameters;
d3, determining the environmental information of the area to be detected according to the target image;
d4, determining first type correction information according to the wind power parameters and the environment information;
d5, acquiring the illumination intensity of the area to be detected;
d6, determining second type correction information according to the illumination intensity;
d7, determining target correction information according to the first type correction information and the second type correction information;
d8, correcting the first smoke type according to the target correction information to obtain a second smoke type.
The morphological parameters of the wind power identification can be determined according to methods such as feature extraction, and the wind power parameters of the area to be detected can be determined according to the mapping relation between the morphological parameters and the wind power parameters. Of course, the wind parameter may also be determined in other ways, for example by a wind sensor or the like. The wind parameters may include wind force magnitude, etc.
The information corresponding to the background image in the target image can be determined as the environmental information of the area to be detected. Or the target image can be identified to determine the environmental information of the region to be detected. The environmental information may include the color of the environment, the weather conditions in the environment, etc.
When the first type of correction information is determined, the larger the wind power is, the higher the correction intensity of the correction information is, and the smaller the wind power is, the lower the correction intensity of the correction information is; the greater the similarity between the color in the environmental information and the smoke color is, the higher the correction intensity of the correction information is, and the smaller the similarity between the color in the environmental information and the smoke color is, the lower the correction intensity of the correction information is; if the weather in the environmental information is poor weather, the correction strength of the correction information is higher, and if the weather in the environmental information is good weather, the correction strength of the correction information is lower.
The correction intensity is understood to be a correction amount when the first smoke type is corrected, and the correction intensity is larger, the correction intensity is smaller, and the correction amount is smaller. The correction quantity may be a quantity which characterizes the first smoke type as being corrected to other smoke types. Bad weather may include various types of rain including rainy days, such as, for example, gusts of rain, light rain, heavy to heavy rain, and the like. Good weather may include sunny days, etc.
The method for determining the second type of correction information according to the illumination intensity may be: and determining an offset value between the illumination intensity of the common weather according to the illumination intensity, wherein the larger the offset value is, the larger the correction amount of the second type correction information is, and the smaller the offset value is, the smaller the correction amount of the second type correction information is. Ordinary weather is understood to include weather with a preset light intensity. The preset illumination intensity is set by an empirical value or historical data.
An average value of the first type correction information and the second type correction information may be determined as the target correction information.
In this example, the first type of correction information is determined according to the wind power parameter and the environmental information, the second type of correction information is determined according to the illumination intensity, the target correction information is determined according to the first type of correction information and the second type of correction information, and the first smoke type is corrected according to the target correction information to obtain the second smoke type, so that the first smoke type can be corrected to obtain the second smoke type, and the accuracy in smoke type determination is improved.
In a possible implementation manner, the data may also be stored, which may specifically be:
the cyclic data storage method comprises the following steps:
and setting a cycle counting clock and accumulating the count. For example: 0. 1. N;
and (4) clock counting and saving. An address is to be designated by sending designated data;
and detecting the clock value, wherein N is a clock return-to-zero signal, and the rest represents a corresponding program processing interface.
The cyclic data relation comparison and data relation mapping method comprises the following steps:
and step 1, realizing data storage of the first two times by using a cyclic data storage method according to the mapping relation.
Step 2, judging which relation the previous two times of data satisfy:
a. satisfy A > B data output 1
b. Satisfy A < becomeB ═ data output 0
Step 3, judging the previous data and the current data, and executing the step 3)
And 4, judging that the two outputs of the step 3 and the step 4 are both effective values 1, if so, the reaction is 1, and if not, the reaction is 0.
Referring to fig. 2, fig. 2 is a schematic flow chart of another smoke dynamic identification method according to an embodiment of the present application. As shown in fig. 2, the method includes:
201. acquiring a target image of a to-be-detected area;
202. extracting the features of the target image to obtain feature data of the target image;
203. determining the smoke content in the area to be detected according to the gray value;
204. determining the smoke state information according to the smoke content;
205. acquiring a smoke contour of a smoke area in the target image;
206. determining the smoke motion parameters according to the smoke contour;
207. and determining a first smoke type according to the smoke state information and the smoke motion parameters.
In this example, the smoke contour of the smoke region in the target image is obtained, and the smoke motion parameter is determined according to the relevant parameter of the contour convex region of the smoke contour, so that the accuracy of determining the smoke motion parameter can be improved.
In accordance with the foregoing embodiments, please refer to fig. 3, fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present application, and as shown in the drawing, the terminal includes a processor, an input device, an output device, and a memory, and the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for performing the following steps;
acquiring a target image of a to-be-detected area by a gray characteristic value method;
extracting the features of the target image to obtain feature data of the target image;
performing data modeling operation according to the characteristic data to determine smoke state information of the area to be detected;
comparing the characteristic data of the target image to determine smoke motion parameters;
and determining a first smoke type according to the smoke state information and the smoke motion parameters.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the terminal includes corresponding hardware structures and/or software modules for performing the respective functions in order to implement the above-described functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the terminal may be divided into the functional units according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
In accordance with the above, please refer to fig. 4, and fig. 4 is a schematic structural diagram of a smoke dynamic recognition device according to an embodiment of the present application. As shown in fig. 4, the apparatus includes:
an obtaining unit 401, configured to obtain a target image of a to-be-detected region by using a gray characteristic value method;
an extracting unit 402, configured to perform feature extraction on the target image to obtain feature data of the target image;
a first determining unit 403, configured to perform data modeling operation according to the feature data to determine smoke state information of the area to be detected;
a second determining unit 404, configured to determine a smoke motion parameter according to comparison of the feature data of the target image;
a third determining unit 405, configured to determine the first smoke type according to the smoke status information and the smoke motion parameter.
In one possible implementation manner, the feature data includes a gray-scale value, and the first determining unit 403 is configured to:
determining the smoke content in the area to be detected according to the gray value;
and determining the smoke state information according to the smoke content.
In one possible implementation manner, the second determining unit 404 is configured to:
acquiring a smoke contour of a smoke region in the feature data in the target image;
and determining the smoke motion parameters according to the change of the smoke contour.
In one possible implementation, in the determining the smoke motion parameter according to the smoke profile, the second determining unit 404 is configured to:
acquiring M contour convex areas of the smoke contour;
determining the smoke diffusion direction of each contour convex area in the M contour convex areas according to the M contour convex areas;
acquiring position information of the M contour convex areas in the smoke contour;
determining a first reference smoke movement speed of smoke in each contour convex area in the M contour convex areas according to the position information and the smoke generation points;
determining a second reference smoke movement speed of the smoke in each of the M contour-convex areas according to the contour shape of the M contour-convex areas;
determining a target smoke movement velocity of smoke in each of the M contour-salient regions according to a first reference smoke movement velocity of smoke in each of the M contour-salient regions and a second reference smoke movement velocity of smoke in each of the M contour-salient regions;
and determining the smoke diffusion direction of each of the M contour convex areas and the target smoke movement speed of the smoke in each of the M contour convex areas as the smoke movement parameters.
In one possible implementation, the apparatus is further configured to:
acquiring morphological parameters of a wind power identifier in the target image;
determining the wind power parameters of the area to be detected according to the morphological parameters;
determining the environmental information of the area to be detected according to the target image;
determining first type correction information according to the wind power parameters and the environment information;
acquiring the illumination intensity of the area to be detected;
determining second type correction information according to the illumination intensity;
determining target correction information according to the first type correction information and the second type correction information;
and correcting the first smoke type according to the target correction information to obtain a second smoke type.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the smoke dynamic identification methods as described in the above method embodiments.
Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program causes a computer to execute part or all of the steps of any one of the smoke dynamic identification methods as described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash memory disks, read-only memory, random access memory, magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method for dynamic smoke identification, the method comprising:
acquiring a target image of a to-be-detected area by a gray characteristic value method;
extracting the features of the target image to obtain feature data of the target image;
performing data modeling operation according to the characteristic data to determine smoke state information of the area to be detected;
comparing the characteristic data of the target image to determine smoke motion parameters;
and determining a first smoke type according to the smoke state information and the smoke motion parameters.
2. The method of claim 1, wherein the characteristic data comprises gray scale values, and the performing a data modeling operation based on the characteristic data to determine smoke status information of the area to be detected comprises:
determining the smoke content in the area to be detected according to the gray value;
and determining the smoke state information according to the smoke content.
3. The method according to claim 1 or 2, wherein the comparing according to the feature data of the target image to determine the smoke motion parameter comprises:
acquiring a smoke contour of a smoke region in the feature data in the target image;
and determining the smoke motion parameters according to the change of the smoke contour.
4. The method of claim 3, wherein said determining said smoke motion parameter from said smoke profile comprises:
acquiring M contour convex areas of the smoke contour;
determining the smoke diffusion direction of each contour convex area in the M contour convex areas according to the M contour convex areas;
acquiring position information of the M contour convex areas in the smoke contour;
determining a first reference smoke movement speed of smoke in each contour convex area in the M contour convex areas according to the position information and the smoke generation points;
determining a second reference smoke movement speed of the smoke in each of the M contour-convex areas according to the contour shape of the M contour-convex areas;
determining a target smoke movement velocity of smoke in each of the M contour-salient regions according to a first reference smoke movement velocity of smoke in each of the M contour-salient regions and a second reference smoke movement velocity of smoke in each of the M contour-salient regions;
and determining the smoke diffusion direction of each of the M contour convex areas and the target smoke movement speed of the smoke in each of the M contour convex areas as the smoke movement parameters.
5. The method of claim 4, further comprising:
acquiring morphological parameters of a wind power identifier in the target image;
determining the wind power parameters of the area to be detected according to the morphological parameters;
determining the environmental information of the area to be detected according to the target image;
determining first type correction information according to the wind power parameters and the environment information;
acquiring the illumination intensity of the area to be detected;
determining second type correction information according to the illumination intensity;
determining target correction information according to the first type correction information and the second type correction information;
and correcting the first smoke type according to the target correction information to obtain a second smoke type.
6. A smoke dynamic identification device, the device comprising:
the acquisition unit is used for acquiring a target image of the area to be detected by a gray characteristic value method;
the extraction unit is used for extracting the features of the target image to obtain feature data of the target image;
the first determining unit is used for carrying out data modeling operation according to the characteristic data so as to determine smoke state information of the area to be detected;
the second determining unit is used for determining smoke motion parameters according to the comparison of the characteristic data of the target image;
and the third determining unit is used for determining the first smoke type according to the smoke state information and the smoke motion parameters.
7. The apparatus of claim 6, wherein the feature data comprises a gray value, and wherein the first determining unit is configured to:
determining the smoke content in the area to be detected according to the gray value;
and determining the smoke state information according to the smoke content.
8. The apparatus according to claim 6 or 7, wherein the second determining unit is configured to:
acquiring a smoke contour of a smoke region in the feature data in the target image;
and determining the smoke motion parameters according to the change of the smoke contour.
9. A terminal, comprising a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-5.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to carry out the method according to any one of claims 1-5.
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| CN202111215104.0A CN113989516A (en) | 2021-10-19 | 2021-10-19 | Smoke dynamic identification method and related device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116824166A (en) * | 2023-08-29 | 2023-09-29 | 南方电网数字电网研究院有限公司 | Transmission line smoke identification method, device, computer equipment and storage medium |
| CN117079167A (en) * | 2023-10-18 | 2023-11-17 | 山东龙翼航空科技有限公司 | Image processing-based high-rise fire-fighting unmanned aerial vehicle monitoring method |
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2021
- 2021-10-19 CN CN202111215104.0A patent/CN113989516A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116824166A (en) * | 2023-08-29 | 2023-09-29 | 南方电网数字电网研究院有限公司 | Transmission line smoke identification method, device, computer equipment and storage medium |
| CN116824166B (en) * | 2023-08-29 | 2024-03-08 | 南方电网数字电网研究院股份有限公司 | Transmission line smoke identification method, device, computer equipment and storage medium |
| CN117079167A (en) * | 2023-10-18 | 2023-11-17 | 山东龙翼航空科技有限公司 | Image processing-based high-rise fire-fighting unmanned aerial vehicle monitoring method |
| CN117079167B (en) * | 2023-10-18 | 2024-01-09 | 山东龙翼航空科技有限公司 | Image processing-based high-rise fire-fighting unmanned aerial vehicle monitoring method |
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