Construction environment safety intelligent monitoring method and system

Abstract

The invention relates to the technical field of intelligent fire monitoring, in particular to an intelligent monitoring method and system for construction environment safety. The method comprises the following steps: acquiring a first fire possibility of a to-be-monitored area according to texture features of smoke areas in all continuous target video frame grey-scale maps; acquiring a second fire possibility of the to-be-monitored area according to the movement condition of the smoke area between the adjacent target video frame grey-scale maps; acquiring a third fire possibility of the to-be-monitored region according to the area change and texture change of the smoke region between adjacent target video frame grey-scale maps in combination with the smoke duration; and the fire possibility of the to-be-monitored area is evaluated so as to carry out safety monitoring. According to the method, the possibility that the smoke area in the continuous target video frame grey-scale map is the fire smoke is analyzed based on the distinguishing texture features and the distinguishing motion features between the fire smoke and the non-fire smoke, the interference of other non-fire smoke in the construction site is reduced, and the fire safety intelligent monitoring accuracy of the construction site is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent fire monitoring, and in particular to a method and system for intelligent monitoring of construction environment safety. Background Art

[0002] The construction materials and electrical lines in the construction environment are complicated, and some equipment involves open flames, which can easily cause fires. Therefore, safety supervision of the construction site is crucial to ensure personnel safety and project quality, as well as to maintain the corporate image. In recent years, technologies such as the Internet of Things and artificial intelligence have developed rapidly. Industrial visual intelligent monitoring is gradually replacing manual inspections and manual supervision. Fire monitoring and prevention mechanisms based on industrial visual intelligence have been promoted and applied, greatly improving the efficiency and accuracy of safety supervision in the construction environment.

[0003] However, fire smoke detection in construction environments faces many challenges. There are other sources of smoke in the construction site, such as dust or wood chips. Fire monitoring based on industrial visual intelligence will be interfered by other smoke and prone to false alarms, which increases the difficulty of fire monitoring and early warning, and thus leads to poor safety intelligent monitoring of the construction site. Summary of the invention

[0004] In order to solve the technical problem that the existing technology has poor effect on the safety intelligent monitoring in the construction environment, the purpose of the present invention is to provide a construction environment safety intelligent monitoring method and system, and the technical solution adopted is as follows:

[0005] The present invention provides a construction environment safety intelligent monitoring method, the method comprising:

[0006] Obtaining continuous target video frame grayscale images in the monitoring video of the area to be monitored at the construction site, and obtaining the smoke area in each of the target video frame grayscale images;

[0007] According to the texture features of the smoke area in all the grayscale images of the target video frames, the first fire possibility of the area to be monitored is obtained; between the adjacent grayscale images of the target video frames, the second fire possibility of the area to be monitored is obtained according to the movement of the smoke area; according to the area change and texture change of the smoke area between the adjacent grayscale images of the target video frames, combined with the duration of the smoke in the monitoring video, the third fire possibility of the area to be monitored is obtained;

[0008] The first fire possibility, the second fire possibility and the third fire possibility are integrated to obtain the fire possibility of the area to be monitored; and security monitoring is performed based on the fire possibility.

[0009] Furthermore, the method for obtaining the first fire possibility includes:

[0010] Obtaining a gray level co-occurrence matrix of the smoke region in each target video frame grayscale image; obtaining contrast, correlation, homogeneity and entropy of the gray level co-occurrence matrix;

[0011] According to the contrast, the correlation, the homogeneity and the entropy, a fire smoke texture parameter of the smoke area in the grayscale image of each target video frame is obtained; the contrast and the entropy are both positively correlated with the fire smoke texture parameter, and the correlation and the homogeneity are both negatively correlated with the fire smoke texture parameter;

[0012] The fire smoke texture parameters of the smoke area in all the target video frame grayscale images are combined to obtain the first fire possibility of the area to be monitored.

[0013] Furthermore, the method for obtaining the second fire possibility includes:

[0014] According to the change difference of the pixel points in the smoke area between the adjacent target video frame grayscale images, the moving direction and the moving speed of the smoke area in each target video frame grayscale image are obtained;

[0015] The smoke trajectory confusion coefficient in the area to be monitored is obtained by comprehensively analyzing the change uniformity of the moving direction and the moving speed of the smoke area between the adjacent target video frame grayscale images;

[0016] According to the moving speed of the smoke area in the grayscale image of all the target video frames, and the directional difference between the moving direction and the preset direction, combined with the smoke trajectory confusion coefficient, the second fire possibility of the monitored area is obtained; the moving speed is positively correlated with the second fire possibility, and the directional difference and the smoke trajectory confusion coefficient are both negatively correlated with the second fire possibility.

[0017] Furthermore, the method for obtaining the moving direction and the moving speed includes:

[0018] Obtaining a motion vector of each pixel in the smoke area in each target video frame grayscale image based on an optical flow method;

[0019] According to the motion vector of each pixel point, the moving speed and moving direction of the smoke area in each target video frame grayscale image are obtained.

[0020] Furthermore, the method for obtaining the smoke trajectory confusion coefficient includes:

[0021] Constructing a moving vector of the moving speed and the moving direction of the smoke area in each target video frame grayscale image;

[0022] Obtaining the Euclidean distance between the corresponding motion vectors between each target video frame grayscale image and the next adjacent target video frame grayscale image;

[0023] The corresponding Euclidean distances between all adjacent grayscale images of the target video frames are integrated to obtain the smoke trajectory confusion coefficient in the area to be monitored.

[0024] Furthermore, the method for obtaining the duration of the smoke includes:

[0025] The product of the appearance frequency of the smoke area in all target video frame grayscale images and the frame rate of the target video frame grayscale image is taken as the smoke duration.

[0026] Furthermore, the method for obtaining the third fire possibility includes:

[0027] Between each target video frame grayscale image and the next adjacent target video frame grayscale image, the sum of the area change difference and the overall gradient change difference of the smoke region is used as the dissipation parameter of the smoke region between the adjacent target video frame grayscale images; the dissipation parameters of the smoke region between all adjacent target video frame grayscale images are combined to obtain the smoke dissipation coefficient of the area to be monitored;

[0028] The third fire possibility of the area to be monitored is obtained according to the smoke dissipation coefficient and the smoke duration; the smoke duration is positively correlated with the third fire possibility, and the smoke dissipation coefficient is negatively correlated with the third fire possibility.

[0029] Furthermore, the method for performing safety monitoring according to the fire possibility includes:

[0030] When the fire possibility is greater than a preset threshold, a safety warning is issued to the monitored area.

[0031] Furthermore, the preset direction is a direction vertical to the ground and upward.

[0032] The present invention also proposes a construction environment safety intelligent monitoring system, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the construction environment safety intelligent monitoring method are implemented.

[0033] The present invention has the following beneficial effects:

[0034] The present invention obtains all continuous target video frame grayscale images in the monitoring video of the area to be monitored at the construction site, and obtains the smoke area in each target video frame grayscale image so as to subsequently analyze the texture characteristics and changes of the smoke area, and then evaluate the possibility of fire in the area to be monitored; according to the texture characteristics of the smoke area in all target video frame grayscale images, the first fire possibility of the area to be monitored is obtained; between adjacent target video frame grayscale images, according to the movement of the smoke area, the second fire possibility of the area to be monitored is obtained; according to the area change and texture change of the smoke area between adjacent target video frame grayscale images, combined with the duration of smoke in the monitoring video, the third fire possibility of the area to be monitored is obtained; the first fire possibility, the second fire possibility and the third fire possibility are integrated to obtain the fire possibility of the area to be monitored; and safety monitoring is performed according to the fire possibility. Based on the distinguishing texture characteristics and distinguishing motion characteristics between fire smoke and non-fire smoke, the present invention analyzes the possibility that the smoke area in the continuous target video frame grayscale image is fire smoke, thereby reducing the interference of other non-fire smoke in the construction site and improving the accuracy of fire safety intelligent monitoring of the construction site by industrial visual intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flowchart of a construction environment safety intelligent monitoring method provided by one embodiment of the present invention;

[0037] Figure 2 A flow chart of a method for obtaining a first fire possibility provided by an embodiment of the present invention;

[0038] Figure 3 A flow chart of a method for obtaining a second fire possibility provided by an embodiment of the present invention;

[0039] Figure 4 A flow chart of a method for obtaining the third fire possibility provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a construction environment safety intelligent monitoring method and system proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0041] 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 invention belongs.

[0042] The specific scheme of a construction environment safety intelligent monitoring method and system provided by the present invention is described in detail below with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a flow chart of a construction environment safety intelligent monitoring method provided by an embodiment of the present invention, specifically comprising:

[0044] Step S1, obtaining all continuous target video frame grayscale images in the monitoring video of the area to be monitored at the construction site, and obtaining the smoke area in the grayscale image of each target video frame.

[0045] In one embodiment of the present invention, a high-definition camera is installed at the construction site to monitor the flammable area of ​​the construction site in real time. The present invention takes one high-definition camera monitoring one flammable area as an example, performs corresponding frame segmentation and grayscale preprocessing on the real-time acquired monitoring video, and further extracts the smoke area in the grayscale image of each video frame, so as to subsequently analyze the texture characteristics and changes of the smoke area, so as to evaluate the possibility of it being fire smoke, and then evaluate the possibility of fire in each monitored area; in other embodiments of the present invention, the implementer can also use multiple cameras to monitor a flammable area, and comprehensively evaluate the possibility of fire in the monitored area based on the monitoring results of multiple cameras.

[0046] As an example, surveillance video is acquired in real time, wherein the frame rate of the surveillance video is set to 30fps to obtain all video frame images, and then the video frame images are grayscale processed and conventional denoising is performed to obtain grayscale images of all video frames; the smoke area in the grayscale images of all video frames is identified using the trained convolutional neural network, and the moment when the smoke area is first identified is used as the time for early warning analysis. Within 10 seconds after the time for early warning analysis, all video frame grayscale images in the surveillance video of the monitored area are acquired in real time, that is, 300 consecutive video frame grayscale images after the smoke area is first identified are used as continuous target video frame grayscale images for analysis and monitoring, and the smoke area in each video frame grayscale image is acquired.

[0047] It should be noted that grayscale processing and denoising, training and application of convolutional neural networks are already existing technologies well known to those skilled in the art and will not be described in detail here. In other embodiments of the present invention, the implementer may also adopt other neural network models, or set up multiple cameras to collect surveillance videos of the area to be monitored, fuse the grayscale images of multi-view target video frames to extract the smoke area, and directly perform optical flow analysis on the grayscale images of all target video frames to extract the smoke area. These are all existing technologies well known to those skilled in the art and will not be described in detail here.

[0048] It should be noted that the embodiment of the present invention performs monitoring, analysis and early warning within 10 seconds after smoke is detected in the monitored area; in the 300 consecutive target video frame grayscale images within these 10 seconds, it is possible that the smoke area is not identified in some of the target video frame grayscale images, that is, the smoke dissipates after a period of time, and then the subsequent target video frame grayscale images will no longer be analyzed, and only the target video frame grayscale images of the continuous smoke area will be intercepted and analyzed; in other embodiments of the present invention, the implementer may also set the total number of other continuous target video frame grayscale images, such as 300 to 1800 frames, that is, the monitoring time from the first identification of the smoke area to the current moment is between 10 seconds and 60 seconds. The monitoring period should not be too long, otherwise the near real-time monitoring effect cannot be achieved; it should not be too short, otherwise the characteristic changes of the smoke area are not obvious, resulting in poor analysis effect.

[0049] Step S2, according to the texture features of the smoke area in the grayscale images of all target video frames, obtain the first fire possibility of the area to be monitored; between the grayscale images of adjacent target video frames, according to the movement of the smoke area, obtain the second fire possibility of the area to be monitored; according to the area change and texture change of the smoke area between the grayscale images of adjacent target video frames, combined with the duration of smoke in the monitoring video, obtain the third fire possibility of the area to be monitored.

[0050] Since dust or wood chips may fly around at the construction site, other non-fire smoke may appear, which may interfere with fire monitoring during visual intelligent recognition. Considering that fire smoke has a more obvious smoke texture due to its irregular morphological structure and the influence of fire light refraction and shadow, while dust smoke or wood chip smoke usually presents a mist-like morphological structure with a fuzzy smoke texture boundary; therefore, the embodiment of the present invention analyzes the texture features of the smoke area in the grayscale image of all target video frames from a static perspective based on the distinguishing texture features of fire smoke and other interfering smoke, evaluates the possibility of it being fire smoke, and obtains the first fire possibility of the area to be monitored.

[0051] Preferably, in one embodiment of the present invention, the method for obtaining the first fire possibility includes:

[0052] See also Figure 2, which shows a flow chart of a method for obtaining a first fire possibility provided by an embodiment of the present invention, specifically comprising:

[0053] Step S201, obtaining the gray level co-occurrence matrix of the smoke area in the gray level image of each target video frame; obtaining the contrast, correlation, homogeneity and entropy of the gray level co-occurrence matrix.

[0054] Considering that the grayscale co-occurrence matrix is ​​a statistical method for describing image texture features, in one embodiment of the present invention, the grayscale co-occurrence matrix of the smoke area in the grayscale image of each target video frame is first obtained, wherein since the smoke area may be an irregular image, after extracting the smoke area, the implementer may set the grayscale value of the non-smoke area in the grayscale image of the target video frame to 0 as the background area to obtain the processed target video frame grayscale image, and further obtain the grayscale co-occurrence matrix of the processed target video frame grayscale image. Obtaining the grayscale co-occurrence matrix of the smoke area is a prior art well known to those skilled in the art and will not be elaborated here.

[0055] Considering that the grayscale variation of fire smoke is large, the contrast is usually high; since the correlation feature in the grayscale co-occurrence matrix reflects the similarity of the matrix elements in the row and column directions, it indirectly reflects the grayscale correlation of the smoke area. The lower the correlation, the more complex and chaotic the texture features of the smoke area are, and the more consistent with the texture features of fire smoke; the entropy feature is used to measure the distribution of grayscale levels. The larger the entropy, the more uneven and chaotic the smoke texture is, and the more consistent with the texture features of fire smoke; homogeneity also reflects the texture uniformity of the smoke area. The smaller the homogeneity, the more consistent with the texture features of fire smoke. Therefore, in one embodiment of the present invention, the contrast, correlation, homogeneity and entropy of the grayscale co-occurrence matrix are obtained to prepare for obtaining the fire smoke texture parameters of the smoke area in the grayscale image of each target video frame in the subsequent step S202.

[0056] It should be noted that the methods for obtaining features such as contrast, correlation, homogeneity and entropy are already existing technologies well known to those skilled in the art and will not be elaborated here; in other embodiments of the present invention, the implementer may also obtain features of other gray-level co-occurrence matrices to evaluate the uniformity of the texture and further evaluate the first fire possibility.

[0057] Step S202, obtaining fire smoke texture parameters of the smoke area in the grayscale image of each target video frame according to contrast, correlation, homogeneity and entropy; contrast and entropy are both positively correlated with the fire smoke texture parameters, and correlation and homogeneity are both negatively correlated with the fire smoke texture parameters.

[0058] As an example, the fire smoke texture parameters are calculated as: Among them, JT i,kis the fire smoke texture parameter of the smoke area in the grayscale image of the kth target video frame in the ith monitoring video; CT i,k is the contrast of the grayscale co-occurrence matrix of the smoke area in the grayscale image of the kth target video frame in the ith surveillance video; EN i,k is the entropy of the gray-level co-occurrence matrix of the smoke area in the grayscale image of the k-th target video frame in the ith surveillance video; CL i,k Hom is the correlation of the gray-level co-occurrence matrix of the smoke area in the gray-level image of the k-th target video frame in the i-th surveillance video; i,k is the homogeneity of the gray-level co-occurrence matrix of the smoke region in the grayscale image of the k-th target video frame in the ith surveillance video.

[0059] In the formula, addition or ratio is specifically used to represent the corresponding positive correlation or negative correlation; in other embodiments of the present invention, the implementer may also use other basic mathematical operations or related mapping means to represent the correlation, such as addition or subtraction, which will not be repeated here.

[0060] Step S203, combining the fire smoke texture parameters of the smoke areas in the grayscale images of all target video frames to obtain the first fire possibility of the area to be monitored.

[0061] As an example, the mean of the fire smoke texture parameters of the smoke area in the grayscale image of all target video frames is normalized to obtain the first fire possibility of the monitored area; specifically, the maximum and minimum normalization method is adopted, and the implementer may also adopt other normalization methods, which will not be repeated here.

[0062] At this point, the first fire possibility of the area to be monitored in each surveillance video is obtained.

[0063] Considering that the heat released by the burning of materials in the fire causes the surrounding air to heat up and rise, the fire smoke usually shows a trend of rapid upward drift; while the movement of dust smoke or wood chip smoke may diffuse freely and directionlessly in the air. Therefore, based on the distinguishing movement characteristics of fire smoke and other interfering smoke, the embodiment of the present invention analyzes the changes in the moving direction and speed of the smoke area between the grayscale images of adjacent target video frames from a dynamic perspective to obtain the second fire possibility of the area to be monitored.

[0064] Preferably, in one embodiment of the present invention, the method for obtaining the second fire possibility includes:

[0065] See also Figure 3 , which shows a flow chart of a method for obtaining a second fire possibility provided by an embodiment of the present invention, specifically comprising:

[0066] Step S301, obtaining the moving direction and moving speed of the smoke area in each target video frame grayscale image according to the change difference of the pixel points in the smoke area between the adjacent target video frame grayscale images.

[0067] Taking into account the distinguishing motion characteristics between fire smoke and other smoke, the changes in the smoke area between adjacent target video frame grayscale images may not be consistent. Therefore, one embodiment of the present invention will evaluate the motion of the smoke area based on the difference in changes of pixel points in the smoke area between adjacent target video frame grayscale images, and obtain the moving direction and speed of the smoke area in each target video frame grayscale image.

[0068] In a preferred embodiment of the present invention, considering that the optical flow method can accurately perform motion analysis on objects in a video frame sequence, the method for obtaining the moving direction and moving speed includes:

[0069] Obtain the motion vector of each pixel in the smoke area of ​​each target video frame grayscale image based on the optical flow method;

[0070] Based on the motion vector of each pixel, the moving speed and direction of the smoke area in the grayscale image of each target video frame are obtained.

[0071] As an example, after obtaining the motion vector of each pixel based on the optical flow method, since the motion vector includes the horizontal component and the vertical component of each pixel, the Euclidean norm can be used to obtain the displacement value of each pixel, and this displacement value approximately replaces its moving speed; the inverse tangent function can be used to obtain the displacement angle, that is, the moving direction, of each pixel; in the smoke area in the grayscale image of each target video frame, the moving speeds and moving directions of all pixels are averaged respectively, and the overall moving speed and moving direction of the smoke area in the grayscale image of the target video frame can be obtained.

[0072] It should be noted that the optical flow method for obtaining motion vectors and using motion vectors to obtain speed and direction are existing technologies well known to those skilled in the art and will not be elaborated here. In other examples, implementers can also obtain the mode of the moving speed and moving direction of all pixels respectively instead of calculating the mean, which will not be elaborated here.

[0073] In another embodiment of the present invention, the implementer may also detect and track feature points or feature descriptors in the smoke area in the video frame, match the feature points or descriptors in the smoke area between the grayscale images of adjacent target video frames, and estimate the moving direction and speed through the matching results. This is an existing technology well known to those skilled in the art and will not be described in detail here.

[0074] Step S302 , the smoke trajectory confusion coefficient in the area to be monitored is obtained by comprehensively analyzing the change uniformity of the moving direction and moving speed of the smoke area between the grayscale images of adjacent target video frames.

[0075] Considering that the moving direction and moving speed of the smoke area reflect the movement trajectory of the smoke area to a certain extent, since fire smoke usually spreads rapidly upward vertically from the ground, the movement trajectory is relatively consistent; while other interfering smoke usually diffuses freely without direction, and the movement trajectory is relatively chaotic; therefore, one embodiment of the present invention comprehensively considers the uniformity of changes in the moving direction and moving speed of the smoke area, estimates the smoke movement trajectory, and obtains the smoke trajectory confusion coefficient in the monitored area. The larger the smoke trajectory confusion coefficient, the less it conforms to the movement trajectory characteristics of the fire smoke, and the smaller the possibility of fire.

[0076] In a preferred embodiment of the present invention, considering that both the moving speed and the moving angle can reflect the movement trajectory of the smoke, the two are analyzed comprehensively, and the method for obtaining the smoke trajectory confusion coefficient includes:

[0077] Construct the moving vector of the moving speed and moving direction of the smoke area in the grayscale image of each target video frame;

[0078] Obtain the Euclidean distance between the corresponding motion vectors between each target video frame grayscale image and the adjacent next target video frame grayscale image;

[0079] The corresponding Euclidean distances between grayscale images of all adjacent target video frames are combined to obtain the smoke trajectory confusion coefficient in the monitored area.

[0080] As an example, the moving vector of the smoke area in the grayscale image of the t-th target video frame in the ith surveillance video is (V i,t ,Q i,t ), where V i,t is the moving speed of the smoke area in the grayscale image of the t-th target video frame in the ith surveillance video; Q i,t is the moving direction of the smoke area in the grayscale image of the tth target video frame in the ith surveillance video; the mean of the corresponding Euclidean distances between all adjacent target video frame grayscale images is taken as the smoke trajectory confusion coefficient in the monitored area.

[0081] In another embodiment of the present invention, the direction sequence and speed sequence of the moving direction and moving speed of the smoke area can be obtained respectively according to the time sequence of the grayscale image of the target video frame, and the variance of the speed sequence and the direction sequence can be further obtained respectively, and the variance is averaged to obtain the smoke trajectory confusion coefficient; the corresponding curves of the direction sequence and the speed sequence can also be fitted, and whether the motion trajectory change is chaotic can be evaluated according to the shape of the curve, and then the smoke trajectory confusion coefficient can be comprehensively obtained, which will not be repeated here.

[0082] Step S303, according to the moving speed of the smoke area in the grayscale image of all target video frames, and the direction difference between the moving direction and the preset direction, combined with the smoke trajectory confusion coefficient, the second fire possibility of the monitored area is obtained; the moving speed is positively correlated with the second fire possibility, and the direction difference and the smoke trajectory confusion coefficient are negatively correlated with the second fire possibility.

[0083] In one embodiment of the present invention, considering that fire smoke rises vertically quickly with hot air and its movement trajectory is relatively regular and consistent, the greater the moving speed of the smoke area, the closer the moving direction is to vertically upward from the ground, and the smaller the confusion coefficient of the smoke trajectory, the greater the possibility that it is fire smoke; based on this, the second fire possibility is obtained.

[0084] As an example, the calculation formula for the second fire probability is: Among them, JD i is the second fire probability of the monitored area in the ith surveillance video; norm[] is the standard normalization function; MT i is the total number of grayscale images of the target video frame in the i-th surveillance video; V i,t is the moving speed of the smoke area in the grayscale image of the t-th target video frame in the ith surveillance video; Q i,t is the moving direction of the smoke area in the grayscale image of the tth target video frame in the ith surveillance video; Q is the preset direction; C i is the smoke trajectory confusion coefficient of the monitored area in the i-th surveillance video.

[0085] In the above formula, the preset direction is the direction vertical to the ground, that is, Q is 90°; the average moving speed of the smoke area in the grayscale image of all target video frames reflects the moving speed of the smoke, and the average deviation of the moving angle relative to the vertical direction of the ground reflects whether the smoke rises vertically; the average moving speed is used as the numerator, and the product of the smoke trajectory confusion coefficient and the mean direction deviation is used as the denominator. The greater the moving speed, the greater the possibility of the second fire, and the smaller the smoke trajectory confusion coefficient and the mean direction deviation are, the greater the possibility of the second fire; in other examples, the implementer can also use other basic mathematical operations such as addition and subtraction and other related mapping methods to combine the three, which will not be repeated here.

[0086] At this point, the second fire possibility of the area to be monitored in each surveillance video is obtained.

[0087] Considering that fire burning is a continuous process, fire smoke will gradually spread and expand, the smoke area will gradually increase and the smoke concentration will become higher and higher, making the texture features more and more irregular and complex, while non-fire smoke will usually settle slowly and naturally, the mist-like features will usually gradually dissipate and the smoke area will not continue to expand, and the texture features of the smoke area will gradually weaken or disappear; therefore, the embodiment of the present invention will obtain the third fire possibility of the area to be monitored based on the area change and texture change of the smoke area between the grayscale images of adjacent target video frames, combined with the duration of smoke in the monitoring video.

[0088] Preferably, in one embodiment of the present invention, the method for obtaining the third fire possibility includes:

[0089] See also Figure 4 , which shows a flow chart of a method for obtaining the third fire possibility provided by an embodiment of the present invention, specifically comprising:

[0090] Step S401, between each target video frame grayscale image and the next adjacent target video frame grayscale image, the sum of the area change difference of the smoke area and the overall gradient change difference is used as the dissipation parameter of the smoke area between the adjacent target video frame grayscale images; the dissipation parameters of the smoke area between all adjacent target video frame grayscale images are combined to obtain the smoke dissipation coefficient of the area to be monitored.

[0091] It should be noted that, in one embodiment of the present invention, the method for obtaining the overall gradient of the smoke area is: using the Sobel operator convolution to calculate the gradient size of each pixel point in the smoke area, and averaging the gradient sizes of all pixel points in the smoke area to obtain the overall gradient of the entire smoke area; the larger the overall gradient, the more complex and obvious the texture features in the smoke area are; in other embodiments of the present invention, the implementer may also use other operators to obtain the pixel gradient, which is already a prior art well known to those skilled in the art and will not be repeated here.

[0092] As an example, since fire smoke will spread and expand over time, and the overall texture features will become more and more complex and obvious, the overall gradient will become larger and larger, then the smoke area in each target video frame grayscale image will have an area change difference and an overall gradient change difference relative to the smoke area in the next adjacent target video frame grayscale image. The difference will be less than zero; on the contrary, non-fire smoke will gradually dissipate and decrease over time, and the overall texture features will further fade and disappear, the overall gradient will become smaller and smaller, and the area change difference and the overall gradient change difference will both be greater than zero; the sum of the area change difference and the overall gradient change difference of the smoke area indirectly reflects the smoke dissipation situation between the corresponding adjacent target video frame grayscale images. The larger the sum, the larger the corresponding dissipation parameter, and the greater the possibility of non-fire smoke; then all dissipation parameters are averaged to obtain the smoke dissipation coefficient of the area to be monitored; the smaller the smoke dissipation coefficient, the greater the possibility of fire smoke.

[0093] In other examples, considering that in the method for obtaining the first fire possibility, the fire smoke texture parameter also evaluates the possibility of fire smoke based on the texture characteristics of the fire smoke, and the texture characteristics of the initial fire smoke are relatively lighter than the texture characteristics of the later fire smoke, then the fire smoke texture parameter should become larger and larger with time; the difference in the fire smoke texture parameter between the grayscale image of each target video frame and the grayscale image of the next adjacent target video frame can be added and combined with the difference in the area change of the smoke area to obtain the dissipation parameter, and then the smoke dissipation coefficient of the area to be monitored can be obtained.

[0094] Step S402, obtaining the third fire possibility of the area to be monitored according to the smoke dissipation coefficient and the smoke duration; the smoke duration is positively correlated with the third fire possibility, and the smoke dissipation coefficient is negatively correlated with the third fire possibility.

[0095] As an example, the smoke dissipation coefficient is subjected to negative correlation mapping adjustment logic, and then the negative correlation mapping result is multiplied and combined with the smoke duration, and then normalized to obtain the third fire possibility of the monitored area; wherein, the smoke dissipation coefficient is used as the x in exp(-x) to perform negative correlation mapping adjustment logic, exp() is an exponential function with the natural constant e as the base, and other negative correlation mapping methods may also be used; maximum and minimum value normalization is used, and other normalization methods may also be used; the larger the smoke dissipation coefficient, the smaller the possibility of fire smoke, and the longer the smoke duration, the greater the possibility of fire smoke.

[0096] The product of the frequency of occurrence of smoke areas in the grayscale images of all target video frames and the frame rate of the grayscale images of the target video frames is taken as the duration of the smoke. The implementer may also directly use the frequency of occurrence of smoke areas in the grayscale images of all target video frames to approximate the duration of the smoke.

[0097] At this point, the third fire possibility of the area to be monitored in each surveillance video is obtained.

[0098] Step S3, integrating the first fire possibility, the second fire possibility and the third fire possibility to obtain the fire possibility of the area to be monitored; and performing safety monitoring according to the fire possibility.

[0099] In one embodiment of the present invention, the first fire possibility, the second fire possibility and the third fire possibility are multiplied and combined to obtain the fire possibility of the monitored area in each monitoring video; in other embodiments, the implementer may also fuse the three through basic mathematical operations such as addition or weighted correlation and related mapping means, which will not be repeated here.

[0100] The greater the possibility of fire in each monitoring video, the greater the possibility of fire. In one embodiment of the present invention, a preset threshold is specifically set. When the preset threshold is exceeded, it is considered that a fire has occurred in the monitored area in the current monitoring video, and a safety warning is required for the monitored area. The implementer may further integrate the fire possibilities of all monitoring videos and evaluate the necessity of safety warnings. When more than a certain proportion, such as 60%, of the monitoring videos consider that a fire has occurred in the monitored area, a safety alarm is issued to warn the relevant person in charge in the construction site to take safety fire-fighting measures.

[0101] In summary, the embodiment of the present invention obtains the first fire possibility of the area to be monitored based on the texture features of the smoke area in the grayscale images of all target video frames; obtains the second fire possibility of the area to be monitored based on the movement of the smoke area between adjacent target video frame grayscale images; obtains the third fire possibility of the area to be monitored based on the area change and texture change of the smoke area between adjacent target video frame grayscale images, combined with the duration of smoke in the monitoring video; and then integrates the first fire possibility, the second fire possibility and the third fire possibility to obtain the fire possibility of the area to be monitored for safety monitoring. Based on the distinguishing texture features and distinguishing motion features between fire smoke and non-fire smoke, the present invention analyzes the possibility that the smoke area in the grayscale image of the target video frame is fire smoke, thereby reducing the interference of other non-fire smoke in the construction site and improving the accuracy of intelligent fire safety monitoring of the construction site by industrial visual intelligence.

[0102] The present invention also proposes a construction environment safety intelligent monitoring system, the system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements the steps of a construction environment safety intelligent monitoring method described in steps S1 to S3.

[0103] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0104] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A construction environment safety intelligent monitoring method, characterized in that: The method comprises: Obtaining continuous target video frame grayscale images in the monitoring video of the area to be monitored at the construction site, and obtaining the smoke area in each of the target video frame grayscale images; According to the texture features of the smoke area in all the grayscale images of the target video frames, the first fire possibility of the area to be monitored is obtained; between the adjacent grayscale images of the target video frames, the second fire possibility of the area to be monitored is obtained according to the movement of the smoke area; according to the area change and texture change of the smoke area between the adjacent grayscale images of the target video frames, combined with the duration of the smoke in the monitoring video, the third fire possibility of the area to be monitored is obtained; The first fire possibility, the second fire possibility and the third fire possibility are integrated to obtain the fire possibility of the area to be monitored; and safety monitoring is performed according to the fire possibility.

2. A construction environment safety intelligent monitoring method according to claim 1, characterized in that: The method for obtaining the first fire possibility includes: Obtaining a gray level co-occurrence matrix of the smoke region in each target video frame grayscale image; obtaining contrast, correlation, homogeneity and entropy of the gray level co-occurrence matrix; According to the contrast, the correlation, the homogeneity and the entropy, a fire smoke texture parameter of the smoke area in the grayscale image of each target video frame is obtained; the contrast and the entropy are both positively correlated with the fire smoke texture parameter, and the correlation and the homogeneity are both negatively correlated with the fire smoke texture parameter; The fire smoke texture parameters of the smoke area in all the target video frame grayscale images are combined to obtain the first fire possibility of the area to be monitored.

3. A construction environment safety intelligent monitoring method according to claim 1, characterized in that: The method for obtaining the second fire probability includes: According to the change difference of the pixel points in the smoke area between the adjacent target video frame grayscale images, the moving direction and the moving speed of the smoke area in each target video frame grayscale image are obtained; The smoke trajectory confusion coefficient in the area to be monitored is obtained by comprehensively analyzing the change uniformity of the moving direction and the moving speed of the smoke area between the adjacent target video frame grayscale images; According to the moving speed of the smoke area in the grayscale image of all the target video frames, and the directional difference between the moving direction and the preset direction, combined with the smoke trajectory confusion coefficient, the second fire possibility of the monitored area is obtained; the moving speed is positively correlated with the second fire possibility, and the directional difference and the smoke trajectory confusion coefficient are both negatively correlated with the second fire possibility.

4. A construction environment safety intelligent monitoring method according to claim 3, characterized in that: The method for obtaining the moving direction and the moving speed includes: Obtaining a motion vector of each pixel in the smoke area in each target video frame grayscale image based on an optical flow method; According to the motion vector of each pixel, the moving speed and moving direction of the smoke area in each target video frame grayscale image are obtained.

5. A construction environment safety intelligent monitoring method according to claim 3, characterized in that: The method for obtaining the smoke track confusion coefficient includes: Constructing a moving vector of the moving speed and the moving direction of the smoke area in each target video frame grayscale image; Obtaining the Euclidean distance between the corresponding motion vectors between each target video frame grayscale image and the next adjacent target video frame grayscale image; The corresponding Euclidean distances between all adjacent grayscale images of the target video frames are integrated to obtain the smoke trajectory confusion coefficient in the area to be monitored.

6. A construction environment safety intelligent monitoring method according to claim 1, characterized in that: The method for obtaining the duration of smoke includes: The product of the appearance frequency of the smoke area in all target video frame grayscale images and the frame rate of the target video frame grayscale image is taken as the smoke duration.

7. A construction environment safety intelligent monitoring method according to claim 1, characterized in that: The method for obtaining the third fire possibility includes: Between each target video frame grayscale image and the next adjacent target video frame grayscale image, the sum of the area change difference and the overall gradient change difference of the smoke region is used as the dissipation parameter of the smoke region between the adjacent target video frame grayscale images; the dissipation parameters of the smoke region between all adjacent target video frame grayscale images are combined to obtain the smoke dissipation coefficient of the area to be monitored; The third fire possibility of the area to be monitored is obtained according to the smoke dissipation coefficient and the smoke duration; the smoke duration is positively correlated with the third fire possibility, and the smoke dissipation coefficient is negatively correlated with the third fire possibility.

8. The method for intelligent monitoring of construction environment safety according to claim 1, characterized in that: The method for performing safety monitoring according to the fire possibility comprises: When the fire possibility is greater than a preset threshold, a safety warning is issued to the monitored area.

9. A construction environment safety intelligent monitoring method according to claim 3, characterized in that: The preset direction is a direction vertical to the ground and upward.

10. A construction environment safety intelligent monitoring system, characterized in that: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a construction environment safety intelligent monitoring method as described in any one of claims 1 to 9 are implemented.

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