CN112907512A - Method and device for detecting working performance of freshly-mixed self-compacting concrete - Google Patents
Method and device for detecting working performance of freshly-mixed self-compacting concrete Download PDFInfo
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Abstract
The application provides a method and a device for detecting the working performance of freshly mixed self-compacting concrete, wherein the method comprises the following steps: correcting the acquired image of each frame of slump expansion degree; carrying out contour recognition on each frame of corrected slump expansion image to obtain a concrete performance index; the concrete performance indicators include flow time, maximum expansion diameter, and segregation degree. The method and the device can calculate the flowing time, the maximum expansion diameter and the segregation degree of the freshly mixed self-compacting concrete through the contour recognition technology, and detect the working performance of the freshly mixed self-compacting concrete.
Description
Technical Field
The application relates to the technical field of concrete working performance detection, in particular to a method and a device for detecting the working performance of freshly mixed self-compacting concrete.
Background
Self-compacting Concrete (hereinafter, SCC) is a high-performance Concrete, can be fluidized and compacted under the action of its own gravity, and has good homogeneity. In order to ensure the construction quality of the engineering, the working performance of the SCC must be detected before the actual pouring is carried out. A common method for detecting the working performance of SCC is to perform a slump expansion test. Two important indexes, namely slump expansion mini-SF and flow time T can be measured through a slump expansion test500As shown in fig. 1. When lifting a slump expansion cylinder, the SCC sample in the cylinder can freely flow to form a cake-shaped mixture after stopping under the action of self gravity. Wherein, the average value of the diameter lengths of the two vertical directions of the mixture is the slump expansion mini-SF of the SCC. The time from lifting the slump expansion cylinder to the slump expansion of the mixture reaching 500mm is the flowing time T500. The mini-SF may reflect the fluidity, T, of SCC500The viscosity of SCC can be reflected.
The traditional SCC extension measurement method mostly adopts manual measurement. The measurer measures mini-SF with a steel ruler, and estimates T with naked eyes and a stopwatch500. In addition, a concrete slump and expansion degree measuring method based on an augmented reality technology and a smart phone platform is provided. The operation process is as follows: firstly, placing a predefined identifier beside the expanded SCC; then shooting an SCC image by using a camera of the mobile phone; then, solving the relative position of the mobile phone and the plane where the mark is located by using an image processing technology; and finally, appointing the start and end positions of the line segment to be measured on the mobile phone screen, and further calculating the actual distance of the line outgoing segment to obtain the mini-SF.
Generally, after the slump expansion test is finished, the degree of segregation of SCC is also determined. Segregation of SCC means that the coarse aggregate in the concrete is not uniformly and sufficiently wrapped with the fine aggregate and the cement paste. After the concrete slump expansion test is finished, if the coarse aggregate is piled up in the center or cement paste is separated out at the edge, the concrete is separated. The existing method is to characterize the segregation degree of SCC by the radial distribution gradient of stones, and this method needs to select some specific points, as shown in fig. 2, take out the stones and count them, and calculate the density according to the radial distribution gradient of stones, as shown in fig. 3, to know the segregation degree of SCC.
The SCC has a wide application prospect as a high-performance concrete with a plurality of advantages. However, a method for determining the degree of segregation which is agreed with the above method has not been known so far. The characterization method for the segregation degree of the SCC also has the problems of incapability of quantification, ambiguity and the like, and most of the methods still use visual inspection as a unique characterization means. And the slump spread mini-SF and the flow time T are measured500Among the methods of (1), the conventional method has a large operation error, especially to the flow time T500Is extremely inaccurate. And when the mini-SF is measured, the boundary points need to be manually selected in the traditional method, the intellectualization is insufficient, and the engineering practicability is low.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a method and a device for detecting the working performance of freshly mixed self-compacting concrete, which can obtain the flowing time, the maximum expansion diameter and the segregation degree of the freshly mixed self-compacting concrete through a contour recognition technology.
In order to solve the technical problem, the application provides the following technical scheme:
in a first aspect, the application provides a method for detecting the working performance of freshly mixed self-compacting concrete, which comprises the following steps:
correcting the acquired image of each frame of slump expansion degree;
and carrying out contour recognition on each frame of corrected slump expansion image to obtain a concrete performance index.
Further, the correcting the acquired per-frame slump expansion image includes:
performing edge detection on each frame of the obtained slump expansion image to obtain edge contour images of all objects in the corresponding frame;
searching a bottom plate edge contour image from the edge contour images of all objects in the frame, and determining contour coordinate information of the bottom plate edge contour image;
generating corresponding bottom plate corner point coordinate information according to the contour coordinate information;
and correcting the slump expansion images of each frame according to the coordinate information of the corner points of the bottom plate.
Further, the concrete performance index includes: the method comprises the following steps of carrying out flow time and maximum expansion diameter, carrying out outline identification on each frame of corrected slump expansion image to obtain a concrete performance index, and comprising the following steps:
carrying out contour recognition on each frame of corrected slump expansion images based on the gray value of the images;
and generating a relation curve of the expansion time and the expansion diameter according to the contour recognition result to obtain the flow time and the maximum expansion diameter.
Further, the concrete performance index includes: and (3) the segregation degree, the contour recognition is carried out on the corrected slump expansion image of the last frame, and the concrete performance index is obtained, and the method comprises the following steps:
carrying out contour recognition on the corrected slump expansion image of the last frame based on the HSV value range of the image;
and obtaining the segregation degree according to the contour recognition result.
Further, performing contour recognition on the corrected per-frame slump expansion image based on the gray value of the image, wherein the contour recognition comprises the following steps:
calculating the similarity between the pixel points according to the gray value of each pixel point in each frame of slump expansion image;
and establishing a contour formed by all the pixel points with the similarity within a preset range.
Further, performing contour recognition on the corrected slump expansion image of the last frame based on the HSV value range of the image, including:
obtaining the HSV value range of the final frame slump expansion degree image according to the HSV value of each pixel point in the final frame slump expansion degree image;
and carrying out contour recognition on the slump and expansion image of the last frame according to the HSV value range of the last frame.
Further, obtaining the segregation degree according to the contour recognition result includes:
carrying out mesh division in the outline area to respectively obtain characteristic parameters in each mesh; the stone characteristic parameters comprise: the number of stones and the surface area of the stones exposed out of the slurry;
and calculating the segregation degree according to the stone characteristic parameters in each grid.
In a second aspect, the present application provides a freshly mixed self-compacting concrete working property detection device, includes:
the correction unit is used for correcting the acquired each frame slump expansion degree image;
and the index determining unit is used for carrying out contour recognition on each frame of corrected slump expansion images to obtain the concrete performance index.
Further, the correction unit includes:
the contour detection module is used for carrying out edge detection on the obtained each frame slump expansion degree image to obtain edge contour images of all objects in the corresponding frame;
the contour coordinate determination module is used for searching a bottom plate edge contour image from the edge contour images of all objects in the frame and determining contour coordinate information of the bottom plate edge contour image;
the corner point coordinate determination module is used for generating corresponding bottom plate corner point coordinate information according to the contour coordinate information;
and the correcting module is used for correcting the slump expansion images of each frame according to the coordinate information of the corner points of the bottom plate.
Further, the concrete performance index includes: flow time and maximum extension diameter, the index determination unit includes:
each frame of contour recognition module is used for carrying out contour recognition on each frame of corrected slump expansion images based on the gray value of the images;
and the time diameter determining module is used for generating a relation curve of the expansion time and the expansion diameter according to the contour recognition result to obtain the flow time and the maximum expansion diameter.
Further, the concrete performance index includes: a degree of segregation, the index determination unit comprising:
the last frame contour identification module is used for carrying out contour identification on the corrected last frame slump expansion image based on the HSV value range of the image;
and the segregation degree determining module is used for obtaining the segregation degree according to the contour recognition result.
Further, the per-frame contour identification module includes:
the similarity determining submodule is used for calculating the similarity between the pixel points according to the gray value of each pixel point in each frame of slump expansion image;
and the each frame contour identification submodule is used for establishing a contour formed by all the pixel points with the similarity within a preset range.
Further, the last frame contour identification module includes:
the last frame value range determining submodule is used for obtaining the HSV value range of the last frame slump expansion image according to the HSV value of each pixel point in the obtained last frame slump expansion image;
and the last frame contour identification submodule is used for carrying out contour identification on the last frame slump expansion image according to the last frame HSV value range.
Further, the segregation degree determining module includes:
the stone characteristic determination submodule is used for carrying out grid division in the outline area to respectively obtain stone characteristic parameters in each grid; the stone characteristic parameters comprise: the number of stones and the surface area of the stones exposed out of the slurry;
and the segregation degree determining submodule is used for calculating the segregation degree according to the stone characteristic parameters in each grid.
In a third aspect, the present application provides an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for detecting the working performance of the fresh self-compacting concrete when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for detecting the workability of freshly mixed self-compacting concrete.
Aiming at the problems in the prior art, the application provides a method for detecting the working performance of freshly-mixed self-compacting concrete, which can detect the working performance of freshly-mixed self-compacting concrete by calculating the flowing time, the maximum expansion diameter and the segregation degree of the freshly-mixed self-compacting concrete.
Drawings
FIG. 1 is a schematic diagram of slump expansion test tests performed in the prior art;
FIG. 2 is a diagram showing a specific point when obtaining the degree of segregation in the prior art;
FIG. 3 is a diagram illustrating a prior art statistical analysis of the radial distribution gradient of the pebbles;
FIG. 4 is a flowchart of a method for detecting the working performance of freshly mixed self-compacting concrete in the embodiment of the present application;
FIG. 5 is a flowchart illustrating a process of correcting an obtained per-frame slump-expansion image according to an embodiment of the present disclosure;
FIG. 6 is a flow chart of obtaining concrete performance index in the example of the present application;
FIG. 7 is a second flowchart of the method for obtaining concrete performance index in the embodiment of the present application;
FIG. 8 is a flowchart of contour recognition performed in an embodiment of the present application;
FIG. 9 is a second flowchart of the contour recognition performed in the embodiment of the present application;
FIG. 10 is a flow chart illustrating the isolation according to the contour recognition result in the embodiment of the present application;
FIG. 11 is a structural diagram of a working property detection device for freshly mixed self-compacting concrete in an embodiment of the present application;
FIG. 12 is a block diagram of a calibration unit in the embodiment of the present application;
FIG. 13 is one of structural diagrams of a structure of an index determining unit in the embodiment of the present application;
FIG. 14 is a second block diagram of a configuration of an index determining unit according to an embodiment of the present application;
FIG. 15 is a block diagram of a per-frame contour recognition module according to an embodiment of the present application;
FIG. 16 is a block diagram of a last frame contour recognition module in an embodiment of the present application;
FIG. 17 is a structural diagram of a segregation degree determining module in the embodiment of the present application;
fig. 18 is a schematic structural diagram of an electronic device in an embodiment of the present application;
FIG. 19 shows four corner points of a base plate in an embodiment of the present application;
FIG. 20 is a corrected slump-spread image in the example of the present application;
FIG. 21 is a graph showing the relationship between the expansion time and the expansion diameter in the example of the present application;
fig. 22 is a schematic diagram of boundary points in the horizontal direction and the vertical direction in the Mask image in the 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.
Referring to fig. 4, in order to obtain the flow time, the maximum expansion diameter and the segregation degree of the freshly mixed self-compacting concrete, the application provides a method for detecting the working performance of the freshly mixed self-compacting concrete, which comprises the following steps:
s401: correcting the acquired image of each frame of slump expansion degree;
s402: and carrying out contour recognition on each frame of corrected slump expansion image to obtain a concrete performance index.
It is understood that the embodiments of the present application may be implemented by a mobile device or a computer device as an execution subject to perform the steps in the embodiments of the present application. In actual engineering, when the method is needed to detect the working performance of the freshly mixed self-compacting concrete, the whole process of slump expansion tests of the freshly mixed self-compacting concrete can be recorded by using a camera on mobile equipment, and recorded videos are stored in a memory of the mobile equipment. And then extracting the video frame by frame to obtain each frame of slump expansion image, and after carrying out gray processing on each frame of slump expansion image, correcting each frame of slump expansion image. After each frame of slump expansion image is corrected, a relation curve of expansion time and expansion diameter can be obtained by utilizing a contour recognition technology, and two indexes of flow time and maximum expansion diameter in concrete performance indexes are finally obtained; in addition, the index of segregation degree in the concrete performance index can be obtained by carrying out grid division in the outline area.
From the above description, the working performance detection method for the freshly-mixed self-compacting concrete provided by the application can detect the working performance of the freshly-mixed self-compacting concrete by calculating the flowing time, the maximum expansion diameter and the segregation degree of the freshly-mixed self-compacting concrete.
Referring to fig. 5, the correction of the acquired per-frame slump-spread image includes:
s501: performing edge detection on each frame of the obtained slump expansion image to obtain edge contour images of all objects in the corresponding frame;
s502: searching a bottom plate edge contour image from the edge contour images of all objects in the frame, and determining contour coordinate information of the bottom plate edge contour image;
s503: generating corresponding bottom plate corner point coordinate information according to the contour coordinate information;
s504: and correcting the slump expansion images of each frame according to the coordinate information of the corner points of the bottom plate.
It can be understood that, in the embodiment of the present application, edge detection is performed on each frame of slump expansion image by using a Canny edge detection operator. In the detection process, all objects in each frame of slump expansion image can be hollowed, and only edges are left, so that edge contour images of all objects in the frame are obtained. When the slump expansion test of the freshly mixed self-compacting concrete is carried out, the freshly mixed self-compacting concrete needs to be placed on a bottom plate for testing, so that each frame of slump expansion image comprises the bottom plate. In general, the edge contour of the base plate is the largest compared to other objects in each frame of slump-spread image, so the edge contour of the base plate can be easily found by using the functions findcount and drawcount in the cross-platform computer vision and machine learning software library OpenCV, and the edge contour points of the base plate are plotted in the clockwise direction. Thus, since the base plate is generally square, the four corner points of the base plate can be determined by drawing lines with dots, see fig. 19. In general, the coordinates of the points that make up the edge profile of the substrate may be stored in a two-dimensional array, with each element in the array representing coordinate information for a point in the edge profile, in a sequential manner along the profileNeedle direction storage. Smallest abscissa X in the arrayminAnd minimum ordinate YminThe corresponding point is the upper left corner point of the bottom plate, and the maximum abscissa X in the arraymaxAnd minimum ordinate YminThe corresponding point is the upper right corner point of the bottom plate, and the minimum abscissa X in the arrayminAnd maximum ordinate YmaxThe corresponding point is the left lower angular point of the bottom plate, and the maximum abscissa X in the arraymaxAnd maximum ordinate YmaxThe corresponding point is the lower right corner point of the bottom plate. The coordinate values of the four corner points are necessary parameters of a perspective transformation model, the perspective transformation model corresponds to a function getPerspectivetransform in a cross-platform computer vision and machine learning software library OpenCV, and the perspective transformation model can realize the correction of a bottom plate of each frame of collapse expansion degree image in a video, so that the bottom plate which is distorted and inclined in a natural shooting state is converted into a front view, namely a Mask image of each frame of collapse expansion degree image, and the reference is shown in FIG. 20.
When the coordinate calculation is carried out, the actual value of the bottom plate can be used for positioning calculation, the pixel value of each point in the slump expansion degree image can also be used for positioning calculation, and the conversion formula of the pixel value and the actual value is as follows:
where p is the pixel size of the target length, d is the bit depth of the slump expansion image, l is the actual size of the target length, and 2.54 is a conversion of inches to centimeters. The real size of the bottom plate can be converted into the pixel size by using the formula, so that the perspective transformation of the bottom plate is facilitated.
From the above description, the working performance detection method for the freshly mixed self-compacting concrete can correct each frame of slump expansion image.
Referring to fig. 6, the concrete performance indicators include: the method comprises the following steps of carrying out flow time and maximum expansion diameter, carrying out outline identification on each frame of corrected slump expansion image to obtain a concrete performance index, and comprising the following steps:
s601: carrying out contour recognition on each frame of corrected slump expansion images based on the gray value of the images;
it is understood that the background in each frame of slump expansion image can be removed by loading the corrected each frame of slump expansion image into a background elimination model by the embodiment of the application, and the background comprises but is not limited to a bottom plate, so that a Mask image only containing freshly mixed self-compacting concrete is obtained. Wherein, the background elimination model can separate the objects in the video which are in dynamic state. The method and the device are carried out by adopting a function background and subtrectorkknn in cross-platform computer vision and machine learning software library OpenCV.
And then, continuously carrying out edge detection and contour recognition on the Mask image by adopting a watershed algorithm, wherein a function Threshold in cross-platform computer vision and a machine learning software library OpenCV is adopted at the moment. The watershed algorithm is an image region segmentation method, in the segmentation process, the gray value of each pixel point in an image is obtained firstly, then the similarity between the pixel points is calculated according to the gray value of each pixel point, so that the pixel points which are close in spatial position and have close gray values are connected with each other to form a closed contour, and finally contour identification is carried out on each frame of corrected slump expansion images. After the identification, noise interference such as light and shadow in the Mask image can be removed, so that a more pure image of each frame slump and expansion degree is obtained, the processing of the next step is facilitated, and the specific steps can be seen in S801-S802.
S602: and generating a relation curve of the expansion time and the expansion diameter according to the contour recognition result to obtain the flow time and the maximum expansion diameter.
It can be understood that the contour curve of the freshly mixed self-compacting concrete in each frame of slump expansion image can be obtained through the contour recognition result. At this time, the functions findcount and drawcount in the cross-platform computer vision and machine learning software library OpenCV can be used again to draw the edge contour points of the freshly mixed self-compacting concrete in each frame in the clockwise direction. In general, each of the points constituting the edge profile of freshly mixed self-compacting concreteThe coordinates may be stored in a two-dimensional array, where each element in the array represents coordinate information for a point in the edge profile, and is stored clockwise along the profile. The smallest abscissa X can be found in the arrayminMaximum abscissa XmaxMinimum ordinate YminAnd maximum ordinate YmaxDetermining the expansion diameter of the newly-mixed self-compacting concrete in each frame of slump expansion image, namely the straight-line distance from the left edge point to the right edge point of the edge profile of the newly-mixed self-compacting concrete; then, the expansion diameter of the newly-mixed self-compacting concrete in each frame of slump expansion image corresponds to the time point of the frame, so that a transformation relation that the expansion diameter is continuously increased along with the lapse of the expansion time, namely a relation curve (namely an SF-t curve) of the expansion time and the expansion diameter can be obtained, and the method is shown in figure 21; in the coordinate selection, the coordinate selection can be performed according to the boundary points in the horizontal direction and the vertical direction in the Mask image of the freshly mixed self-compacting concrete, as shown in fig. 22. Further, the flow time T can be obtained500And a maximum expanded diameter mini-SF; wherein the flow time T500The slump expansion test method is characterized in that in a slump expansion test, the slump expansion test method is a time period used for lifting a slump expansion cylinder until the slump expansion of a newly-mixed self-compacting concrete mixture reaches 500 mm; the maximum spreading diameter mini-SF is the spreading diameter corresponding to the last time point in the above relation curve, i.e., the SF value.
From the above description, the method for detecting the working performance of the freshly mixed self-compacting concrete can identify the outline of each corrected slump expansion image frame to obtain the concrete performance indexes, namely the flowing time and the maximum expansion diameter.
Referring to fig. 7, the concrete performance indicators include: and (3) the segregation degree, the contour recognition is carried out on the corrected slump expansion image of the last frame, and the concrete performance index is obtained, and the method comprises the following steps:
s701: carrying out contour recognition on the corrected slump expansion image of the last frame based on the HSV value range of the image;
s702: and obtaining the segregation degree according to the contour recognition result.
It can be understood that, in the embodiments of the present application, loading the corrected final frame of slump expansion image into the background elimination model can remove the background in each frame of slump expansion image, where the background includes but is not limited to the bottom plate, so as to obtain a Mask image only containing freshly mixed self-compacting concrete corresponding to the final frame of slump expansion image.
In order to perform contour identification on the final frame of slump expansion image, in the embodiment of the application, firstly, a color value of each pixel point in the image in an HSV color space needs to be obtained, so that an area where newly-mixed self-compacting concrete is located is identified according to the color value, and specific steps can be seen in S901-S902; then, the area is divided into 60mm × 60mm grids; the area of each grid, i.e., 3600mm2 and the number of stones contained in each grid, is then calculated. When the number of stones contained in each grid is calculated, the contour of the stones is firstly divided by using a watershed algorithm, the mass center of each contour and the surface area of the stones exposed out of the slurry are calculated, the number of the mass centers can be used for obtaining the number of the stones, and meanwhile, the separation degree can be better judged by observing the surface area of the stones exposed out of the slurry. The number of stones and the surface area of the exposed slurry of the stones may be collectively referred to herein as stone characteristic parameters. After the number of stones is obtained, the distribution rate of the stones, that is, the ratio of the number of stones to the area of the area, can be calculated, and the segregation degree of the freshly mixed self-compacting concrete is determined according to the distribution rate of the stones, and the specific steps can be referred to from S1001 to S1002.
In the embodiment of the present application, the judgment criterion of the segregation degree may be performed according to the following table:
the calculation formula of the average value of the stone distribution rate in the embodiment of the application can be as follows:
where α represents a stone distribution ratio average value, m represents the number of stones in each divided region, and S represents the area of each divided region, and is a constant, 3600mm in the embodiment of the present application2And n represents the number of grids in the area where the whole freshly mixed self-compacting concrete is located.
From the above description, the method for detecting the working performance of the freshly mixed self-compacting concrete can identify the outline of each corrected slump expansion image frame to obtain the concrete performance index, namely the segregation degree.
Referring to fig. 8, the contour recognition of each frame of the corrected slump-expansion image based on the gray value of the image includes:
s801: calculating the similarity between the pixel points according to the gray value of each pixel point in each frame of slump expansion image;
s802: and establishing a contour formed by all the pixel points with the similarity within a preset range.
From the above description, the working performance detection method for the freshly mixed self-compacting concrete can perform contour identification on each frame of corrected slump expansion images based on the gray values of the images.
Referring to fig. 9, contour recognition is performed on the corrected slump-expansion image of the last frame based on the HSV value range of the image, including:
s901: obtaining the HSV value range of the final frame slump expansion degree image according to the HSV value of each pixel point in the final frame slump expansion degree image;
s902: and carrying out contour recognition on the slump and expansion image of the last frame according to the HSV value range of the last frame.
From the above description, the method for detecting the working performance of the freshly mixed self-compacting concrete can perform contour recognition on the corrected slump expansion image of the last frame based on the HSV value range of the image.
Referring to fig. 10, obtaining the segregation degree according to the contour recognition result includes:
s1001: carrying out grid division in the outline area to respectively obtain the quantity of stones in each grid;
s1002: and calculating the segregation degree according to the quantity of stones in each grid.
From the above description, the working performance detection method for the freshly-mixed self-compacting concrete provided by the application can obtain the segregation degree according to the outline recognition result.
Based on the same inventive concept, the embodiment of the present application further provides a device for detecting the performance of freshly mixed self-compacting concrete, which can be used to implement the method described in the above embodiments, as described in the following embodiments. The principle of the device for detecting the working performance of the freshly-mixed self-compacted concrete is similar to that of the method for detecting the working performance of the freshly-mixed self-compacted concrete, so the implementation of the device for detecting the working performance of the freshly-mixed self-compacted concrete can refer to the implementation of a software performance reference determination method, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
Referring to fig. 11, in order to obtain the flow time, the maximum expansion diameter and the segregation degree of the fresh self-compacting concrete, thereby detecting the working performance of the fresh self-compacting concrete, the application provides a device for detecting the working performance of the fresh self-compacting concrete, which comprises: a correction unit 1101 and an index determination unit 1102.
A correcting unit 1101 configured to correct the acquired each frame slump-spread image;
and an index determining unit 1102, configured to perform contour recognition on each frame of corrected slump expansion images to obtain a concrete performance index.
Referring to fig. 12, the correction unit 1101 includes: a contour detection module 1201, a contour coordinate determination module 1202, a corner point coordinate determination module 1203, and a correction module 1204.
A contour detection module 1201, configured to perform edge detection on each frame of the obtained slump expansion image to obtain edge contour images of all objects in the corresponding frame;
a contour coordinate determination module 1202, configured to search a bottom plate edge contour image from edge contour images of all objects in the frame, and determine contour coordinate information of the bottom plate edge contour image;
a corner point coordinate determining module 1203, configured to generate corresponding bottom plate corner point coordinate information according to the contour coordinate information;
and a correcting module 1204, configured to correct the slump-expansion image of each frame according to the bottom plate corner coordinate information.
Referring to fig. 13, the concrete performance indicators include: a flow time and a maximum expanded diameter, and the index determination unit 1102 includes: a per frame outline identification module 1301 and a time diameter determination module 1302.
A per-frame contour recognition module 1301, configured to perform contour recognition on each frame of corrected slump expansion image based on a gray value of the image;
and a time diameter determining module 1302, configured to generate a relation curve between the expansion time and the expansion diameter according to the contour recognition result, so as to obtain the flow time and the maximum expansion diameter.
Referring to fig. 14, the concrete performance indicators include: the index determination unit 1102, which determines the degree of segregation, includes: a last frame contour identification module 1401 and an isolation degree determination module 1402.
A last frame contour identification module 1401, configured to perform contour identification on the corrected last frame slump expansion image based on the HSV value range of the image;
and an isolation degree determining module 1402, configured to obtain the isolation degree according to the contour recognition result.
Referring to fig. 15, the each frame contour recognition module 1301 includes: a similarity determination submodule 1501 and a per frame contour identification submodule 1502.
The similarity determination submodule 1501 is used for calculating the similarity between the pixels according to the gray value of each pixel in each frame of slump expansion image;
each frame contour identification submodule 1502 is configured to establish a contour formed by each pixel point whose similarity is within a preset range.
Referring to fig. 16, the last frame contour recognition module 1401 includes: a last frame value range determination sub-module 1601 and a last frame contour identification sub-module 1602.
The last frame numerical range determining submodule 1601 is used for obtaining an HSV numerical range of the last frame slump expansion image according to the HSV numerical value of each pixel point in the obtained last frame slump expansion image;
and a last frame contour identification submodule 1602, configured to perform contour identification on the last frame slump expansion image according to the last frame HSV value range.
Referring to fig. 17, the segregation degree determining module 1402 includes: stone characteristic determination submodule 1701 and segregation degree determination submodule 1702.
A stone characteristic determination submodule 1701 for performing mesh division in the contour region to obtain stone characteristic parameters in each mesh; the stone characteristic parameters comprise: the number of stones and the surface area of the stones exposed out of the slurry;
a segregation degree determining submodule 1702 for calculating the segregation degree according to the stone characteristic parameter in each grid.
From the hardware aspect, in order to obtain the flow time, the maximum expansion diameter and the segregation degree of the freshly mixed self-compacting concrete, thereby detecting the working performance of the freshly mixed self-compacting concrete, the application provides an embodiment of an electronic device for realizing all or part of the contents in the method for detecting the working performance of the freshly mixed self-compacting concrete, wherein the electronic device specifically comprises the following contents:
a Processor (Processor), a Memory (Memory), a communication Interface (Communications Interface) and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the newly-mixed self-compacting concrete working performance detection device and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiment of the method for detecting the working performance of the freshly mixed self-compacting concrete and the embodiment of the device for detecting the working performance of the freshly mixed self-compacting concrete in the embodiment, and the contents thereof are incorporated herein, and repeated details are not repeated herein.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the method for detecting the working performance of the freshly mixed self-compacting concrete can be performed on the electronic device side as described above, or all the operations can be performed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.
The client device may have a communication module (i.e., a communication unit), and may be in communication connection with a remote server to implement data transmission with the server. The server may include a server on the side of the task scheduling center, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
Fig. 18 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 18, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 18 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the method for detecting the working performance of the freshly mixed self-compacting concrete can be integrated into the central processor 9100. The central processor 9100 may be configured to control as follows:
s401: correcting the acquired image of each frame of slump expansion degree;
s402: and carrying out contour recognition on each frame of corrected slump expansion image to obtain a concrete performance index.
From the above description, the working performance detection method for the freshly-mixed self-compacting concrete provided by the application can detect the working performance of the freshly-mixed self-compacting concrete by calculating the flowing time, the maximum expansion diameter and the segregation degree of the freshly-mixed self-compacting concrete.
In another embodiment, the working performance detection device of the freshly mixed self-compacting concrete can be configured separately from the central processing unit 9100, for example, the working performance detection device of the freshly mixed self-compacting concrete of the data composite transmission device can be configured as a chip connected with the central processing unit 9100, and the function of the working performance detection method of the freshly mixed self-compacting concrete can be realized through the control of the central processing unit.
As shown in fig. 18, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 18; further, the electronic device 9600 may further include a component not shown in fig. 18, and reference may be made to the related art.
As shown in fig. 18, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless lan module, may be disposed in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the method for detecting the working performance of the fresh mix self-compacting concrete with the execution subject being the server or the client in the foregoing embodiments, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program implements all the steps in the method for detecting the working performance of the fresh mix self-compacting concrete with the execution subject being the server or the client in the foregoing embodiments, for example, when the processor executes the computer program, the processor implements the following steps:
s401: correcting the acquired image of each frame of slump expansion degree;
s402: and carrying out contour recognition on each frame of corrected slump expansion image to obtain a concrete performance index.
From the above description, the working performance detection method for the freshly-mixed self-compacting concrete provided by the application can detect the working performance of the freshly-mixed self-compacting concrete by calculating the flowing time, the maximum expansion diameter and the segregation degree of the freshly-mixed self-compacting concrete.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, 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 invention.
Claims (12)
1. A method for detecting the working performance of freshly mixed self-compacting concrete is characterized by comprising the following steps:
correcting the acquired image of each frame of slump expansion degree;
carrying out contour recognition on each frame of corrected slump expansion image to obtain a concrete performance index; the concrete performance indicators include flow time, maximum expansion diameter, and segregation degree.
2. The method for detecting the working performance of the fresh-mix self-compacting concrete according to claim 1, wherein the step of correcting the obtained slump expansion image of each frame comprises the following steps:
performing edge detection on each frame of the obtained slump expansion image to obtain edge contour images of all objects in the corresponding frame;
searching a bottom plate edge contour image from the edge contour images of all objects in the frame, and determining contour coordinate information of the bottom plate edge contour image;
generating corresponding bottom plate corner point coordinate information according to the contour coordinate information;
and correcting the slump expansion images of each frame according to the coordinate information of the corner points of the bottom plate.
3. The method for detecting the working performance of the freshly mixed self-compacting concrete as claimed in claim 1, wherein the concrete performance index comprises: the method comprises the following steps of carrying out flow time and maximum expansion diameter, carrying out outline identification on each frame of corrected slump expansion image to obtain a concrete performance index, and comprising the following steps:
carrying out contour recognition on each frame of corrected slump expansion images based on the gray value of the images;
and generating a relation curve of the expansion time and the expansion diameter according to the contour recognition result to obtain the flow time and the maximum expansion diameter.
4. The method for detecting the working performance of the freshly mixed self-compacting concrete as claimed in claim 1, wherein the concrete performance index comprises: and (3) the segregation degree, the contour recognition is carried out on the corrected slump expansion image of the last frame, and the concrete performance index is obtained, and the method comprises the following steps:
carrying out contour recognition on the corrected slump expansion image of the last frame based on the HSV value range of the image;
and obtaining the segregation degree according to the contour recognition result.
5. The method for detecting the working performance of the freshly mixed self-compacting concrete as claimed in claim 4, wherein the obtaining of the segregation degree according to the contour recognition result comprises:
carrying out mesh division in the outline area to respectively obtain stone characteristic parameters in each mesh; the stone characteristic parameters comprise: the number of stones and the surface area of the stones exposed out of the slurry;
and calculating the segregation degree according to the stone characteristic parameters in each grid.
6. The utility model provides a freshly mixed self-compaction concrete working property detection device which characterized in that includes:
the correction unit is used for correcting the acquired each frame slump expansion degree image;
the index determining unit is used for carrying out contour recognition on each frame of corrected slump expansion images to obtain concrete performance indexes; the concrete performance indicators include flow time, maximum expansion diameter, and segregation degree.
7. The apparatus for detecting the workability of fresh self-compacting concrete according to claim 6, wherein the correction unit includes:
the contour detection module is used for carrying out edge detection on the obtained each frame slump expansion degree image to obtain edge contour images of all objects in the corresponding frame;
the contour coordinate determination module is used for searching a bottom plate edge contour image from the edge contour images of all objects in the frame and determining contour coordinate information of the bottom plate edge contour image;
the corner point coordinate determination module is used for generating corresponding bottom plate corner point coordinate information according to the contour coordinate information;
and the correcting module is used for correcting the slump expansion images of each frame according to the coordinate information of the corner points of the bottom plate.
8. The apparatus of claim 6, wherein the concrete performance index comprises: flow time and maximum extension diameter, the index determination unit includes:
each frame of contour recognition module is used for carrying out contour recognition on each frame of corrected slump expansion images based on the gray value of the images;
and the time diameter determining module is used for generating a relation curve of the expansion time and the expansion diameter according to the contour recognition result to obtain the flow time and the maximum expansion diameter.
9. The apparatus of claim 6, wherein the concrete performance index comprises: a degree of segregation, the index determination unit comprising:
the last frame contour identification module is used for carrying out contour identification on the corrected last frame slump expansion image based on the HSV value range of the image;
and the segregation degree determining module is used for obtaining the segregation degree according to the contour recognition result.
10. The apparatus of claim 9, wherein the segregation degree determining module comprises:
the stone characteristic determination submodule is used for carrying out grid division in the outline area to respectively obtain stone characteristic parameters in each grid; the stone characteristic parameters comprise: the number of stones and the surface area of the stones exposed out of the slurry;
and the segregation degree determining submodule is used for calculating the segregation degree according to the stone characteristic parameters in each grid.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for detecting the workability of fresh self-compacting concrete according to any one of claims 1 to 5 when executing the program.
12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for detecting the workability of fresh self-compacting concrete according to any one of claims 1 to 5.
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