CN114627287A - Water turbidity detection method and system in air tightness detection based on artificial intelligence - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, in particular to a method and a system for detecting water turbidity in air tightness detection based on artificial intelligence, wherein the method comprises the following steps: acquiring a cylinder wall image of a detection cylinder, and detecting attached bubbles on the cylinder wall to obtain an attached bubble image; clustering the attached bubbles based on the gray level to obtain bubble areas under each gray level category; selecting a sub-area with the largest attached bubble density in each bubble area; acquiring texture characteristic parameters and light and shade contrast characteristic parameters of each subregion, wherein the texture characteristic parameters comprise energy, contrast, correlation and entropy, and the light and shade contrast characteristic parameters are obtained according to the difference value of the grayscale of the subregions and the grayscale of the cylinder wall; selecting a subregion most capable of reacting the turbidity of the water by using an analytic hierarchy process; and detecting the water turbidity according to the texture characteristic parameters and the light and shade contrast characteristic parameters of the selected sub-regions. The invention can obtain more accurate and effective detection results of the water turbidity.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a method and a system for detecting water turbidity in air tightness detection based on artificial intelligence.
Background
The bubble method is one of the most commonly used traditional air tightness detection methods, however, the turbidity of the water body gradually increases over time no matter the bubble method is a direct bubble method or an indirect bubble method. On one hand, the detected device is accompanied by pollutants such as oil stain and dust, on the other hand, the water body carries more and more pollutants, and therefore when the water body reaches a certain turbidity degree, the water needs to be replaced in time to ensure the reliability of a detection result. The traditional water changing period basically depends on subjective judgment of people, so that a plurality of uncertainties can be caused in the judgment of specific turbidity, water resources can be wasted, and the error of a detection result can be increased. The conventional turbidity judgment is generally based on single factors such as brightness value and color tone, and the detection result is inaccurate.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a method and a system for detecting water turbidity in air tightness detection based on artificial intelligence, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a method for detecting water turbidity in air tightness detection based on artificial intelligence, which includes the following specific steps:
acquiring a cylinder wall image of a detection cylinder, and detecting attached bubbles on the cylinder wall to obtain an attached bubble image;
clustering the attached bubbles based on the gray level to obtain bubble areas under each gray level category; selecting a sub-area with the largest attached bubble density in each bubble area;
acquiring texture characteristic parameters and light and dark contrast characteristic parameters of each subregion, wherein the texture characteristic parameters comprise energy, contrast, correlation and entropy, and the light and dark contrast characteristic parameters are obtained according to the difference value of the grayscale of the subregions and the grayscale of the cylinder wall;
selecting a subregion most capable of reacting the turbidity of the water by using an analytic hierarchy process, specifically, a target layer in the hierarchical structure model is the subregion most capable of reacting the turbidity of the water, a criterion layer is a texture characteristic parameter and a light and shade contrast characteristic parameter, and a scheme layer is each subregion;
and detecting the turbidity of the water according to the texture characteristic parameters and the light and shade contrast characteristic parameters of the selected sub-regions.
Further, Canopy clustering is combined with mean clustering to cluster dependent bubbles.
Further, a sub-area with the largest attachment bubble density is selected from each bubble area, specifically:
traversing search is carried out based on the coordinates of pixel points attached to the bubbles in the bubble area, and a maximum convex hull formed by the attached bubbles in the bubble area is obtained; acquiring the mass center of the convex hull, calculating the average distance from the mass center of the convex hull to the mass center of each attached bubble in the bubble area, taking the mass center of any attached bubble in the bubble area as an initial circle center, and taking the average distance as a radius to obtain an initial circle;
acquiring a principal component direction according to a plurality of vectors of the attached bubbles pointing to the initial circle from the initial circle center, wherein the attached bubbles on one side with a larger number of the attached bubbles are taken as bubbles to be traversed on two sides of a straight line which passes through the initial circle center and is vertical to the straight line of the principal component direction; determining a new circle center according to the projection length of a plurality of vectors of the initial circle center pointing to each bubble to be traversed in the principal component direction, and generating a new circle by still taking the distance mean value as the radius;
and continuously generating new circles until the new circles converge to the place where the density of the attached bubbles is maximum, wherein the positions of the circles during convergence are the sub-areas.
Furthermore, the weight of each characteristic parameter can be obtained by utilizing an analytic hierarchy process, and the detection of the water turbidity is carried out by utilizing a fuzzy comprehensive evaluation method based on each characteristic parameter and the corresponding weight of the selected subarea.
Further, the acquiring of the cylinder wall image specifically includes acquiring a front view image of the detection cylinder, capturing a cylinder wall area in the front view image, and obtaining the cylinder wall image through perspective transformation.
Further, a semantic segmentation network is utilized to detect the attached bubbles on the cylinder wall.
In a second aspect, another embodiment of the present invention provides a system for detecting water turbidity in airtight detection based on artificial intelligence, which specifically includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the computer program, when executed by the processor, implements the steps of a method for detecting water turbidity in airtight detection based on artificial intelligence.
The embodiment of the invention at least has the following beneficial effects: the invention can more sensitively and accurately obtain the observation factors reflecting the turbidity degree of the water body; the texture characteristic parameters obtained by the gray level co-occurrence matrix are matched with the light and shade contrast characteristic parameters to be combined with a corresponding algorithm of fuzzy analysis to obtain the process of the water turbidity, main change factors in the process of changing the water turbidity are comprehensively analyzed, the weight values of the factors in the reaction water change are determined, and then a more accurate and effective detection result of the water turbidity is obtained.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic view of a cylinder wall area in an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description will be given to a method and a system for detecting turbidity of water in air tightness detection based on artificial intelligence in accordance with the present invention, in conjunction with the preferred embodiments, the detailed implementation, structure, features and effects thereof. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
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.
The following application scenarios are taken as examples to illustrate the present invention:
the application scene is as follows: the water body turbidity in the air tightness detection cylinder is detected based on a visual perception method, so that managers can be helped to control the water body state in real time and replace the water body with high turbidity in time, specifically, image acquisition can be performed at fixed time under the state that the water body is stable and a camera has a fixed visual angle, and a water turbidity detection result is obtained based on the acquired image, so that the acquisition interval T of the image needs to be set, if T =3, the image acquisition is performed every 3 days, and the specific T value can be flexibly set according to different environments where each air tightness device is located; it should be noted that the detection cylinder in the present invention needs to be a transparent detection cylinder, or a detection cylinder with several transparent sides, so that the device to be detected can be seen through the side of the detection cylinder.
One embodiment of the invention provides a water turbidity detection method in air tightness detection based on artificial intelligence, which comprises the following steps:
and step S1, acquiring a cylinder wall image of the detection cylinder, and detecting attached bubbles on the cylinder wall to obtain an attached bubble image.
(a) In the embodiment, the front view image is as shown in fig. 1, a cylinder wall area is intercepted from the front view image, and a cylinder wall image is obtained through perspective transformation. In the embodiment, a side cylinder wall with the clearest and densest bubbles is selected, the area, above the device to be detected and below the water surface, of the cylinder wall is a cylinder wall area, and the cylinder wall area in the embodiment is an area framed by a black rectangle on the right side in fig. 1.
The perspective transformation is carried out for reducing the error of bubble area caused by different visual angles, specifically, the perspective transformation is the mapping from two-dimensional (X, Y) to three-dimensional (X, Y, Z) and then to another two-dimensional (X ', Y') space, and is based on the transformation of four fixed vertexes of an image, namely four corner points of a cylinder wall area; the perspective transformation is realized by matrix multiplication using one
The specific formula of the matrix is as follows:
and further:
and finally:
wherein the first two rows of the matrix implement linear transformation and translation and the third row is used to implement perspective transformation. Thus, the cylinder wall image obtained through perspective conversion is an orthographic view image.
(b) Preferably, the embodiment utilizes a semantic segmentation network to detect the attachment of bubbles on the cylinder wall.
The semantic segmentation network is an Encoder-Decoder structure, and the specific training content is as follows:
(1) collecting an orthographic view cylinder wall image containing attached bubbles to construct a training data set, labeling the training data set, wherein the attached bubbles are labeled as 1, and the other attached bubbles are labeled as 0. Where 80% of the data was randomly selected as the training set and the remaining 20% as the validation set.
(2) Inputting training images and label data into a semantic segmentation network, extracting image characteristics by an Encoder, and converting the number of channels into the number of categories to obtain a characteristic diagram; then, the height and width of the feature map are converted into the size of the input image by the Decoder, thereby outputting the class of each pixel.
(3) The Loss function is trained using a cross entropy Loss function.
And multiplying the attached bubble segmentation graph obtained by the semantic segmentation network with the cylinder wall image to obtain the attached bubble graph only comprising the attached bubbles.
Step S2, clustering the attached bubbles based on gray level to obtain bubble areas under each gray level category; the sub-region with the highest attached bubble density is selected in each bubble region.
(a) The attached bubble image is converted into a gray image, and due to illumination, the gray levels of all bubbles in the image are inconsistent, so that the method of combining Canopy and mean value clustering is used for classifying the gray levels of the bubbles; the reason for using the Canopy clustering algorithm is that the algorithm does not need to specify the k value (namely, the number of clustering), so that the data can be roughly clustered according to the algorithm, the k value is obtained, then the mean value clustering is used for carrying out fine clustering, and after the clustering is finished, the bubble area under each gray level category is obtained.
(b) Selecting the sub-area with the highest attached bubble density in each bubble area:
for each bubble area, performing traversal search based on the coordinates of pixel points attached to bubbles in the bubble area to obtain a maximum convex hull formed by the attached bubbles in the bubble area; the method comprises the steps of obtaining the mass center of a convex hull, calculating the distance mean value from the mass center of the convex hull to the mass center of each attached bubble in a bubble area, taking the mass center of any attached bubble in the bubble area as an initial circle center, and taking the distance mean value as a radius to obtain an initial circle.
Acquiring a principal component direction according to a plurality of vectors of the attached bubbles pointing to the initial circle from the initial circle center, wherein the attached bubbles on one side with a larger number of the attached bubbles are taken as bubbles to be traversed on two sides of a straight line which passes through the initial circle center and is vertical to the straight line of the principal component direction; and determining a new circle center according to the projection lengths of a plurality of vectors of the initial circle center pointing to each bubble to be traversed in the principal component direction, and generating a new circle by still taking the distance mean value as the radius, namely all the generated circles have the same size. Wherein, a vertical line of a straight line where the main component direction is located is made, and the projection length corresponding to the bubble to be traversed and positioned at the two sides of the vertical line is divided into a positive part and a negative part, namely the projection length has two directions; calculating the mean value of the projection lengths of a plurality of vectors of which the initial circle centers point to each bubble to be traversed in the principal component direction to obtain the average projection length, and simultaneously obtaining the direction of the average projection length so as to obtain the position of the end point of the average projection length, wherein the attached bubble center point closest to the position is a new circle center.
Continuously generating new circles until the new circles converge to the place where the density of the attached bubbles is maximum, wherein the positions of the circles during convergence are the sub-areas; after multiple iterations, the position of the new circle center is not changed, and the circle area obtained based on the circle center is the sub-area with the maximum attachment bubble density.
Thus, the sub-area with the largest attached bubble density in each bubble area can be obtained, and the sub-area is circular.
Step S3, acquiring texture characteristic parameters and light and shade contrast characteristic parameters of each sub-region, wherein the texture characteristic parameters comprise energy, contrast, correlation and entropy, and the light and shade contrast characteristic parameters are obtained according to the difference value of the gray scale of the sub-region and the gray scale of the cylinder wall; and selecting the sub-region which can most react to the turbidity of the water by utilizing an analytic hierarchy process, specifically, selecting the sub-region which can most react to the turbidity of the water by a target layer in the hierarchical structure model, selecting the standard layer as a texture characteristic parameter and a light and shade contrast characteristic parameter, and selecting the scheme layer as each sub-region.
(a) For each sub-region, calculating a corresponding gray level co-occurrence matrix, and then obtaining partial characteristic values of the matrix through calculating the gray level co-occurrence matrix to respectively represent the texture characteristics of the image; the reason for using the gray level co-occurrence matrix is that the gray level co-occurrence matrix can comprehensively reflect the comprehensive information of the image about the direction, the adjacent interval, the change amplitude and the like, so that the change condition of the water body can be more accurately reflected in a subsequent fuzzy analysis method.
In the embodiment, four most common characteristic parameters of the gray level co-occurrence matrix are selected to extract the texture characteristics of the image, and the four characteristics and the meanings represented by the four characteristics are respectively as follows:
an energy γ; the energy is the sum of squares of each element of the gray level co-occurrence matrix, also called as angle second moment, is the measurement of the uniform change of the gray level of the image texture, and reflects the uniform degree of the gray level distribution of the image and the thickness degree of the texture.
A contrast ε; the contrast is the moment of inertia near the main diagonal of the gray level co-occurrence matrix, which reflects how the values of the matrix are distributed and reflects the definition of the image and the depth of texture grooves.
A degree of correlation ϵ; the correlation represents the similarity of the elements of the space gray level co-occurrence matrix in the row or column direction, and reflects the correlation of the local gray level of the image.
The entropy σ; the entropy reflects the randomness of image texture, and if all values in the gray level co-occurrence matrix are equal, the maximum value is obtained; if the values in the co-occurrence matrix are not uniform, the values become small.
In addition to the four characteristic parameters obtained by the gray level co-occurrence matrix, the light and shade contrast characteristic parameter mu is used for representing the light and shade contrast of the bubbles in the density circle region and the cylinder wall background on the gray level, because the more obvious the light and shade contrast is, the easier the change caused by the turbidity of the water body is observed, however, in the process that the water body gradually becomes turbid, the gray level of the bubbles and the gray level of the background can gradually become regional and integrated, and finally the difference is difficult; and obtaining the light and shade contrast characteristic parameters according to the difference absolute value of the gray level mean value of the sub-area and the gray level mean value of the cylinder wall.
(b) The embodiment utilizes an analytic hierarchy process to select a subregion which can most reflect the turbidity of water, and performs weight analysis on five characteristic parameters, specifically:
establishing a hierarchical structure model: the target layer is a subregion which can most reflect the turbidity of the water, the criterion layer is influencing factors, namely texture characteristic parameters and light and shade contrast characteristic parameters, namely energy, contrast, correlation, entropy and light and shade contrast characteristic parameters, and the scheme layer is each subregion.
Constructing a comparison judgment matrix: when the weight between the factors of each layer is determined, the analysis and determination are carried out through the comparison between every two factors.
And (3) checking the hierarchical single ordering and the consistency thereof: and determining whether the sorting weight of the same-level factor to the relative importance of a certain factor in the previous-level factor is correct and reasonable.
And (3) checking the total hierarchical ordering and the consistency thereof: and calculating whether the weight of all factors of a certain layer to the relative importance of the highest layer is reasonable.
Because the weight occupied by analyzing each factor to reflect the change of the turbidity of the water body is not determined, and the analytic hierarchy process is used for solving the problems, the invention can obtain the weight of each area density circle in the process when the turbidity of the water body is changed by the analytic hierarchy process, and determine the weight coefficient of the corresponding five factors when the turbidity of the water body is changed by the maximum weight of the corresponding subarea.
Therefore, the sub-region which can reflect the turbidity of the water most and the weight corresponding to each characteristic parameter can be obtained.
And step S4, detecting the water turbidity according to the texture characteristic parameters and the light and shade contrast characteristic parameters of the selected sub-area.
The weights of all characteristic parameters can be obtained by using an analytic hierarchy process, and the detection of the water turbidity is carried out by using a fuzzy comprehensive evaluation method based on all characteristic parameters of the selected subareas and the corresponding weights; specifically, four turbidity levels of clear, slight turbidity, moderate turbidity and severe turbidity are set in the embodiment, membership degrees of the turbidity levels can be obtained based on a fuzzy comprehensive evaluation method, the turbidity level of water in the detection cylinder can be obtained according to the maximum value of the membership degrees, and related personnel are reminded to change water when the detection result is moderate turbidity or severe turbidity, so that the air tightness detection result is prevented from generating errors due to water turbidity in subsequent air tightness detection.
Based on the same inventive concept as the method embodiment, an embodiment of the present invention provides an artificial intelligence based water turbidity detection system in air tightness detection, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, realizes the steps of the artificial intelligence based water turbidity detection method in air tightness detection.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A method for detecting water turbidity in air tightness detection based on artificial intelligence is characterized by comprising the following steps:
acquiring a cylinder wall image of a detection cylinder, and detecting attached bubbles on the cylinder wall to obtain an attached bubble image;
clustering the attached bubbles based on the gray level to obtain bubble areas under each gray level category; selecting a sub-area with the largest attached bubble density in each bubble area;
acquiring texture characteristic parameters and light and shade contrast characteristic parameters of each subregion, wherein the texture characteristic parameters comprise energy, contrast, correlation and entropy, and the light and shade contrast characteristic parameters are obtained according to the difference value of the grayscale of the subregions and the grayscale of the cylinder wall;
selecting a subregion most capable of reacting to the turbidity of water by utilizing an analytic hierarchy process, specifically, selecting a target layer in a hierarchical structure model as the subregion most capable of reacting to the turbidity of water, a criterion layer as a texture characteristic parameter and a light and shade contrast characteristic parameter, and a scheme layer as each subregion;
and detecting the water turbidity according to the texture characteristic parameters and the light and shade contrast characteristic parameters of the selected sub-regions.
2. The method of claim 1, wherein Canopy clustering is combined with mean clustering to cluster dependent bubbles.
3. The method according to claim 2, characterized in that the sub-area with the highest attached bubble density is selected in each bubble area, specifically:
traversing search is carried out based on the coordinates of pixel points attached to the bubbles in the bubble area, and a maximum convex hull formed by the attached bubbles in the bubble area is obtained; acquiring the mass center of the convex hull, calculating the average distance from the mass center of the convex hull to the mass center of each attached bubble in the bubble area, taking the mass center of any attached bubble in the bubble area as an initial circle center, and taking the average distance as a radius to obtain an initial circle;
acquiring a principal component direction according to a plurality of vectors of the attached bubbles pointing to the initial circle from the initial circle center, wherein the attached bubbles on one side with a larger number of the attached bubbles are taken as bubbles to be traversed on two sides of a straight line which passes through the initial circle center and is vertical to the straight line of the principal component direction; determining a new circle center according to the projection length of a plurality of vectors of the initial circle center pointing to each bubble to be traversed in the principal component direction, and generating a new circle by still taking the distance mean value as the radius;
and continuously generating new circles until the new circles converge to the place where the density of the attached bubbles is maximum, wherein the positions of the circles during convergence are the sub-areas.
4. The method of claim 3, wherein the weights of the characteristic parameters are obtained by an analytic hierarchy process, and the detection of the turbidity of the water is performed by a fuzzy comprehensive evaluation process based on the characteristic parameters and the corresponding weights of the selected sub-regions.
5. The method as claimed in claim 4, wherein the cylinder wall image is obtained by acquiring an elevation image of the detection cylinder, cutting a cylinder wall area in the elevation image, and obtaining a cylinder wall image through perspective transformation.
6. The method of claim 1, wherein detecting attached bubbles on the cylinder wall is performed using a semantic segmentation network.
7. An artificial intelligence based water turbidity detection system in air tightness detection, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method according to any one of claims 1-6.
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