CN112489071A - Pointer water meter identification method and system - Google Patents
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- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F15/00—Details of, or accessories for, apparatus of groups G01F1/00 - G01F13/00 insofar as such details or appliances are not adapted to particular types of such apparatus
- G01F15/06—Indicating or recording devices
- G01F15/061—Indicating or recording devices for remote indication
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Abstract
The technical scheme of this application provides a hanging pointer type water gauge identification system, and this system adopts artificial intelligence algorithm to discern the water gauge flowmeter numerical value that shows on the dial plate, then, gives the server with this count value passback to make the water gauge business of checking meter no longer rely on manual work, very big improvement efficiency. Meanwhile, the method for identifying the numerical value of the pointer type water meter flowmeter based on the artificial intelligence algorithm is further arranged, wherein the scale numerical value in the dial plate is ingeniously identified by the artificial intelligence algorithm, and the identification and reading of the pointer are carried out by other modes with simple and efficient calculation modes, so that the efficiency and the accuracy are both considered.
Description
Technical Field
The application relates to the technical field of remote meter reading, in particular to a pointer type water meter identification method and system.
Background
The current manual visual reading of water meters is a time-consuming, inefficient and expensive data acquisition method; it is also time consuming and expensive to replace or upgrade a water meter to achieve automatic collection and communication of data. The camera is used for collecting the instrument image in real time, image processing and pattern recognition technology is applied, and real-time monitoring of the instrument by using a computer instead of manpower becomes possible and is becoming a mainstream trend.
However, how to ensure the accuracy of the identification result of the pointer type water meter becomes a technical problem that needs to be improved at present.
Disclosure of Invention
In order to solve the technical problem, the application provides a pointer type water meter identification method and system.
The first aspect of the application provides a pointer type water meter identification method, the method includes:
s1, shooting a pointer type water meter dial by a camera of the water meter reading equipment to obtain a water meter dial image, and sending the water meter dial image to a processor;
and S2, the processor calls an artificial intelligence algorithm to identify the water meter dial to obtain the identification result of the water meter flow count value.
Preferably, before the processor invokes an artificial intelligence algorithm to identify the water meter dial, the method further includes the following steps:
and binarizing the water meter dial image, performing connected domain analysis on the binarized image through a morphological expansion algorithm, and taking an area defined by the identified regular boundary of the connected domain as an effective area of the dial.
Preferably, the binarization processing is performed by:
s11, carrying out gray processing on the water meter dial image;
s12, calculating the maximum value pray _ max of gray values in all pixel points in the whole image area after the graying processing;
s13, comparing the gray values pray of all the pixel pointsijAnd decision threshold value TijComparing, and outputting a binarization result P of the pixel point based on the following formula:
wherein, i and j are horizontal and longitudinal values of the pixel points in the image and are used for positioning a single pixel point;
and S14, repeating the step S13 until all the pixel points are binarized, and further obtaining a binarized image.
Preferably, the step S2 specifically includes the following steps:
s20, copying the binary image of the effective area of the dial plate to obtain two binary images;
s21, extracting a pointer and each scale mark from one of the binary images through Hough transformation or a difference image method;
s22, identifying the other binary image by adopting a connected domain method to obtain a digital area, and identifying the number in the digital area by adopting a digital identification algorithm;
s23, judging whether the pointer is overlapped with the scale mark, if so, executing a step S24, otherwise, executing a step S25;
s24, taking the indication value of the scale mark as the reading value of the instrument;
s25, calculating the distance M between the pointer and the indication value of the left scale mark, and calculating the distance M between the pointer and the indication value of the right scale mark;
s26, determining whether the dial scale value is in clockwise layout or anticlockwise layout based on the numbers at the two ends of the digital area;
s27, if the layout is clockwise, executing the step S28, otherwise executing the step S29;
s28, the reading value of the instrument is V ═ M + (N-M) d/L;
s29, the reading value of the instrument is V ═ N + (M-N) d/L;
where d is the average distance from all points on the pointer to the left scale line and L is the average distance from all points on the pointer to the right scale line.
Preferably, the step S22 of recognizing the numbers in the number area by using a number recognition algorithm includes:
s221, receiving the character images;
s222, identifying the character image, and extracting statistical features and structural features; the statistical characteristics comprise coarse grid characteristics and projection characteristics, and the structural characteristics comprise concave-convex characteristics and contour boundaries;
s223, optimally combining the statistical characteristics and the structural characteristics, and inputting the optimally combined statistical characteristics and structural characteristics into a neural network classifier;
s224, the neural network classifier classifies digital characters based on the input statistical characteristics and structural characteristic combinations and outputs classification results; the neural network classifier comprises a first-stage classifier and a second-stage classifier, wherein the first-stage classifier classifies 0, 1 and 7, and the second-stage classifier classifies other numbers.
Preferably, the second-stage classifier adopts a neural network model with a four-layer structure, and the excitation function of the neural network model is as follows:
wherein λ is weight, ujIs the state value of the jth neuron, xiInputting the state value of the ith neuron for the preceding stage, wijInputting weights, θ, of i-th to j-th neurons for preceding stagesjIs the threshold of the neuron.
Preferably, before step S2, the method further includes: and acquiring a set number of single digital images for pre-training of the neural network classifier, and determining the optimal characteristic combination for classification of each digital character through pre-training.
A second aspect of the application provides a pointer type water meter identification system, which comprises an externally-hung water meter reading device and a server, wherein the water meter reading device comprises a camera, a processor and a communication module;
the camera is used for shooting a pointer type water meter dial to obtain a water meter dial image and sending the water meter dial image to the processor;
the processor is used for calling an artificial intelligence algorithm to identify the water meter dial to obtain an identification result of the water meter flow counting value;
and the communication module is used for sending the identification result of the flow counting value of the water meter to a server.
A third aspect of the present application provides a pointer-type water meter recognition apparatus, the apparatus including:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the pointer water meter identification method.
A fourth aspect of the present application provides a storage medium, wherein the storage medium stores computer instructions, and when the computer instructions are called, the computer instructions are used to execute the method for identifying a pointer water meter according to any one of the preceding items.
The invention has the beneficial effects that:
the technical scheme of this application provides a hanging pointer type water gauge identification system, and this system adopts artificial intelligence algorithm to discern the water gauge flowmeter numerical value that shows on the dial plate, then, gives the server with this count value passback to make the water gauge business of checking meter no longer rely on manual work, very big improvement efficiency. Meanwhile, the method for identifying the numerical value of the pointer type water meter flowmeter based on the artificial intelligence algorithm is further arranged, wherein the scale numerical value in the dial plate is ingeniously identified by the artificial intelligence algorithm, and the identification and reading of the pointer are carried out by other modes with simple and efficient calculation modes, so that the efficiency and the accuracy are both considered.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flow chart of a natural grassland grass storage balance assessment method based on Beidou navigation, disclosed in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a natural grassland grass storage balance evaluation system based on Beidou navigation, disclosed in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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 some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. 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.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present application, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings or the orientation or positional relationship which the present invention product is usually put into use, it is only for convenience of describing the present application and simplifying the description, but it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and be operated, and thus, should not be construed as limiting the present application.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a method for identifying a pointer-type water meter according to an embodiment of the present application. As shown in fig. 1, a pointer type water meter identification method according to an embodiment of the present application includes:
s1, shooting a pointer type water meter dial by a camera of the water meter reading equipment to obtain a water meter dial image, and sending the water meter dial image to a processor;
and S2, the processor calls an artificial intelligence algorithm to identify the water meter dial to obtain the identification result of the water meter flow count value.
In this application embodiment, this application has set up outer hanging water gauge equipment of checking meter, only needs simple equipment can realize the long-range work of checking meter to pointer-type water gauge, need not to reform transform the water gauge on a large scale, the realization cost that can very big reduction long-range checking meter. In recent years, Artificial Intelligence (AI) has been rapidly developed and widely used, and achieved fruitful results. In view of the technical characteristics of accuracy, rapidness, learning evolution and the like of an artificial intelligence technology in the aspect of computer vision identification processing, the technical scheme of the application adopts an artificial intelligence algorithm to identify the dial plate of the pointer type water meter, and can obviously improve the identification accuracy.
In an alternative embodiment, before the processor invokes the artificial intelligence algorithm to identify the water meter face, the method further includes the following steps:
and binarizing the water meter dial image, performing connected domain analysis on the binarized image through a morphological expansion algorithm, and taking an area defined by the identified regular boundary of the connected domain as an effective area of the dial.
In the embodiment of the application, due to various factors such as irregular installation, movement and irregular dial of the water meter, images shot by the camera cannot completely contain the counting part of the dial, and therefore, the images of the dial need to be preprocessed before being recognized. That is, the dial image is binarized, and then the connected domain having a regular boundary (the dial is usually in a regular shape, for example, a circle, a square, an ellipse, etc.) is recognized as the effective area of the dial.
It is determined as an optional implementation manner that the binarization processing is performed by the following steps:
s11, carrying out gray processing on the water meter dial image;
s12, calculating the maximum value pray _ max of gray values in all pixel points in the whole image area after the graying processing;
s13, comparing the gray values pray of all the pixel pointsijAnd decision threshold value TijComparing, and outputting a binarization result P of the pixel point based on the following formula:
wherein, i and j are horizontal and longitudinal values of the pixel points in the image and are used for positioning a single pixel point;
and S14, repeating the step S13 until all the pixel points are binarized, and further obtaining a binarized image.
It is determined that an optional implementation manner is provided, and the step S2 specifically includes the following steps:
s20, copying the binary image of the effective area of the dial plate to obtain two binary images;
s21, extracting a pointer and each scale mark from one of the binary images through Hough transformation or a difference image method;
s22, identifying the other binary image by adopting a connected domain method to obtain a digital area, and identifying the number in the digital area by adopting a digital identification algorithm;
s23, judging whether the pointer is overlapped with the scale mark, if so, executing a step S24, otherwise, executing a step S25;
s24, taking the indication value of the scale mark as the reading value of the instrument;
s25, calculating the distance M between the pointer and the indication value of the left scale mark, and calculating the distance M between the pointer and the indication value of the right scale mark;
s26, determining whether the dial scale value is in clockwise layout or anticlockwise layout based on the numbers at the two ends of the digital area;
s27, if the layout is clockwise, executing the step S28, otherwise executing the step S29;
s28, the reading value of the instrument is V ═ M + (N-M) d/L;
s29, the reading value of the instrument is V ═ N + (M-N) d/L;
where d is the average distance from all points on the pointer to the left scale line and L is the average distance from all points on the pointer to the right scale line.
In the embodiment of the application, reading identification of the pointer instrument is mostly realized according to the deflection angle of the pointer at present, although the method is simple in calculation, the error of the identification result is large, and the method can only be used for occasions with low requirement on the error. According to the technical scheme, the real reading value is obtained based on the distance between the pointer and the left and right scale marks, and the calculation accuracy is high. Meanwhile, the condition that different dial scales are arranged clockwise or anticlockwise is also considered, and the dial scale has higher applicability.
As an alternative embodiment, the step S22 of recognizing the numbers in the number area by using a number recognition algorithm includes:
s221, receiving the character images;
s222, identifying the character image, and extracting statistical features and structural features; the statistical characteristics comprise coarse grid characteristics and projection characteristics, and the structural characteristics comprise concave-convex characteristics and contour boundaries;
s223, optimally combining the statistical characteristics and the structural characteristics, and inputting the optimally combined statistical characteristics and structural characteristics into a neural network classifier;
s224, the neural network classifier classifies digital characters based on the input statistical characteristics and structural characteristic combinations and outputs classification results; the neural network classifier comprises a first-stage classifier and a second-stage classifier, wherein the first-stage classifier classifies 0, 1 and 7, and the second-stage classifier classifies other numbers.
In the embodiment of the application, the neural network algorithm is one of ten artificial intelligence algorithms, and has a self-learning function, for example, when image recognition is realized, the network can slowly learn to recognize similar images through the self-learning function only by inputting a plurality of different image templates and corresponding recognition results into the neural network in advance. Based on the characteristics of the neural network, the technical scheme of the application utilizes the artificial intelligence algorithm to identify the scale numbers in the dial plate of the pointer type water meter.
In an alternative embodiment, the second-stage classifier employs a four-layer neural network model, and the excitation function of the neural network model is:
wherein λ is weight, ujIs the state value of the jth neuron, xiInputting the state value of the ith neuron for the preceding stage, wijInputting weights, θ, of i-th to j-th neurons for preceding stagesjIs the threshold of the neuron.
As an optional implementation, before step S2, the method further includes: and acquiring a set number of single digital images for pre-training of the neural network classifier, and determining the optimal characteristic combination for classification of each digital character through pre-training.
In the embodiment of the application, the artificial intelligence algorithm is not adopted to identify the scale numbers in the dial plate of the pointer type water meter in an omnibearing manner, the artificial intelligence algorithm is skillfully adopted during identification of the scale numbers in the dial plate, and other modes with simple and efficient calculation modes are adopted for identification of the pointer and reading of the reading values, so that the efficiency and the accuracy can be achieved.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a pointer type water meter identification system disclosed in the embodiment of the present application. As shown in fig. 2, a pointer type water meter identification system according to an embodiment of the present application includes an external water meter reading device and a server, where the water meter reading device includes a camera, a processor, and a communication module;
the camera is used for shooting a pointer type water meter dial to obtain a water meter dial image and sending the water meter dial image to the processor;
the processor is used for calling an artificial intelligence algorithm to identify the water meter dial to obtain an identification result of the water meter flow counting value;
and the communication module is used for sending the identification result of the flow counting value of the water meter to a server.
EXAMPLE III
The embodiment of the application provides a pointer type water gauge identification equipment, equipment includes:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the pointer water meter identification method in any one of the first embodiment.
Example four
The embodiment of the application provides a storage medium, which is characterized in that the storage medium stores a computer instruction, and the computer instruction is used for executing the pointer type water meter identification method in the first embodiment when being called.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A pointer type water meter identification method is characterized in that: the method comprises the following steps:
s1, shooting a pointer type water meter dial by a camera of the water meter reading equipment to obtain a water meter dial image, and sending the water meter dial image to a processor;
and S2, the processor calls an artificial intelligence algorithm to identify the water meter dial to obtain the identification result of the water meter flow count value.
2. The method of claim 1, wherein: before the processor calls an artificial intelligence algorithm to identify the water meter dial, the method further comprises the following steps:
and binarizing the water meter dial image, performing connected domain analysis on the binarized image through a morphological expansion algorithm, and taking an area defined by the identified regular boundary of the connected domain as an effective area of the dial.
3. The method of claim 2, wherein: the binarization processing is carried out by the following steps:
s11, carrying out gray processing on the water meter dial image;
s12, calculating the maximum value pray _ max of gray values in all pixel points in the whole image area after the graying processing;
s13, comparing the gray values pray of all the pixel pointsijAnd decision threshold value TijComparing, and outputting a binarization result P of the pixel point based on the following formula:
wherein, i and j are horizontal and longitudinal values of the pixel points in the image and are used for positioning a single pixel point;
and S14, repeating the step S13 until all the pixel points are binarized, and further obtaining a binarized image.
4. The method of claim 1, wherein: the step S2 specifically includes the following steps:
s20, copying the binary image of the effective area of the dial plate to obtain two binary images;
s21, extracting a pointer and each scale mark from one of the binary images through Hough transformation or a difference image method;
s22, identifying the other binary image by adopting a connected domain method to obtain a digital area, and identifying the number in the digital area by adopting a digital identification algorithm;
s23, judging whether the pointer is overlapped with the scale mark, if so, executing a step S24, otherwise, executing a step S25;
s24, taking the indication value of the scale mark as the reading value of the instrument;
s25, calculating the distance M between the pointer and the indication value of the left scale mark, and calculating the distance M between the pointer and the indication value of the right scale mark;
s26, determining whether the dial scale value is in clockwise layout or anticlockwise layout based on the numbers at the two ends of the digital area;
s27, if the layout is clockwise, executing the step S28, otherwise executing the step S29;
s28, the reading value of the instrument is V ═ M + (N-M) d/L;
s29, the reading value of the instrument is V ═ N + (M-N) d/L;
where d is the average distance from all points on the pointer to the left scale line and L is the average distance from all points on the pointer to the right scale line.
5. The method of claim 4, wherein: in step S22, recognizing the numbers in the number area by using a number recognition algorithm, including:
s221, performing cutting operation on the digital area to obtain a plurality of digital character images;
s222, respectively identifying the digital character images, and extracting statistical features and structural features; the statistical characteristics comprise coarse grid characteristics and projection characteristics, and the structural characteristics comprise concave-convex characteristics and contour boundaries;
s223, optimally combining the statistical characteristics and the structural characteristics, and inputting the optimally combined statistical characteristics and structural characteristics into a neural network classifier;
s224, the neural network classifier classifies digital characters based on the input statistical characteristics and structural characteristic combinations and outputs classification results; the neural network classifier comprises a first-stage classifier and a second-stage classifier, wherein the first-stage classifier classifies 0, 1 and 7, and the second-stage classifier classifies other numbers.
6. The method of claim 1, wherein: the second-stage classifier adopts a neural network model with a four-layer structure, and the excitation function of the neural network model is as follows:
wherein λ is weight, ujIs the state value of the jth neuron, xiInputting the state value of the ith neuron for the preceding stage, wijInputting weights, θ, of i-th to j-th neurons for preceding stagesjIs the threshold of the neuron.
7. The method of claim 6, wherein: before step S2, the method further includes: and acquiring a set number of single digital images for pre-training of the neural network classifier, and determining the optimal characteristic combination for classification of each digital character through pre-training.
8. A pointer type water meter identification system comprises an externally-hung water meter reading device and a server, wherein the water meter reading device comprises a camera, a processor and a communication module;
the camera is used for shooting a pointer type water meter dial to obtain a water meter dial image and sending the water meter dial image to the processor;
the processor is used for calling an artificial intelligence algorithm to identify the water meter dial to obtain an identification result of the water meter flow counting value;
and the communication module is used for sending the identification result of the flow counting value of the water meter to a server.
9. A pointer water meter identification apparatus, the apparatus comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the pointer water meter identification method according to any one of claims 1-7.
10. A storage medium storing computer instructions which, when invoked, perform a method of identifying a pointer water meter as claimed in any one of claims 1 to 7.
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