CN112434693A - Digital water meter identification method and system - Google Patents
Digital water meter identification method and system Download PDFInfo
- Publication number
- CN112434693A CN112434693A CN202011212962.5A CN202011212962A CN112434693A CN 112434693 A CN112434693 A CN 112434693A CN 202011212962 A CN202011212962 A CN 202011212962A CN 112434693 A CN112434693 A CN 112434693A
- Authority
- CN
- China
- Prior art keywords
- water meter
- dial
- image
- neural network
- digital
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 115
- 238000000034 method Methods 0.000 title claims description 29
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 24
- 238000013528 artificial neural network Methods 0.000 claims abstract description 20
- 238000012545 processing Methods 0.000 claims description 15
- 210000002569 neuron Anatomy 0.000 claims description 11
- 238000004891 communication Methods 0.000 claims description 6
- 238000003062 neural network model Methods 0.000 claims description 6
- 238000012549 training Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
- 230000005284 excitation Effects 0.000 claims description 3
- 238000003709 image segmentation Methods 0.000 claims description 3
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 8
- 238000004590 computer program Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 230000001788 irregular Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/63—Scene text, e.g. street names
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/02—Recognising information on displays, dials, clocks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
Abstract
The technical scheme of this application provides a hanging digital 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. Simultaneously, this application has still set up the neural network classifier of digital water gauge flow count value, and wherein, the classifier includes first grade classifier and second grade classifier, and two classifiers are responsible for respectively carrying out categorised discernment to the digit of difference to the efficiency of categorised discernment has effectively been improved.
Description
Technical Field
The application relates to the field of remote meter reading of water meters, in particular to a digital water meter identification method and system.
Background
At present, data acquisition work of many resident water meters and electricity meters is still completed by manual meter reading, and the traditional mode of utilizing manual meter reading needs to consume a large amount of manpower, still has the problem of inaccurate numerical value of gathering because of the error of the personnel of checking meter, can't reach the effect of real-time acquisition, and manual meter reading still needs the homeowner to be in the harsh condition of cooperation at home. With the development of the technology of the internet of things, a remote meter reading service for a traditional non-intelligent water meter becomes a new business field of the internet of things. Meanwhile, digital water meters have been replaced in some residential communities. Therefore, how to realize intelligent remote real-time meter reading of the digital water meter is a technical problem to be solved.
Disclosure of Invention
In order to solve the technical problem of remote meter reading of the digital water meter, the application provides a digital water meter identification method and a digital water meter identification system.
The first aspect of the application provides a digital water meter identification method, which is applied to an external hanging type water meter reading device, and the method comprises the following steps:
s1, shooting a digital 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:
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;
determining a numerical value display area of the water meter dial from the effective area of the dial;
performing image segmentation on the numerical value display area image to obtain a plurality of character images;
the plurality of character images are respectively numbered from left to right.
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.
Preferably, the choice threshold value TijDetermined by the following equation:
wherein, TijA choice threshold corresponding to the pixel point (i, j), wherein lambda is a preset weight and T0The initial value of the threshold value is chosen.
Preferably, the determining the numerical value display area of the water meter dial from the effective area of the dial comprises: and analyzing the identified effective area of the dial plate again, and taking the area defined by the regular boundary of the identified connected area as the numerical value display area of the dial plate of the water meter.
Preferably, the artificial intelligence algorithm is a neural network based algorithm.
Preferably, the processor calls an artificial intelligence algorithm to identify the water meter dial to obtain an identification result of the water meter flow count value, and the identification result includes:
s21, receiving the character images;
s22, recognizing 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;
s23, optimally combining the statistical characteristics and the structural characteristics, and inputting the optimally combined statistical characteristics and structural characteristics into a neural network classifier;
s24, the neural network classifier classifies digital characters based on the input statistical features and structural feature 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;
and S25, sequencing the character recognition results based on the character image arrangement serial number, thereby obtaining a water meter flow counting value.
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 collecting the digital water meter dial images with set quantity for pre-training the neural network classifier, and determining the optimal combination of the statistical characteristics and the structural characteristics of each digital character through the pre-training.
A second aspect of the present application provides a digital water meter identification system, which includes an external-hanging water meter reading device and a server, wherein the water meter reading device includes a camera, a processor and a communication module;
the camera is used for shooting a digital 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.
The invention has the beneficial effects that:
the technical scheme of this application provides a hanging digital 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. Simultaneously, this application has still set up the neural network classifier of digital water gauge flow count value, and wherein, the classifier includes first grade classifier and second grade classifier, and two classifiers are responsible for respectively carrying out categorised discernment to the digit of difference to the efficiency of categorised discernment has effectively been improved.
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 digital water meter identification method disclosed in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a digital water meter identification system 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 digital water meter identification method according to an embodiment of the present application. As shown in fig. 1, a digital water meter identification method according to an embodiment of the present application is applied to an external water meter reading device, and the method includes:
s1, shooting a digital 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 the embodiment of the application, the external water meter reading equipment is arranged, remote meter reading work for the digital water meter can be realized only by simple equipment, the water meter does not need to be transformed on a large scale, and the realization cost of remote meter reading can be greatly reduced. In recent years, Artificial Intelligence (AI) has been rapidly developed and widely used, and achieved fruitful results. In view of the technical characteristics of the artificial intelligence technology, such as accuracy, rapidness, learning and evolution and the like, in the aspect of computer vision identification processing, the neural network algorithm is one of ten large artificial intelligence algorithms and has a self-learning function, for example, when image identification is realized, a network can slowly learn to identify similar images through the self-learning function only by inputting a plurality of different image templates and corresponding identification 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 digital water meter dial plate, so that the water meter flow count value can be accurately and quickly obtained.
In this optional implementation manner, before the processor invokes the artificial intelligence algorithm to identify the water meter dial, the method further includes the following steps:
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;
determining a numerical value display area of the water meter dial from the effective area of the dial;
performing image segmentation on the numerical value display area image to obtain a plurality of character images;
the plurality of character images are respectively numbered from left to right.
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. Meanwhile, a real identification object area, namely a numerical display area, needs to be determined again in the effective area, and the identification mode of the numerical display area can be realized by adopting the shape of the boundary of the connected domain.
In this optional embodiment, 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.
In this alternative embodiment, the choice threshold TijDetermined by the following equation:
wherein, TijA choice threshold corresponding to the pixel point (i, j), wherein lambda is a preset weight and T0The initial value of the threshold value is chosen.
In this optional embodiment, the determining the numerical value display area of the water meter dial from the effective area of the dial includes: and analyzing the identified effective area of the dial plate again, and taking the area defined by the regular boundary of the identified connected area as the numerical value display area of the dial plate of the water meter.
In this alternative embodiment, the artificial intelligence algorithm is a neural network based algorithm.
In this optional embodiment, the processor calls an artificial intelligence algorithm to identify the water meter dial to obtain an identification result of the water meter flow count value, and the identification result includes:
s21, receiving the character images;
s22, recognizing 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;
s23, optimally combining the statistical characteristics and the structural characteristics, and inputting the optimally combined statistical characteristics and structural characteristics into a neural network classifier;
s24, the neural network classifier classifies digital characters based on the input statistical features and structural feature 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;
and S25, sequencing the character recognition results based on the character image arrangement serial number, thereby obtaining a water meter flow counting value.
In this optional embodiment, the second-stage classifier adopts a neural network model with a four-layer structure, 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, wijFor preceding stage inputting i-th to j-th neuronsWeight, θjIs the threshold of the neuron.
In this optional embodiment, before step S2, the method further includes: and collecting the digital water meter dial images with set quantity for pre-training the neural network classifier, and determining the optimal combination of the statistical characteristics and the structural characteristics of each digital character through the pre-training.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a digital water meter identification system disclosed in the embodiment of the present application. As shown in fig. 2, the digital water meter identification system according to the 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 digital 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.
In the embodiment of the application, the external water meter reading equipment is arranged, remote meter reading work for the digital water meter can be realized only by simple equipment, the water meter does not need to be transformed on a large scale, and the realization cost of remote meter reading can be greatly reduced. 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 identifies the digital water meter dial by using an artificial intelligence algorithm, so that the flow count value of the water meter can be accurately and rapidly obtained.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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 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 digital water meter identification method is characterized in that: the method is applied to the externally-hung water meter reading equipment, and comprises the following steps:
s1, shooting a digital 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:
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;
determining a numerical value display area of the water meter dial from the effective area of the dial;
performing image segmentation on the numerical value display area image to obtain a plurality of character images;
the plurality of character images are respectively numbered from left to right.
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.
4. The method of claim 3, wherein: the choice threshold value TijDetermined by the following equation:
wherein, TijA choice threshold corresponding to the pixel point (i, j), wherein lambda is a preset weight and T0The initial value of the threshold value is chosen.
5. The method according to any one of claims 2-4, wherein: the follow confirm in the effective area of dial plate the numerical value display area of water gauge dial plate includes: and analyzing the identified effective area of the dial plate again, and taking the area defined by the regular boundary of the identified connected area as the numerical value display area of the dial plate of the water meter.
6. The method of claim 5, wherein: the artificial intelligence algorithm is an algorithm based on a neural network.
7. The method according to any one of claims 2-6, wherein: 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, and the method comprises the following steps:
s21, receiving the character images;
s22, recognizing 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;
s23, optimally combining the statistical characteristics and the structural characteristics, and inputting the optimally combined statistical characteristics and structural characteristics into a neural network classifier;
s24, the neural network classifier classifies digital characters based on the input statistical features and structural feature 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;
and S25, sequencing the character recognition results based on the character image arrangement serial number, thereby obtaining a water meter flow counting value.
8. The method of claim 7, 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.
9. The method of claim 8, wherein: before step S2, the method further includes: and collecting the digital water meter dial images with set quantity for pre-training the neural network classifier, and determining the optimal combination of the statistical characteristics and the structural characteristics of each digital character through the pre-training.
10. A digital water meter identification system is characterized in that: the 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 digital 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011212962.5A CN112434693A (en) | 2020-11-03 | 2020-11-03 | Digital water meter identification method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011212962.5A CN112434693A (en) | 2020-11-03 | 2020-11-03 | Digital water meter identification method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112434693A true CN112434693A (en) | 2021-03-02 |
Family
ID=74695212
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011212962.5A Pending CN112434693A (en) | 2020-11-03 | 2020-11-03 | Digital water meter identification method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112434693A (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013003032A (en) * | 2011-06-20 | 2013-01-07 | Hochiki Corp | Water meter-based detecting device |
| CH710839A1 (en) * | 2015-03-11 | 2016-09-15 | Localbargain Solutions Suisse Sarl | The process of reading a consumption meter with a fluid and computer system for the implementation of the process. |
| CN106373365A (en) * | 2016-11-02 | 2017-02-01 | 山东金米尔仪器科技有限公司 | Camera shooting type remote water meter reading system achieving fast recognition and reading recognition method thereof |
| WO2018018788A1 (en) * | 2016-07-29 | 2018-02-01 | 深圳友讯达科技股份有限公司 | Image recognition-based meter reading apparatus and method thereof |
| US20190086233A1 (en) * | 2015-10-08 | 2019-03-21 | Aqua Command Limited | A self-powering smart water meter system and method |
| CN109543688A (en) * | 2018-11-14 | 2019-03-29 | 北京邮电大学 | A kind of novel meter reading detection and knowledge method for distinguishing based on multilayer convolutional neural networks |
| CN109977944A (en) * | 2019-02-21 | 2019-07-05 | 杭州朗阳科技有限公司 | A kind of recognition methods of digital water meter reading |
| CN110490195A (en) * | 2019-08-07 | 2019-11-22 | 桂林电子科技大学 | A kind of water meter dial plate Recognition of Reading method |
| CN111444781A (en) * | 2020-03-09 | 2020-07-24 | 武汉理工大学 | Water meter reading identification method and equipment and storage medium |
-
2020
- 2020-11-03 CN CN202011212962.5A patent/CN112434693A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013003032A (en) * | 2011-06-20 | 2013-01-07 | Hochiki Corp | Water meter-based detecting device |
| CH710839A1 (en) * | 2015-03-11 | 2016-09-15 | Localbargain Solutions Suisse Sarl | The process of reading a consumption meter with a fluid and computer system for the implementation of the process. |
| US20190086233A1 (en) * | 2015-10-08 | 2019-03-21 | Aqua Command Limited | A self-powering smart water meter system and method |
| WO2018018788A1 (en) * | 2016-07-29 | 2018-02-01 | 深圳友讯达科技股份有限公司 | Image recognition-based meter reading apparatus and method thereof |
| CN106373365A (en) * | 2016-11-02 | 2017-02-01 | 山东金米尔仪器科技有限公司 | Camera shooting type remote water meter reading system achieving fast recognition and reading recognition method thereof |
| CN109543688A (en) * | 2018-11-14 | 2019-03-29 | 北京邮电大学 | A kind of novel meter reading detection and knowledge method for distinguishing based on multilayer convolutional neural networks |
| CN109977944A (en) * | 2019-02-21 | 2019-07-05 | 杭州朗阳科技有限公司 | A kind of recognition methods of digital water meter reading |
| CN110490195A (en) * | 2019-08-07 | 2019-11-22 | 桂林电子科技大学 | A kind of water meter dial plate Recognition of Reading method |
| CN111444781A (en) * | 2020-03-09 | 2020-07-24 | 武汉理工大学 | Water meter reading identification method and equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| RUI XIAO-PING，等: ""Reading Digital Numbers"", 《2009 WORLD CONGRESS ON COMPUTER SCIENCE AND INFORMATION ENGINEERING》, 24 June 2019 (2019-06-24), pages 632 - 636 * |
| YAOBEN PENG，等: "" Application of Deep Residual Neural Network to Water Meter Reading Recognition"", 《 2020 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTER APPLICATIONS》, 1 September 2020 (2020-09-01), pages 774 - 777 * |
| 李云栋，等: ""基于BP神经网络的实时水表识别系统研究"", 《计算机测量与控制》, vol. 21, no. 5, 31 May 2013 (2013-05-31), pages 1330 - 2 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108171184B (en) | Method for re-identifying pedestrians based on Simese network | |
| CN109670441A (en) | A kind of realization safety cap wearing knows method for distinguishing, system, terminal and computer readable storage medium | |
| CN109614488B (en) | Text classification and image recognition-based distribution network live working condition judgment method | |
| CN105574550A (en) | Vehicle identification method and device | |
| CN108520215B (en) | Single-sample face recognition method based on multi-scale joint feature encoder | |
| CN113869162A (en) | Violation identification method and system based on artificial intelligence | |
| CN109858367A (en) | The vision automated detection method and system that worker passes through support unsafe acts | |
| CN113901977A (en) | Deep learning-based power consumer electricity stealing identification method and system | |
| CN111950812B (en) | Method and device for automatically identifying and predicting rainfall | |
| CN116468392A (en) | Method, device, equipment and storage medium for monitoring progress of power grid engineering project | |
| CN115809833A (en) | Intelligent monitoring method and device for capital construction project based on portrait technology | |
| CN111340727A (en) | Abnormal flow detection method based on GBR image | |
| CN114417981A (en) | Intelligent river length patrol system | |
| CN109583499A (en) | A kind of transmission line of electricity target context categorizing system based on unsupervised SDAE network | |
| CN116614366A (en) | Industrial Internet optimization method and system based on edge calculation | |
| CN116205905B (en) | Power distribution network construction safety and quality image detection method and system based on mobile terminal | |
| CN110502605B (en) | Electric power asset LCC cost collection system based on artificial intelligence technology | |
| CN112434693A (en) | Digital water meter identification method and system | |
| CN117252459A (en) | Fruit quality evaluation system based on deep learning | |
| CN111652242A (en) | Image processing method, image processing device, electronic equipment and storage medium | |
| CN116128413A (en) | Intelligent warehouse material statistics system based on Bluetooth communication | |
| CN116308679A (en) | Supply chain abnormal order processing method, device, equipment and storage medium | |
| CN114140793A (en) | Matching method and device for terminal block and terminal block wiring | |
| CN115223157A (en) | Power grid equipment nameplate optical character recognition method based on recurrent neural network | |
| CN114077877A (en) | Newly added garbage identification method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |