CN105635546A - Method for automatically reading electric energy meter by automatic meter reading platform based on image acquisition - Google Patents

Method for automatically reading electric energy meter by automatic meter reading platform based on image acquisition Download PDF

Info

Publication number
CN105635546A
CN105635546A CN201610122882.8A CN201610122882A CN105635546A CN 105635546 A CN105635546 A CN 105635546A CN 201610122882 A CN201610122882 A CN 201610122882A CN 105635546 A CN105635546 A CN 105635546A
Authority
CN
China
Prior art keywords
value
pixel
subset
block diagram
diagram picture
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.)
Withdrawn
Application number
CN201610122882.8A
Other languages
Chinese (zh)
Inventor
不公告发明人
Original Assignee
周杰
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 周杰 filed Critical 周杰
Priority to CN201510152400.9A priority Critical patent/CN104702920B/en
Publication of CN105635546A publication Critical patent/CN105635546A/en
Withdrawn legal-status Critical Current

Links

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00Television systems
    • H04N7/18Closed circuit television systems, i.e. systems in which the signal is not broadcast
    • H04N7/183Closed circuit television systems, i.e. systems in which the signal is not broadcast for receiving images from a single remote source
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/20Image acquisition
    • G06K9/32Aligning or centering of the image pick-up or image-field
    • G06K9/3233Determination of region of interest
    • G06K9/325Detection of text region in scene imagery, real life image or Web pages, e.g. licenses plates, captions on TV images
    • G06K9/3258Scene text, e.g. street name
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00Details of television systems
    • H04N5/222Studio circuitry; Studio devices; Studio equipment ; Cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, TV cameras, video cameras, camcorders, webcams, camera modules for embedding in other devices, e.g. mobile phones, computers or vehicles
    • H04N5/225Television cameras ; Cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, camcorders, webcams, camera modules specially adapted for being embedded in other devices, e.g. mobile phones, computers or vehicles
    • H04N5/2251Constructional details
    • H04N5/2254Mounting of optical parts, e.g. lenses, shutters, filters or optical parts peculiar to the presence or use of an electronic image sensor
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00Details of television systems
    • H04N5/222Studio circuitry; Studio devices; Studio equipment ; Cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, TV cameras, video cameras, camcorders, webcams, camera modules for embedding in other devices, e.g. mobile phones, computers or vehicles
    • H04N5/225Television cameras ; Cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, camcorders, webcams, camera modules specially adapted for being embedded in other devices, e.g. mobile phones, computers or vehicles
    • H04N5/2251Constructional details
    • H04N5/2253Mounting of pick-up device, electronic image sensor, deviation or focusing coils
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R11/00Electromechanical arrangements for measuring time integral of electric power, i.e. electric energy or current, e.g. of consumption
    • G01R11/02Constructional details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/54Combinations of preprocessing functions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration
    • G06T5/001Image restoration
    • G06T5/003Deblurring; Sharpening
    • GPHYSICS
    • G08SIGNALLING
    • G08CTRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C17/00Arrangements for transmitting signals characterised by the use of a wireless electrical link
    • G08C17/02Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00Details of television systems
    • H04N5/222Studio circuitry; Studio devices; Studio equipment ; Cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, TV cameras, video cameras, camcorders, webcams, camera modules for embedding in other devices, e.g. mobile phones, computers or vehicles
    • H04N5/225Television cameras ; Cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, camcorders, webcams, camera modules specially adapted for being embedded in other devices, e.g. mobile phones, computers or vehicles
    • H04N5/232Devices for controlling television cameras, e.g. remote control ; Control of cameras comprising an electronic image sensor
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00Television systems
    • H04N7/18Closed circuit television systems, i.e. systems in which the signal is not broadcast
    • H04N7/181Closed circuit television systems, i.e. systems in which the signal is not broadcast for receiving images from a plurality of remote sources
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N9/00Details of colour television systems
    • H04N9/04Picture signal generators
    • H04N9/045Picture signal generators using solid-state devices

Abstract

The invention relates to a method for automatically reading an electric energy meter by an automatic meter reading platform based on image acquisition. The method comprises the following steps of: (1), shooting the meter frame of the electric energy meter through a CMOS image sensor so as to obtain a meter frame image; (2), processing the meter frame image obtained in the step (1) through a digital signal processor so as to obtain the number of the electric energy meter; and (3), respectively storing and displaying the number of the electric energy meter obtained in the step (2) through a FLASH memory and a LCD screen, wherein the method is implemented by utilizing the automatic meter reading platform based on CMOS image acquisition; and the platform comprises the CMOS image sensor, the digital signal processor, the LCD screen and the FLASH memory, wherein the CMOS image sensor, the LCD screen and the FLASH memory are respectively connected with the digital signal processor. By means of the method disclosed by the invention, the original fussy artificial meter reading operation can be replaced; influence of hazy weather on meter reading can be overcome; and the meter reading efficiency and precision can be increased.

Description

A kind of platform of automatically checking meter based on image collection is to the method for automatic copying electric meter
Technical field
The present invention relates to electric energy table field, particularly relate to a kind of method automatically checked meter by electric energy table based on cmos image collection.
Background technology
Electric energy table is measurement electrical unit or the instrument of personal electric reading, it is power supply department charge and the important referential data of allotment electric power resource, the One's name is legion of electric energy table simultaneously, substantially each needs by electrical unit to arrange an electric energy table, and each family resident also needs to arrange separately an electric energy table. Thus, from value and the characteristic distributions of electric energy table it may be seen that the reading collection of electric energy table needs to meet requirement that is efficient and high precision simultaneously.
But, all there is intrinsic defect in electric energy meter reading scheme of the prior art: (1) is although manual meter reading method can ensure to gather the precision of reading, but needs the cost of labor of at substantial, and collecting efficiency is not high, and real-time is not strong; (2) more existing electronic meter reading schemes can only ensure the reading collection under normal environment; owing to much electricity table is placed in open air; under the weather that visibility meter is lower; such as, under foggy weather environment; often cause the electric energy chart of shooting as smudgy; correspondingly, the reading error of identification is very big.
Therefore, need a kind of new automatic copying electric meter method, the mode of electronic meter reading is adopted to substitute original manual metering mode, simultaneously, electronic meter reading mode is carried out upgrading, increase Sharp processing of image function, overcome foggy weather to the disadvantageous effect of electric energy table Recognition of Reading such that it is able to meet the requirement of the efficient and high precision that electric energy table reading gathers.
Summary of the invention
In order to solve the problem, according to an aspect of the present invention, the present invention provides a kind of method automatically checked meter by electric energy table based on cmos image collection, comprising:
(1) take to obtain table block diagram picture to the table frame of electric energy table by cmos image sensor;
(2) table block diagram picture step (1) obtained by digital signal processor carries out image procossing, to obtain electric energy table reading;
(3) electric energy table reading step (2) obtained respectively by FLASH memory and LCD display screen is stored and shows;
Wherein said method utilizes the automatic copying electric meter platform gathered based on cmos image to implement, described platform comprises cmos image sensor, digital signal processor, LCD display screen and FLASH memory, and wherein said cmos image sensor, described LCD display screen and described FLASH memory are connected with described digital signal processor respectively.
Specifically, described cmos image sensor is used for the table frame to electric energy table and takes to obtain table block diagram picture, described digital signal processor is connected with described cmos image sensor, for described table block diagram picture is carried out image procossing, to obtain electric energy table reading, described FLASH memory is connected with described digital signal processor, for storing described electric energy table reading, described LCD display screen is connected with described digital signal processor, for showing described electric energy table reading.
More specifically, the described automatic copying electric meter platform based on cmos image collection also comprises: CSI interface, between cmos image sensor and image-preprocessing device, send image-preprocessing device to for the table block diagram picture taken by described cmos image sensor;
First parallel port, between described digital signal processor and described LCD display screen, for by described numeral letter
The electric energy table reading that number treater obtains sends described LCD display screen to show;
2nd parallel port, between described digital signal processor and described FLASH memory, sends described FLASH memory to store for the electric energy table reading obtained by described digital signal processor;
Power supply, comprise solar powered device, store battery, change-over switch and voltage transitions device, described change-over switch is connected respectively with described solar powered device and described store battery, remain electricity determines whether be switched to described solar powered device to power by described solar powered device according to store battery, described voltage transitions device is connected with described change-over switch, taking the 5V voltage transitions that will be inputted by change-over switch as 3.3V voltage;
Image-preprocessing device, between described CSI interface and described digital signal processor, for receiving described table block diagram picture, described table block diagram picture goes mist process to obtain fogmeter block diagram picture, goes fogmeter block diagram picture to input described digital signal processor to carry out image procossing to obtain electric energy table reading by described.
More particularly, it is seen that image-preprocessing device also comprises:
Store subset, for prestoring sky upper limit gray scale threshold value and sky lower limit gray scale threshold value, described sky upper limit gray scale threshold value and described sky lower limit gray scale threshold value are for separating of the sky areas published picture in picture, also for prestoring presetted pixel value threshold value, described presetted pixel value threshold value value is between 0 to 255;
Haze Concentration Testing subset, is arranged in air, for detecting the haze concentration of electric energy table position in real time, and determines that intensity removed by haze according to haze concentration, and described haze removes intensity value between 0 to 1;
Region dividing subset, connect described CSI interface to receive described table block diagram picture, described table block diagram picture carries out gray processing process to obtain gray processing table block diagram picture, also it is connected with storage subset, by the pixel identification of gray-scale value in described gray processing table block diagram picture between described sky upper limit gray scale threshold value and described sky lower limit gray scale threshold value and form gray processing sky sub pattern, it is partitioned into described gray processing sky sub pattern to obtain the non-sky subgraph of gray processing from described gray processing table block diagram picture, the colored non-sky subgraph corresponding with described gray processing non-sky subgraph is obtained based on the position of described gray processing non-sky subgraph in described table block diagram picture,
Black channel obtains subset, is connected with described Region dividing subset to obtain the non-sky subgraph of described colour, for each pixel in the non-sky subgraph of described colour, calculates its R, G, B tri-Color Channel pixel value, at described coloured silk
The R of all pixels in look non-sky subgraph, G, B tri-extract the Color Channel at the minimum Color Channel pixel value place of numerical value in Color Channel pixel value as black channel;
Overall atmosphere light value obtains subset, it is connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel and it is connected to obtain described table block diagram picture and described black channel respectively, multiple pixels that black channel pixel value in described table block diagram picture is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, described set of pixels to be tested will have the gray-scale value atmosphere light value as a whole of the pixel of maximum gradation value, atmospheric scattering light value obtains subset, it is connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described table block diagram picture, extract its R, G, in B tri-Color Channel pixel value, minimum value is as target pixel value, use and keep the Gaussian filter EPGF (edge-preservinggaussianfilter) at edge described target pixel value to carry out filtering process to obtain filtering target pixel value, target pixel value is subtracted filtering target pixel value to obtain object pixel difference, EPGF is used object pixel difference to carry out filtering process to obtain filtering object pixel difference, filtering target pixel value is subtracted filtering object pixel difference to obtain haze removal benchmark value, haze is removed intensity and is multiplied by haze removal benchmark value to obtain haze removal threshold value, get haze and remove the minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximum value in comparison reference and 0 as each pixel, medium transmission rate obtains subset, obtain subset with described overall atmosphere light value acquisition subset and described atmospheric scattering light value to be connected respectively, by the atmospheric scattering light value of each pixel divided by overall atmosphere light value to obtain except value, subtract described except value is to obtain the medium transmission rate of each pixel by 1, sharpening Image Acquisition subset, with described Region dividing subset, described overall atmosphere light value obtains subset and described medium transmission rate acquisition subset connects respectively, the medium transmission rate subtracting each pixel by 1 is to obtain the first difference, described first difference is multiplied by overall atmosphere light value to obtain product value, the pixel value of each pixel in described table block diagram picture is subtracted described product value to obtain the 2nd difference, by described 2nd difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described table block diagram picture, the pixel value of each pixel comprises the R of each pixel in described table block diagram picture, G, B tri-Color Channel pixel value, correspondingly, the sharpening pixel value of each pixel obtained comprises the R of each pixel, G, B tri-Color Channel sharpening pixel value, the sharpening pixel value composition of all pixels removes fogmeter block diagram picture,
Wherein, described haze Concentration Testing subset is also built-in with static storage cell, and for prestoring relation synopsis, described relation synopsis saves haze concentration and the corresponding relation between intensity removed by haze.
Specifically, the table block diagram picture of described cmos image sensor shooting sends image-preprocessing device to, described table block diagram picture is gone mist to process to obtain fogmeter block diagram picture by described image-preprocessing device, by described go fogmeter block diagram picture to input described digital signal processor after carry out image sharpening, self-adaptation recurrence filtering and OCR identifying processing successively, to obtain electric energy table reading.
Specifically, in the process, described cmos image sensor is the OV7640 sensor of Omnivision company.
Specifically, in the process, described Region dividing subset, described black channel obtain subset, described overall atmosphere light value acquisition subset, described atmospheric scattering light value acquisition subset, described medium transmission rate acquisition subset and described sharpening Image Acquisition subset and adopt different FPGA chips to realize respectively.
Specifically, in the process, described Region dividing subset, described black channel obtain subset, described overall atmosphere light value acquisition subset, described atmospheric scattering light value acquisition subset, described medium transmission rate acquisition subset and described sharpening Image Acquisition subset and are integrated on one piece of circuit card.
Specifically, in the process, described Region dividing subset, described black channel obtain subset, described overall atmosphere light value acquisition subset, described atmospheric scattering light value acquisition subset, described medium transmission rate acquisition subset and described sharpening Image Acquisition subset and are integrated in one piece of FPGA chip.
According to another aspect of the present invention, present invention also offers a kind of automatic copying electric meter platform gathered based on cmos image, described platform comprises digital signal processor, LCD display screen, FLASH memory and cmos image sensor, described cmos image sensor is used for the table frame to electric energy table and takes to obtain table block diagram picture, described digital signal processor is connected with described cmos image sensor, for described table block diagram picture is carried out image procossing, to obtain electric energy table reading, described FLASH memory is connected with described digital signal processor, for storing described electric energy table reading, described LCD display screen is connected with described digital signal processor, for showing described electric energy table reading.
More specifically, in the described automatic copying electric meter platform gathered based on cmos image, also comprise: CSI interface, between cmos image sensor and image-preprocessing device, send image-preprocessing device to for the table block diagram picture taken by described cmos image sensor;
First parallel port, between described digital signal processor and described LCD display screen, the electric energy table reading for being obtained by described digital signal processor sends described LCD display screen to show;
2nd parallel port, between described digital signal processor and described FLASH memory, for by described numeral
The electric energy table reading that signal processing device obtains sends described FLASH memory to store;
Power supply, comprise solar powered device, store battery, change-over switch and voltage transitions device, described change-over switch is connected respectively with described solar powered device and described store battery, remain electricity determines whether be switched to described solar powered device to power by described solar powered device according to store battery, described voltage transitions device is connected with described change-over switch, taking the 5V voltage transitions that will be inputted by change-over switch as 3.3V voltage;
Image-preprocessing device, between described CSI interface and described digital signal processor, for receiving described table block diagram picture, described table block diagram picture goes mist process to obtain fogmeter block diagram picture, goes fogmeter block diagram picture to input described digital signal processor to carry out image procossing to obtain electric energy table reading by described.
More particularly, it is seen that image-preprocessing device also comprises:
Store subset, for prestoring sky upper limit gray scale threshold value and sky lower limit gray scale threshold value, described sky upper limit gray scale threshold value and described sky lower limit gray scale threshold value are for separating of the sky areas published picture in picture, also for prestoring presetted pixel value threshold value, described presetted pixel value threshold value value is between 0 to 255;
Haze Concentration Testing subset, is arranged in air, for detecting the haze concentration of electric energy table position in real time, and determines that intensity removed by haze according to haze concentration, and described haze removes intensity value between 0 to 1;
Region dividing subset, connect described CSI interface to receive described table block diagram picture, described table block diagram picture carries out gray processing process to obtain gray processing table block diagram picture, also it is connected with storage subset, by the pixel identification of gray-scale value in described gray processing table block diagram picture between described sky upper limit gray scale threshold value and described sky lower limit gray scale threshold value and form gray processing sky sub pattern, it is partitioned into described gray processing sky sub pattern to obtain the non-sky subgraph of gray processing from described gray processing table block diagram picture, the colored non-sky subgraph corresponding with described gray processing non-sky subgraph is obtained based on the position of described gray processing non-sky subgraph in described table block diagram picture,
Black channel obtains subset, it is connected with described Region dividing subset to obtain the non-sky subgraph of described colour, for each pixel in the non-sky subgraph of described colour, calculate its R, G, B tri-Color Channel pixel value, the R of all pixels in described colour non-sky subgraph, G, B tri-extracts the Color Channel at the minimum Color Channel pixel value place of numerical value in Color Channel pixel value as black channel;
Overall atmosphere light value obtains subset, it is connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel and it is connected to obtain described table block diagram picture and described black channel respectively, multiple pixels that black channel pixel value in described table block diagram picture is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, described set of pixels to be tested will have the gray-scale value atmosphere light value as a whole of the pixel of maximum gradation value;
Atmospheric scattering light value obtains subset, it is connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described table block diagram picture, extract its R, G, in B tri-Color Channel pixel value, minimum value is as target pixel value, use and keep the Gaussian filter EPGF (edge-preservinggaussianfilter) at edge described target pixel value to carry out filtering process to obtain filtering target pixel value, target pixel value is subtracted filtering target pixel value to obtain object pixel difference, EPGF is used object pixel difference to carry out filtering process to obtain filtering object pixel difference, filtering target pixel value is subtracted filtering object pixel difference to obtain haze removal benchmark value, haze is removed intensity and is multiplied by haze removal benchmark value to obtain haze removal threshold value, get haze and remove the minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximum value in comparison reference and 0 as each pixel,
Medium transmission rate obtains subset, obtain subset with described overall atmosphere light value acquisition subset and described atmospheric scattering light value to be connected respectively, by the atmospheric scattering light value of each pixel divided by overall atmosphere light value to obtain except value, subtract described except value is to obtain the medium transmission rate of each pixel by 1;
Sharpening Image Acquisition subset, with described Region dividing subset, described overall atmosphere light value obtains subset and described medium transmission rate acquisition subset connects respectively, the medium transmission rate subtracting each pixel by 1 is to obtain the first difference, described first difference is multiplied by overall atmosphere light value to obtain product value, the pixel value of each pixel in described table block diagram picture is subtracted described product value to obtain the 2nd difference, by described 2nd difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described table block diagram picture, the pixel value of each pixel comprises the R of each pixel in described table block diagram picture, G, B tri-Color Channel pixel value, correspondingly, the sharpening pixel value of each pixel obtained comprises the R of each pixel, G, B tri-Color Channel sharpening pixel value, the sharpening pixel value composition of all pixels removes fogmeter block diagram picture,
Described digital signal processor, after removing fogmeter block diagram picture described in receiving, goes fogmeter block diagram picture to carry out image sharpening, self-adaptation recurrence filtering and OCR identifying processing successively to described, to obtain electric energy table reading;
Wherein, described haze Concentration Testing subset is also built-in with static storage cell, and for prestoring relation synopsis, described relation synopsis saves haze concentration and the corresponding relation between intensity removed by haze.
More specifically, in the described automatic copying electric meter platform gathered based on cmos image, described cmos image sensor is the OV7640 sensor of Omnivision company.
More specifically, in the described automatic copying electric meter platform gathered based on cmos image, described Region dividing subset, described black channel obtain subset, described overall atmosphere light value obtains subset, described atmospheric scattering light value obtains subset, described medium transmission rate obtains subset and described sharpening Image Acquisition subset adopts different FPGA chips to realize respectively.
More specifically, in the described automatic copying electric meter platform gathered based on cmos image, described Region dividing subset, described black channel obtain subset, described overall atmosphere light value obtains subset, described atmospheric scattering light value obtains subset, described medium transmission rate obtains subset and described sharpening Image Acquisition subset is integrated on one piece of circuit card.
More specifically, in the described automatic copying electric meter platform gathered based on cmos image, described Region dividing subset, described black channel obtain subset, described overall atmosphere light value obtains subset, described atmospheric scattering light value obtains subset, described medium transmission rate obtains subset and described sharpening Image Acquisition subset is integrated in one piece of FPGA chip.
Method electric energy table checked meter automatically based on cmos image collection of the present invention and the automatic copying electric meter platform gathered based on cmos image, utilize acquisition technology and the image processing techniques of high precision, ensure the high-level efficiency of data gathering, determine that haze is to the influence factor of image according to atmospheric attenuation model simultaneously, and go hazeization to process on the electric energy chart picture gathered under foggy weather, obtain electric energy chart picture clearly, thus ensure the accuracy that reading gathers.
Accompanying drawing explanation
Below with reference to accompanying drawing, embodiment of the present invention are described, wherein:
Fig. 1 is the block diagram of the automatic copying electric meter platform gathered based on cmos image illustrated according to an embodiment of the present invention.
Embodiment
Below with reference to accompanying drawings the embodiment based on the automatic copying electric meter platform of cmos image collection of the present invention is described in detail.
Electric energy can convert various energy to. As: convert heat energy to by electric furnace, convert mechanical energy to by motor, convert luminous energy etc. to by electric light. And the ammeter recording this kind of electric energy is electric energy table.
The principle of work of electric energy table is as follows: when electric energy table is accessed circuit-under-test, just having exchange current to flow through in current coil and potential winding, these two exchange current produce the magnetic flux of alternation respectively in their iron core; Alternating flux, through aluminium dish, induces eddy current in aluminium dish; Eddy current is subject to again the effect of power in magnetic field, thus makes aluminium dish obtain torque (initiatively moment) and rotate. The power that load consumes is more big, and more big by the electric current of current coil, the eddy current induced in aluminium dish is also more big, and the moment that aluminium dish is rotated is more big. Namely the power that the size of torque consumes with load is directly proportional. Power is more big, and torque is also more big, and aluminium dish rotates also more fast. When aluminium dish rotates, being subject to again the effect of the braking moment that permanent magnet produces, braking moment is with initiatively moment direction is contrary; The size of braking moment is directly proportional to the rotating speed of aluminium dish, and aluminium dish rotates more fast, and braking moment is also more big. When active moment and braking moment reach transient equilibrium, aluminium dish is by uniform rotation. The electric energy that load consumes is directly proportional to the revolution of aluminium dish. When aluminium dish rotates, drive counter, the electric energy consumed is indicated. This is exactly the simple process of electric energy table work.
Owing to electric energy table has widely distributed, and electric energy table reading is whether correct extremely important. Therefore, the collection of electric energy table reading should be accurate again fast. But electric energy table reading acquisition platform of the prior art cannot meet this 2 requirements simultaneously. For this reason, the present invention has built a kind of automatic copying electric meter platform gathered based on cmos image, efficient requirement is met by image acquisition and processing technology targetedly, process the collection of the electric energy table reading that also can carry out high precision under realizing severe weather by going hazeization, thus meet the setting requirement of power supply department.
Fig. 1 is the block diagram of the automatic copying electric meter platform gathered based on cmos image illustrated according to an embodiment of the present invention, described platform comprises digital signal processor 1, LCD display screen 4, FLASH memory 3 and cmos image sensor 2, described cmos image sensor 2 is for taking to obtain table block diagram picture to the table frame of electric energy table, described digital signal processor 1 is connected with described cmos image sensor 2, for described table block diagram picture is carried out image procossing, to obtain electric energy table reading, described FLASH memory 3 is connected with described digital signal processor 1, for storing described electric energy table reading, described LCD display screen 4 is connected with described digital signal processor 1, for showing described electric energy table reading.
Then, continue the concrete structure based on the automatic copying electric meter platform of cmos image collection to the present invention to be further detailed.
Described platform also comprises: CSI interface, between cmos image sensor 2 and image-preprocessing device, sends image-preprocessing device to for the table block diagram picture taken by described cmos image sensor 2.
Described platform also comprises: the first parallel port, and between described digital signal processor 1 and described LCD display screen 4, the electric energy table reading for being obtained by described digital signal processor 1 sends described LCD display screen 4 to show.
Described platform also comprises: the 2nd parallel port, and between described digital signal processor 1 and described FLASH memory 3, the electric energy table reading for being obtained by described digital signal processor 1 sends described FLASH memory 3 to store.
Described platform also comprises: power supply, comprise solar powered device, store battery, change-over switch and voltage transitions device, described change-over switch is connected respectively with described solar powered device and described store battery, remain electricity determines whether be switched to described solar powered device to power by described solar powered device according to store battery, described voltage transitions device is connected with described change-over switch, taking the 5V voltage transitions that will be inputted by change-over switch as 3.3V voltage.
Described platform also comprises: image-preprocessing device, between described CSI interface and described digital signal processor 1, for receiving described table block diagram picture, described table block diagram picture goes mist process to obtain fogmeter block diagram picture, goes fogmeter block diagram picture to input described digital signal processor 1 to carry out image procossing to obtain electric energy table reading by described.
Described image-preprocessing device also comprises: store subset, for prestoring sky upper limit gray scale threshold value and sky
Empty lower limit gray scale threshold value, described sky upper limit gray scale threshold value and described sky lower limit gray scale threshold value are for separating of the sky areas published picture in picture, and also for prestoring presetted pixel value threshold value, described presetted pixel value threshold value value is between 0 to 255.
Described image-preprocessing device also comprises: haze Concentration Testing subset, is arranged in air, for detecting the haze concentration of electric energy table position in real time, and determines that intensity removed by haze according to haze concentration, and described haze removes intensity value between 0 to 1.
Described image-preprocessing device also comprises: Region dividing subset, connect described CSI interface to receive described table block diagram picture, described table block diagram picture carries out gray processing process to obtain gray processing table block diagram picture, also it is connected with storage subset, by the pixel identification of gray-scale value in described gray processing table block diagram picture between described sky upper limit gray scale threshold value and described sky lower limit gray scale threshold value and form gray processing sky sub pattern, it is partitioned into described gray processing sky sub pattern to obtain the non-sky subgraph of gray processing from described gray processing table block diagram picture, the colored non-sky subgraph corresponding with described gray processing non-sky subgraph is obtained based on the position of described gray processing non-sky subgraph in described table block diagram picture.
Described image-preprocessing device also comprises: black channel obtains subset, it is connected with described Region dividing subset to obtain the non-sky subgraph of described colour, for each pixel in the non-sky subgraph of described colour, calculate its R, G, B tri-Color Channel pixel value, the R of all pixels in described colour non-sky subgraph, G, B tri-extracts the Color Channel at the minimum Color Channel pixel value place of numerical value in Color Channel pixel value as black channel.
Described image-preprocessing device also comprises: overall atmosphere light value obtains subset, it is connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel and it is connected to obtain described table block diagram picture and described black channel respectively, multiple pixels that black channel pixel value in described table block diagram picture is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, described set of pixels to be tested will have the gray-scale value atmosphere light value as a whole of the pixel of maximum gradation value.
Described image-preprocessing device also comprises: atmospheric scattering light value obtains subset, it is connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described table block diagram picture, extract its R, G, in B tri-Color Channel pixel value, minimum value is as target pixel value, use and keep the Gaussian filter EPGF (edge-preservinggaussianfilter) at edge described target pixel value to carry out filtering process to obtain filtering target pixel value, target pixel value is subtracted filtering target pixel value to obtain object pixel difference, EPGF is used object pixel difference to carry out filtering process to obtain filtering object pixel difference, filtering target pixel value is subtracted filtering object pixel difference to obtain haze removal benchmark value, haze is removed intensity and is multiplied by haze removal benchmark value to obtain haze removal threshold value, get haze and remove the minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximum value in comparison reference and 0 as each pixel.
Described image-preprocessing device also comprises: medium transmission rate obtains subset, obtains subset with described overall atmosphere light value acquisition subset and described atmospheric scattering light value and is connected respectively, by the atmospheric scattering light value of each pixel divided by entirety
Atmosphere light value, to obtain except value, subtracts described except value is to obtain the medium transmission rate of each pixel by 1.
Described image-preprocessing device also comprises: sharpening Image Acquisition subset, with described Region dividing subset, described overall atmosphere light value obtains subset and described medium transmission rate acquisition subset connects respectively, the medium transmission rate subtracting each pixel by 1 is to obtain the first difference, described first difference is multiplied by overall atmosphere light value to obtain product value, the pixel value of each pixel in described table block diagram picture is subtracted described product value to obtain the 2nd difference, by described 2nd difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described table block diagram picture, the pixel value of each pixel comprises the R of each pixel in described table block diagram picture, G, B tri-Color Channel pixel value, correspondingly, the sharpening pixel value of each pixel obtained comprises the R of each pixel, G, B tri-Color Channel sharpening pixel value, the sharpening pixel value composition of all pixels removes fogmeter block diagram picture.
Described digital signal processor 1, after removing fogmeter block diagram picture described in receiving, goes fogmeter block diagram picture to carry out image sharpening, self-adaptation recurrence filtering and OCR identifying processing successively to described, to obtain electric energy table reading.
Wherein, described haze Concentration Testing subset is also built-in with static storage cell, and for prestoring relation synopsis, described relation synopsis saves haze concentration and the corresponding relation between intensity removed by haze.
More specifically, in the described automatic copying electric meter platform gathered based on cmos image, described cmos image sensor is the OV7640 sensor of Omnivision company.
Wherein, in described platform, described Region dividing subset, described black channel obtain subset, described overall atmosphere light value obtains subset, described atmospheric scattering light value obtains subset, described medium transmission rate obtains subset and described sharpening Image Acquisition subset can adopt different FPGA chips to realize respectively; Or described Region dividing subset, described black channel obtain subset, described overall atmosphere light value obtains subset, described atmospheric scattering light value obtains subset, described medium transmission rate obtains subset and described sharpening Image Acquisition subset can be integrated on one piece of circuit card; Alternatively, described Region dividing subset, described black channel obtain subset, described overall atmosphere light value acquisition subset, described atmospheric scattering light value acquisition subset, described medium transmission rate acquisition subset and described sharpening Image Acquisition subset and can be integrated in one piece of FPGA chip.
In addition, haze image can remove haze by what a series of images treatment facility realized image, to obtain the image of sharpening, it is to increase the visibility meter of image. These image processing equipments perform different image processing functions respectively, based on the principle that haze is formed, reach the effect removing haze. The sharpening process of haze image all has great using value for dual-use field, and military domain comprises military and national defense, remote sensing navigation etc., and civilian field comprises road monitoring, target tracking and automatic Pilot etc.
The process that haze image is formed can be described by atmospheric attenuation process, relation between haze image and real image and sharpening image can be stated by the medium transmission rate of overall atmosphere light value and each pixel, namely when known haze image, according to the medium transmission rate of overall atmosphere light value and each pixel, it is possible to solve sharpening image.
There are some effective and through verifying means in the solving of medium transmission rate for overall atmosphere light value and each pixel, such as, for the medium transmission rate of each pixel, need the atmospheric scattering light value obtaining overall atmosphere light value and each pixel, and the pixel value of each pixel in haze image can carried out the Gaussian smoothing filtering at twice maintenance edge and obtain by the atmospheric scattering light value of each pixel, therebetween, the intensity that haze is removed is adjustable, and overall atmosphere light value (namely makes the black channel value of some pixels very low by obtaining the black channel of haze image in haze image, black channel is R, G, one in B tri-Color Channel), in haze image, it is worth in multiple pixels bigger than normal, by searching black channel pixel, the pixel finding gray-scale value maximum to obtain, it is about to search out, the gray-scale value atmosphere light value as a whole of the pixel that gray-scale value is maximum, participate in the sharpening process of each pixel in haze image. relation between concrete haze image and real image and sharpening image, and the relation between each parameter can see above content.
By the discussion to haze image formation basic theory, build the relation between haze image and sharpening image, by multiple parametric representation this kind of relation, can be reduced by the multiple parameter values of acquisition and haze image subsequently and obtain the higher image of sharpness, owing to some statistical means and empirical means have been used in the acquisition of parameter, therefore the image that described sharpness is higher can not be equal to real image completely, but what had certain degree goes haze effect, effectively ensure for the every field operation under haze weather provides.
In addition, parallel port and parallel interface, refer to adopt parallel transmission mode to transmit the interface standard of data. From the simplest parallel data register or special purpose interface integrated circuit (IC) chip such as 8255,6820 etc., to more complicated SCSI or IDE parallel interface, kind has tens of kinds.
The interface features of a parallel interface can be described from two aspects: the width of the data channel 1, transmitted in a parallel fashion, also claims the figure place of interface transmission; 2, for coordinating the extra Interface Controller line of parallel transfer or claim the characteristic of mutual signal. The width of data can from 1-128 position or wider, and the most frequently used is 8, once transmits 8 data bit by interface. At the LPT interface that the parallel interface that computer realm is the most frequently used is usually said.
Adopt the automatic copying electric meter platform gathered based on cmos image of the present invention, the not high technical problem of precision may be there is for existing electric energy meter reading ineffective systems and at severe weather, by adopting image collection and image processing techniques targetedly, substitute manual metering mode, improve meter reading efficiency, simultaneously, add the image preprocess apparatus based on atmospheric attenuation model to realize going hazeization to process, reduce haze on the impact of quality of image of checking meter, avoid electric energy table reading image data disturbed.
Although it should be appreciated that the present invention with better embodiment disclose as above, but above-described embodiment and be not used to limit the present invention. For any those of ordinary skill in the art, do not departing from technical solution of the present invention scope situation, all can utilize the technology contents of above-mentioned announcement that technical solution of the present invention is made many possible variations and modification, or be revised as the equivalent embodiment of equivalent variations. Therefore, every content not departing from technical solution of the present invention, the technical spirit of foundation the present invention, to any simple modification made for any of the above embodiments, equivalent variations and modification, all still belongs in the scope of technical solution of the present invention protection.

Claims (1)

1. the method based on cmos image collection, electric energy table checked meter automatically, comprising:
(1) take to obtain table block diagram picture to the table frame of electric energy table by cmos image sensor;
(2) table block diagram picture step (1) obtained by digital signal processor carries out image procossing, to obtain electric energy table reading;
(3) electric energy table reading step (2) obtained respectively by FLASH memory and LCD display screen is stored and shows;
Described method utilizes the automatic copying electric meter platform gathered based on cmos image to implement, described platform comprises cmos image sensor, digital signal processor, LCD display screen and FLASH memory, and wherein said cmos image sensor, described LCD display screen and described FLASH memory are connected with described digital signal processor respectively;
Described automatic copying electric meter platform also comprises:
CSI interface, between cmos image sensor and image-preprocessing device, sends image-preprocessing device to for the table block diagram picture taken by described cmos image sensor;
First parallel port, between described digital signal processor and described LCD display screen, the electric energy table reading for being obtained by described digital signal processor sends described LCD display screen to show;
2nd parallel port, between described digital signal processor and described FLASH memory, sends described FLASH memory to store for the electric energy table reading obtained by described digital signal processor;
Power supply, comprise solar powered device, store battery, change-over switch and voltage transitions device, described change-over switch is connected respectively with described solar powered device and described store battery, remain electricity determines whether be switched to described solar powered device to power by described solar powered device according to store battery, described voltage transitions device is connected with described change-over switch, taking the 5V voltage transitions that will be inputted by change-over switch as 3.3V voltage;
Image-preprocessing device, between described CSI interface and described digital signal processor, for receiving described table block diagram picture, described table block diagram picture goes mist process to obtain fogmeter block diagram picture, goes fogmeter block diagram picture to input described digital signal processor to carry out image procossing to obtain electric energy table reading by described;
Described image-preprocessing device also comprises:
Store subset, for prestoring sky upper limit gray scale threshold value and sky lower limit gray scale threshold value, described sky upper limit gray scale threshold value and described sky lower limit gray scale threshold value are for separating of the sky areas published picture in picture, also for prestoring presetted pixel value threshold value, described presetted pixel value threshold value value is between 0 to 255;
Haze Concentration Testing subset, is arranged in air, for detecting the haze concentration of electric energy table position in real time, and determines that intensity removed by haze according to haze concentration, and described haze removes intensity value between 0 to 1;
Region dividing subset, connect described CSI interface to receive described table block diagram picture, described table block diagram picture carries out gray processing process to obtain gray processing table block diagram picture, also it is connected with storage subset, by the pixel identification of gray-scale value in described gray processing table block diagram picture between described sky upper limit gray scale threshold value and described sky lower limit gray scale threshold value and form gray processing sky sub pattern, it is partitioned into described gray processing sky sub pattern to obtain the non-sky subgraph of gray processing from described gray processing table block diagram picture, the colored non-sky subgraph corresponding with described gray processing non-sky subgraph is obtained based on the position of described gray processing non-sky subgraph in described table block diagram picture,
Black channel obtains subset, it is connected with described Region dividing subset to obtain the non-sky subgraph of described colour, for each pixel in the non-sky subgraph of described colour, calculate its R, G, B tri-Color Channel pixel value, the R of all pixels in described colour non-sky subgraph, G, B tri-extracts the Color Channel at the minimum Color Channel pixel value place of numerical value in Color Channel pixel value as black channel;
Overall atmosphere light value obtains subset, it is connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel and it is connected to obtain described table block diagram picture and described black channel respectively, multiple pixels that black channel pixel value in described table block diagram picture is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, described set of pixels to be tested will have the gray-scale value atmosphere light value as a whole of the pixel of maximum gradation value;
Atmospheric scattering light value obtains subset, it is connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described table block diagram picture, extract its R, G, in B tri-Color Channel pixel value, minimum value is as target pixel value, use and keep the Gaussian filter EPGF at edge described target pixel value to carry out filtering process to obtain filtering target pixel value, target pixel value is subtracted filtering target pixel value to obtain object pixel difference, EPGF is used object pixel difference to carry out filtering process to obtain filtering object pixel difference, filtering target pixel value is subtracted filtering object pixel difference to obtain haze removal benchmark value, haze is removed intensity and is multiplied by haze removal benchmark value to obtain haze removal threshold value, get haze and remove the minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximum value in comparison reference and 0 as each pixel,
Medium transmission rate obtains subset, obtain subset with described overall atmosphere light value acquisition subset and described atmospheric scattering light value to be connected respectively, by the atmospheric scattering light value of each pixel divided by overall atmosphere light value to obtain except value, subtract described except value is to obtain the medium transmission rate of each pixel by 1;
Sharpening Image Acquisition subset, with described Region dividing subset, described overall atmosphere light value obtains subset and described medium transmission rate acquisition subset connects respectively, the medium transmission rate subtracting each pixel by 1 is to obtain the first difference, described first difference is multiplied by overall atmosphere light value to obtain product value, the pixel value of each pixel in described table block diagram picture is subtracted described product value to obtain the 2nd difference, by described 2nd difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described table block diagram picture, the pixel value of each pixel comprises the R of each pixel in described table block diagram picture, G, B tri-Color Channel pixel value, correspondingly, the sharpening pixel value of each pixel obtained comprises the R of each pixel, G, B tri-Color Channel sharpening pixel value, the sharpening pixel value composition of all pixels removes fogmeter block diagram picture,
Wherein, described haze Concentration Testing subset is also built-in with static storage cell, and for prestoring relation synopsis, described relation synopsis saves haze concentration and the corresponding relation between intensity removed by haze;
The table block diagram picture of described cmos image sensor shooting sends image-preprocessing device to, described table block diagram picture is gone mist to process to obtain fogmeter block diagram picture by described image-preprocessing device, by described go fogmeter block diagram picture to input described digital signal processor after carry out image sharpening, self-adaptation recurrence filtering and OCR identifying processing successively, to obtain electric energy table reading;
Described cmos image sensor is the OV7640 sensor of Omnivision company;
Described Region dividing subset, described black channel obtain subset, described overall atmosphere light value obtains subset, described atmospheric scattering light value obtains subset, described medium transmission rate obtains subset and described sharpening Image Acquisition subset is integrated on one piece of circuit card.
CN201610122882.8A 2015-04-01 2015-04-01 Method for automatically reading electric energy meter by automatic meter reading platform based on image acquisition Withdrawn CN105635546A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510152400.9A CN104702920B (en) 2015-04-01 2015-04-01 Based on cmos image collection, electric energy meter is carried out to the method for automatic data logging

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
CN201510152400.9A Division CN104702920B (en) 2015-04-01 2015-04-01 Based on cmos image collection, electric energy meter is carried out to the method for automatic data logging

Publications (1)

Publication Number Publication Date
CN105635546A true CN105635546A (en) 2016-06-01

Family

ID=53349647

Family Applications (7)

Application Number Title Priority Date Filing Date
CN201510152400.9A Expired - Fee Related CN104702920B (en) 2015-04-01 2015-04-01 Based on cmos image collection, electric energy meter is carried out to the method for automatic data logging
CN201610122882.8A Withdrawn CN105635546A (en) 2015-04-01 2015-04-01 Method for automatically reading electric energy meter by automatic meter reading platform based on image acquisition
CN201510631355.5A Expired - Fee Related CN105120244B (en) 2015-04-01 2015-04-01 Based on the method that electric energy meter is carried out automatic data logging by cmos image collection
CN201610122879.6A Withdrawn CN105809165A (en) 2015-04-01 2015-04-01 Method of using a meter reading platform to automatically read meters on electric energy meter
CN201610122883.2A Withdrawn CN105635692A (en) 2015-04-01 2015-04-01 Method for automatically reading electric energy meter through automatic meter reading platform based on image acquisition
CN201610122885.1A Withdrawn CN105592253A (en) 2015-04-01 2015-04-01 Method for carrying out automatic meter reading on energy meter based on image acquisition and automatic meter reading platform
CN201610122886.6A Withdrawn CN105578154A (en) 2015-04-01 2015-04-01 Platform and method for automatically reading electric energy meter based on image acquisition

Family Applications Before (1)

Application Number Title Priority Date Filing Date
CN201510152400.9A Expired - Fee Related CN104702920B (en) 2015-04-01 2015-04-01 Based on cmos image collection, electric energy meter is carried out to the method for automatic data logging

Family Applications After (5)

Application Number Title Priority Date Filing Date
CN201510631355.5A Expired - Fee Related CN105120244B (en) 2015-04-01 2015-04-01 Based on the method that electric energy meter is carried out automatic data logging by cmos image collection
CN201610122879.6A Withdrawn CN105809165A (en) 2015-04-01 2015-04-01 Method of using a meter reading platform to automatically read meters on electric energy meter
CN201610122883.2A Withdrawn CN105635692A (en) 2015-04-01 2015-04-01 Method for automatically reading electric energy meter through automatic meter reading platform based on image acquisition
CN201610122885.1A Withdrawn CN105592253A (en) 2015-04-01 2015-04-01 Method for carrying out automatic meter reading on energy meter based on image acquisition and automatic meter reading platform
CN201610122886.6A Withdrawn CN105578154A (en) 2015-04-01 2015-04-01 Platform and method for automatically reading electric energy meter based on image acquisition

Country Status (1)

Country Link
CN (7) CN104702920B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108919724A (en) * 2018-08-09 2018-11-30 南京梵科智能科技有限公司 A kind of Automatic meter reading system based on Internet of Things

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107872608B (en) * 2016-09-26 2021-01-12 华为技术有限公司 Image acquisition device and image processing method
CN110136417A (en) * 2019-05-23 2019-08-16 浙江拥安君智能安防科技有限公司 A kind of automatic meter reading method and equipment applied to Natural gas consumption

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101000652A (en) * 2006-12-31 2007-07-18 沈阳工业大学 Automatic recognising method for digital telemetering image of flow meter and digital telemetering recording system
CN101079094A (en) * 2007-04-30 2007-11-28 中国科学院合肥物质科学研究院 Meter reading identification device for long distance automatic meter reading system
CN101806601A (en) * 2010-03-31 2010-08-18 昆明利普机器视觉工程有限公司 Hand-hold meter reading terminal of meter and meter reading method based on image recognition technology
CN102170574A (en) * 2011-05-23 2011-08-31 北京工业大学 Real-time video defogging system
CN103067512A (en) * 2012-12-31 2013-04-24 邢台县供电有限责任公司 Intelligent meter reading system
WO2013157265A1 (en) * 2012-04-18 2013-10-24 パナソニック株式会社 Image processing system, server device, image pickup device and image evaluation method

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005253023A (en) * 2004-03-08 2005-09-15 Iwao Yuasa Remote surveillance apparatus
JP2006107325A (en) * 2004-10-08 2006-04-20 Tokyo Electric Power Co Inc:The Electric energy meter reading support device, terminal device and method of supporting electric energy meter reading
CN1916933A (en) * 2006-08-31 2007-02-21 合肥新思源智能电子有限公司 Video direct reading type automatic meter reading system, and image processing method
US8600186B2 (en) * 2010-04-26 2013-12-03 City University Of Hong Kong Well focused catadioptric image acquisition
CN102013155B (en) * 2010-10-26 2012-08-08 京信通信系统(中国)有限公司 Remote meter reading system utilizing time division (TD) network and implementation method thereof
CN201955891U (en) * 2011-01-20 2011-08-31 浙江海洋学院 Intelligent direct-reading automatic meter reading system based on image recognition technology
CN202650224U (en) * 2012-06-28 2013-01-02 临城县供电有限责任公司 Power image recognition type meter reading instrument
US9338411B2 (en) * 2012-12-12 2016-05-10 King Fahd University Of Petroleum And Minerals System and method for remote utility meter reading
CN103226816A (en) * 2013-04-10 2013-07-31 成都国腾电子技术股份有限公司 Haze image medium transmission rate estimation and optimization method based on quick gaussian filtering
CN203399143U (en) * 2013-06-05 2014-01-15 广州瀚润计算机信息科技有限公司 Intelligent monitor system for modern city
CN104463160B (en) * 2014-11-27 2017-09-15 泰州市宏祥动力机械有限公司 Spinning and weaving workshop intelligent meter data recording robot

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101000652A (en) * 2006-12-31 2007-07-18 沈阳工业大学 Automatic recognising method for digital telemetering image of flow meter and digital telemetering recording system
CN101079094A (en) * 2007-04-30 2007-11-28 中国科学院合肥物质科学研究院 Meter reading identification device for long distance automatic meter reading system
CN101806601A (en) * 2010-03-31 2010-08-18 昆明利普机器视觉工程有限公司 Hand-hold meter reading terminal of meter and meter reading method based on image recognition technology
CN102170574A (en) * 2011-05-23 2011-08-31 北京工业大学 Real-time video defogging system
WO2013157265A1 (en) * 2012-04-18 2013-10-24 パナソニック株式会社 Image processing system, server device, image pickup device and image evaluation method
CN103067512A (en) * 2012-12-31 2013-04-24 邢台县供电有限责任公司 Intelligent meter reading system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108919724A (en) * 2018-08-09 2018-11-30 南京梵科智能科技有限公司 A kind of Automatic meter reading system based on Internet of Things

Also Published As

Publication number Publication date
CN105592253A (en) 2016-05-18
CN105809165A (en) 2016-07-27
CN105635692A (en) 2016-06-01
CN104702920A (en) 2015-06-10
CN105120244A (en) 2015-12-02
CN105578154A (en) 2016-05-11
CN105120244B (en) 2016-07-13
CN104702920B (en) 2016-03-16

Similar Documents

Publication Publication Date Title
CN104112370B (en) Parking lot based on monitoring image intelligent car position recognition methods and system
CN204789548U (en) Food detection device
CN101701919B (en) Pavement crack detection system based on image and detection method thereof
CN105784710B (en) A kind of glue into concrete beam cracks detection device based on Digital Image Processing
Ling et al. Object-based sub-pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery
CN102621414B (en) Method and system for comprehensively and synchronously observing lightning stroke discharge
CN106716443A (en) Feature computation in a sensor element array
CN202010662U (en) Real-time inspection and grading system device for fruit appearance quality
CN104268498B (en) A kind of recognition methods of Quick Response Code and terminal
CN103290766B (en) Pavement crack detection system
CN105260710B (en) Water meter calibration method, apparatus based on image procossing and system
CN104112269B (en) A kind of solar battery laser groove parameter detection method and system based on machine vision
CN105388162B (en) Raw material silicon chip surface scratch detection method based on machine vision
CN102773862A (en) Quick and accurate locating system used for indoor mobile robot and working method thereof
CN100578192C (en) Apparatus for measuring spectrum of fog drop, and image processing device
CN104730598B (en) A kind of Dust Storm Monitoring method and device
CN104809452A (en) Fingerprint identification method
CN103472501B (en) Cloud detection and all-sky total amount of cloud detection method and system
CN105928598A (en) Method and system for measuring object mass based on photographing
CN106295655B (en) A kind of transmission line part extraction method for unmanned plane inspection image
CN103824544B (en) The bearing calibration of LED display, Apparatus and system
CN103020632B (en) The method for quickly identifying of localization for Mobile Robot monumented point in a kind of indoor environment
CN101625723A (en) Rapid image-recognizing method of power line profile
CN104867159A (en) Stain detection and classification method and device for sensor of digital camera
CN102176228B (en) Machine vision method for identifying dial plate information of multi-pointer instrument

Legal Events

Date Code Title Description
PB01 Publication
C06 Publication
SE01 Entry into force of request for substantive examination
C10 Entry into substantive examination
WW01 Invention patent application withdrawn after publication

Application publication date: 20160601

WW01 Invention patent application withdrawn after publication