CN105120244A - CMOS (Complementary Metal-Oxide-Semiconductor Transistor) image capture-based method capable of automatically reading electric energy meter - Google Patents
CMOS (Complementary Metal-Oxide-Semiconductor Transistor) image capture-based method capable of automatically reading electric energy meter Download PDFInfo
- Publication number
- CN105120244A CN105120244A CN201510631355.5A CN201510631355A CN105120244A CN 105120244 A CN105120244 A CN 105120244A CN 201510631355 A CN201510631355 A CN 201510631355A CN 105120244 A CN105120244 A CN 105120244A
- Authority
- CN
- China
- Prior art keywords
- value
- image
- bezel
- subset
- pixel
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000000295 complement effect Effects 0.000 title abstract 2
- 239000004065 semiconductor Substances 0.000 title abstract 2
- 238000012545 processing Methods 0.000 claims abstract description 47
- 230000005540 biological transmission Effects 0.000 claims description 36
- 238000003379 elimination reaction Methods 0.000 claims description 31
- 239000003595 mist Substances 0.000 claims description 31
- 238000007781 pre-processing Methods 0.000 claims description 27
- 230000008030 elimination Effects 0.000 claims description 25
- 230000008569 process Effects 0.000 claims description 23
- 238000012360 testing method Methods 0.000 claims description 13
- 238000001914 filtration Methods 0.000 claims description 12
- 239000000284 extract Substances 0.000 claims description 8
- 230000006978 adaptation Effects 0.000 claims description 4
- 238000003707 image sharpening Methods 0.000 claims description 4
- 239000000203 mixture Substances 0.000 claims description 4
- 230000011218 segmentation Effects 0.000 claims description 4
- 230000003068 static effect Effects 0.000 claims description 4
- 210000000352 storage cell Anatomy 0.000 claims description 4
- 230000007704 transition Effects 0.000 claims description 4
- 239000004973 liquid crystal related substance Substances 0.000 abstract 1
- 239000004411 aluminium Substances 0.000 description 12
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 12
- 229910052782 aluminium Inorganic materials 0.000 description 12
- 230000000694 effects Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000004907 flux Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/50—Constructional details
- H04N23/55—Optical parts specially adapted for electronic image sensors; Mounting thereof
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R11/00—Electromechanical arrangements for measuring time integral of electric power or current, e.g. of consumption
- G01R11/02—Constructional details
-
- G06T5/73—
-
- 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
-
- 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
- G08—SIGNALLING
- G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
- G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
- G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/10—Cameras or camera modules comprising electronic image sensors; Control thereof for generating image signals from different wavelengths
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/50—Constructional details
- H04N23/54—Mounting of pick-up tubes, electronic image sensors, deviation or focusing coils
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/183—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source
Abstract
The invention relates to a CMOS (Complementary Metal-Oxide-Semiconductor Transistor) image capture-based method capable of automatically reading an electric energy meter. The method comprises: (1) shooting a meter frame of the electric energy meter by a CMOS image sensor; (2) performing image processing on the meter frame image obtained in step (1) by a digital signal processor to obtain a reading of the electric energy meter; (3) storing and displaying the reading of the electric energy meter acquired in step (2) by a Flash storage and an LCD (Liquid Crystal Display) screen. The method is executed by an electric energy meter automatic reading platform based on CMOS image capture, the platform comprises the CMOS image sensor, the digital signal processor, the LCD screen and the FLASH storage, wherein the CMOS image sensor, the LCD screen and the FLASH storage are respectively connected with the digital signal processor. Through the invention, original manual meter reading work can be replaced, influence of a maze climate on meter reading can be overcome, and meter reading efficiency and precision can be improved.
Description
The divisional application that the present invention is application number is 201510152400.9, the applying date is on April 1st, 2015, denomination of invention is the patent of " method of based on cmos image collection, electric energy meter being carried out to automatic data logging ".
Technical field
The present invention relates to electric energy meter field, particularly relate to a kind of method of based on cmos image collection, electric energy meter being carried out to automatic data logging.
Background technology
Electric energy meter is the instrument of measurement electric unit or personal electric reading, it is the important referential data of power supply department charge and allotment electric power resource, the One's name is legion of electric energy meter simultaneously, substantially each needs to arrange an electric energy meter by electric unit, and each family resident also needs to arrange separately an electric energy meter.Thus, as can be seen from value and the characteristic distributions of electric energy meter, the reading collection of electric energy meter needs to meet efficient and high-precision requirement simultaneously.
But all there is intrinsic defect in electric energy meter reading scheme of the prior art: (1), although manual meter reading method can ensure the precision gathering reading, need the cost of labor of at substantial, 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 home; because much electricity table is placed in open air; under the weather that visibility is lower; such as, under foggy days environment; often cause the electric energy meter of shooting image blurring unclear; correspondingly, the error in reading of identification is very large.
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, upgrading is carried out to electronic meter reading mode, increase Sharp processing of image function, overcome the adverse effect of foggy days to electric energy meter Recognition of Reading, thus the efficient and high-precision requirement of electric energy meter reading collection can be met.
Summary of the invention
In order to solve the problem, according to an aspect of the present invention, the invention provides a kind of method of based on cmos image collection, electric energy meter being carried out to automatic data logging, it comprises:
(1) take to obtain bezel, cluster image by the bezel, cluster of cmos image sensor to electric energy meter;
(2) by digital signal processor, image procossing is carried out to the bezel, cluster image that step (1) obtains, to obtain electric energy meter reading;
(3) respectively by FLASH memory and LCD display the electric energy meter reading that step (2) obtains is stored and shown;
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 and FLASH memory, and wherein said cmos image sensor, described LCD display are connected with described digital signal processor respectively with described FLASH memory.
Particularly, described cmos image sensor is used for taking to obtain bezel, cluster image to the bezel, cluster of electric energy meter, described digital signal processor is connected with described cmos image sensor, for carrying out image procossing to described bezel, cluster image, to obtain electric energy meter reading, described FLASH memory is connected with described digital signal processor, for storing described electric energy meter reading, described LCD display is connected with described digital signal processor, for showing described electric energy meter 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, sends image-preprocessing device to for the bezel, cluster image taken by described cmos image sensor;
First parallel port, between described digital signal processor and described LCD display, the electric energy meter reading for being obtained by described digital signal processor sends described LCD display to show;
Second parallel port, between described digital signal processor and described FLASH memory, the electric energy meter reading for being obtained by described digital signal processor sends described FLASH memory to store;
Power supply, comprise solar powered device, storage battery, diverter switch and electric pressure converter, described diverter switch is connected respectively with described solar powered device and described storage battery, determine whether be switched to described solar powered device to be powered by described solar powered device according to storage battery dump energy, described electric pressure converter is connected with described diverter switch, with the 5V voltage transitions will inputted by diverter switch for 3.3V voltage;
Image-preprocessing device, between described CSI interface and described digital signal processor, for receiving described bezel, cluster image, mist elimination process is carried out to obtain mist elimination bezel, cluster image to described bezel, cluster image, described mist elimination bezel, cluster image is inputted described digital signal processor to carry out image procossing to obtain electric energy meter reading.
More specifically, described image-preprocessing device also comprises:
Store subset, for prestoring sky upper limit gray threshold and sky lower limit gray threshold, described sky upper limit gray threshold and described sky lower limit gray threshold are for separating of the sky areas of publishing 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 meter position in real time, and removes intensity according to haze concentration determination haze, and described haze removes intensity value between 0 to 1;
Region dividing subset, connect described CSI interface to receive described bezel, cluster image, gray processing process is carried out to obtain gray processing bezel, cluster image to described bezel, cluster image, also be connected with storage subset, the pixel identification of gray value in described gray processing bezel, cluster image between described sky upper limit gray threshold and described sky lower limit gray threshold is formed gray processing sky sub pattern, described gray processing sky sub pattern is gone out to obtain the non-sky subgraph of gray processing from described gray processing bezel, cluster Iamge Segmentation, the colour 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 bezel, cluster image,
Black channel obtains subset, be 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 air light value obtains subset, be connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel to be connected respectively to obtain described bezel, cluster image and described black channel, multiple pixels that black channel pixel value in described bezel, cluster image is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, the gray value air light value as a whole of the pixel of maximum gradation value will be had in described set of pixels to be tested;
Atmospheric scattering light value obtains subset, be connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described bezel, cluster image, 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 to carry out filtering process to obtain filtered target pixel value to described target pixel value, target pixel value is deducted filtered target pixel value to obtain object pixel difference, EPGF is used to carry out filtering process to obtain filtered target pixel value difference to object pixel difference, filtered target pixel value is deducted filtered target pixel value difference and remove fiducial value to obtain haze, haze is removed intensity and be multiplied by haze removal fiducial value to obtain haze removal threshold value, get haze and remove minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximum in comparison reference and 0 as each pixel,
Medium transmission rate obtains subset, obtain subset and described atmospheric scattering light value with described overall air light value to obtain subset and be connected respectively, the atmospheric scattering light value of each pixel is removed value divided by overall air light value to obtain, deducts 1 described except value is to obtain the medium transmission rate of each pixel;
Sharpening image acquisition subset, with described Region dividing subset, described overall air light value obtains subset and is connected respectively with described medium transmission rate acquisition subset, the medium transmission rate of each pixel is deducted to obtain the first difference by 1, described first difference is multiplied by overall air light value to obtain product value, the pixel value of each pixel in described bezel, cluster image is deducted described product value to obtain the second difference, by described second difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described bezel, cluster image, the pixel value of each pixel comprises the R of each pixel in described bezel, cluster image, 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 mist elimination bezel, cluster image of all pixels,
Wherein, described haze Concentration Testing subset is also built-in with static storage cell, and for prestoring comparison table, described comparison table saves haze concentration and haze and removes corresponding relation between intensity.
Particularly, the bezel, cluster image of described cmos image sensor shooting sends image-preprocessing device to, described image-preprocessing device carries out mist elimination process to obtain mist elimination bezel, cluster image to described bezel, cluster image, image sharpening, self adaptation recursive filtering and OCR identifying processing is carried out successively, to obtain electric energy meter reading after described mist elimination bezel, cluster image is inputted described digital signal processor.
Particularly, in the process, described cmos image sensor is the OV7640 transducer of Omnivision company.
Particularly, in the process, described Region dividing subset, described black channel obtain subset, described overall air 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 respectively to realize.
Particularly, in the process, described Region dividing subset, described black channel obtain subset, described overall air 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 board.
Particularly, in the process, described Region dividing subset, described black channel obtain subset, described overall air 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, FLASH memory and cmos image sensor, described cmos image sensor is used for taking to obtain bezel, cluster image to the bezel, cluster of electric energy meter, described digital signal processor is connected with described cmos image sensor, for carrying out image procossing to described bezel, cluster image, to obtain electric energy meter reading, described FLASH memory is connected with described digital signal processor, for storing described electric energy meter reading, described LCD display is connected with described digital signal processor, for showing described electric energy meter 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, sends image-preprocessing device to for the bezel, cluster image taken by described cmos image sensor;
First parallel port, between described digital signal processor and described LCD display, the electric energy meter reading for being obtained by described digital signal processor sends described LCD display to show;
Second parallel port, between described digital signal processor and described FLASH memory, the electric energy meter reading for being obtained by described digital signal processor sends described FLASH memory to store;
Power supply, comprise solar powered device, storage battery, diverter switch and electric pressure converter, described diverter switch is connected respectively with described solar powered device and described storage battery, determine whether be switched to described solar powered device to be powered by described solar powered device according to storage battery dump energy, described electric pressure converter is connected with described diverter switch, with the 5V voltage transitions will inputted by diverter switch for 3.3V voltage;
Image-preprocessing device, between described CSI interface and described digital signal processor, for receiving described bezel, cluster image, mist elimination process is carried out to obtain mist elimination bezel, cluster image to described bezel, cluster image, described mist elimination bezel, cluster image is inputted described digital signal processor to carry out image procossing to obtain electric energy meter reading.
More specifically, described image-preprocessing device also comprises:
Store subset, for prestoring sky upper limit gray threshold and sky lower limit gray threshold, described sky upper limit gray threshold and described sky lower limit gray threshold are for separating of the sky areas of publishing 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 meter position in real time, and removes intensity according to haze concentration determination haze, and described haze removes intensity value between 0 to 1;
Region dividing subset, connect described CSI interface to receive described bezel, cluster image, gray processing process is carried out to obtain gray processing bezel, cluster image to described bezel, cluster image, also be connected with storage subset, the pixel identification of gray value in described gray processing bezel, cluster image between described sky upper limit gray threshold and described sky lower limit gray threshold is formed gray processing sky sub pattern, described gray processing sky sub pattern is gone out to obtain the non-sky subgraph of gray processing from described gray processing bezel, cluster Iamge Segmentation, the colour 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 bezel, cluster image,
Black channel obtains subset, be 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 air light value obtains subset, be connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel to be connected respectively to obtain described bezel, cluster image and described black channel, multiple pixels that black channel pixel value in described bezel, cluster image is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, the gray value air light value as a whole of the pixel of maximum gradation value will be had in described set of pixels to be tested;
Atmospheric scattering light value obtains subset, be connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described bezel, cluster image, 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 to carry out filtering process to obtain filtered target pixel value to described target pixel value, target pixel value is deducted filtered target pixel value to obtain object pixel difference, EPGF is used to carry out filtering process to obtain filtered target pixel value difference to object pixel difference, filtered target pixel value is deducted filtered target pixel value difference and remove fiducial value to obtain haze, haze is removed intensity and be multiplied by haze removal fiducial value to obtain haze removal threshold value, get haze and remove minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximum in comparison reference and 0 as each pixel,
Medium transmission rate obtains subset, obtain subset and described atmospheric scattering light value with described overall air light value to obtain subset and be connected respectively, the atmospheric scattering light value of each pixel is removed value divided by overall air light value to obtain, deducts 1 described except value is to obtain the medium transmission rate of each pixel;
Sharpening image acquisition subset, with described Region dividing subset, described overall air light value obtains subset and is connected respectively with described medium transmission rate acquisition subset, the medium transmission rate of each pixel is deducted to obtain the first difference by 1, described first difference is multiplied by overall air light value to obtain product value, the pixel value of each pixel in described bezel, cluster image is deducted described product value to obtain the second difference, by described second difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described bezel, cluster image, the pixel value of each pixel comprises the R of each pixel in described bezel, cluster image, 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 mist elimination bezel, cluster image of all pixels,
Described digital signal processor, after receiving described mist elimination bezel, cluster image, carries out image sharpening, self adaptation recursive filtering and OCR identifying processing, to obtain electric energy meter reading successively to described mist elimination bezel, cluster image;
Wherein, described haze Concentration Testing subset is also built-in with static storage cell, and for prestoring comparison table, described comparison table saves haze concentration and haze and removes corresponding relation between intensity.
More specifically, in the described automatic copying electric meter platform gathered based on cmos image, described cmos image sensor is the OV7640 transducer 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 air 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 air 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 board.
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 air 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.
Of the present invention based on cmos image collection to electric energy meter carry out automatic data logging method and based on cmos image gather automatic copying electric meter platform, utilize high-precision acquisition technology and image processing techniques, ensure the high efficiency of data acquisition, simultaneously according to atmospheric attenuation model determination haze to the influencing factor of image, and the process of mist elimination haze is carried out to the electric energy meter image gathered under foggy days, obtain electric energy meter image 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 based on cmos image collection illustrated according to an embodiment of the present invention.
Embodiment
Below with reference to accompanying drawings the embodiment of the automatic copying electric meter platform based on 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 electric energy is electric energy meter.
The operation principle of electric energy meter is as follows: when electric energy meter access circuit-under-test, just have alternating current to flow through in current coil and potential winding, these two alternating currents 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 of load consumption is larger, larger by the electric current of current coil, and the eddy current induced in aluminium dish is also larger, and the moment that aluminium dish is rotated is larger.Namely the size of torque follows the power of load consumption to be directly proportional.Power is larger, and torque is also larger, and aluminium dish rotates also faster.When aluminium dish rotates, be 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 faster, and braking moment is also larger.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, consumed electric energy is indicated.The simple procedure of electric energy meter work that Here it is.
Because electric energy meter has widely distributed, and the correctness of electric energy meter reading is extremely important.Therefore, the collection of electric energy meter reading should be accurate again fast.But electric energy meter 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, also can carry out the collection of high-precision electric energy meter reading under realizing bad weather by the process of mist elimination haze, thus meet the setting requirement of power supply department.
Fig. 1 is the block diagram of the automatic copying electric meter platform based on cmos image collection illustrated according to an embodiment of the present invention, described platform comprises digital signal processor 1, LCD display 4, FLASH memory 3 and cmos image sensor 2, described cmos image sensor 2 is for taking to obtain bezel, cluster image to the bezel, cluster of electric energy meter, described digital signal processor 1 is connected with described cmos image sensor 2, for carrying out image procossing to described bezel, cluster image, to obtain electric energy meter reading, described FLASH memory 3 is connected with described digital signal processor 1, for storing described electric energy meter reading, described LCD display 4 is connected with described digital signal processor 1, for showing described electric energy meter reading.
Then, continue to be further detailed the concrete structure of the automatic copying electric meter platform based on cmos image collection of the present invention.
Described platform also comprises: CSI interface, between cmos image sensor 2 and image-preprocessing device, sends image-preprocessing device to for the bezel, cluster image 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 4, the electric energy meter reading for being obtained by described digital signal processor 1 sends described LCD display 4 to show.
Described platform also comprises: the second parallel port, and between described digital signal processor 1 and described FLASH memory 3, the electric energy meter 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, storage battery, diverter switch and electric pressure converter, described diverter switch is connected respectively with described solar powered device and described storage battery, determine whether be switched to described solar powered device to be powered by described solar powered device according to storage battery dump energy, described electric pressure converter is connected with described diverter switch, with the 5V voltage transitions will inputted by diverter switch for 3.3V voltage.
Described platform also comprises: image-preprocessing device, between described CSI interface and described digital signal processor 1, for receiving described bezel, cluster image, mist elimination process is carried out to obtain mist elimination bezel, cluster image to described bezel, cluster image, described mist elimination bezel, cluster image is inputted described digital signal processor 1 to carry out image procossing to obtain electric energy meter reading.
Described image-preprocessing device also comprises: store subset, for prestoring sky upper limit gray threshold and sky lower limit gray threshold, described sky upper limit gray threshold and described sky lower limit gray threshold are for separating of the sky areas of publishing picture in picture, 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 meter position in real time, and removes intensity according to haze concentration determination haze, 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 bezel, cluster image, gray processing process is carried out to obtain gray processing bezel, cluster image to described bezel, cluster image, also be connected with storage subset, the pixel identification of gray value in described gray processing bezel, cluster image between described sky upper limit gray threshold and described sky lower limit gray threshold is formed gray processing sky sub pattern, described gray processing sky sub pattern is gone out to obtain the non-sky subgraph of gray processing from described gray processing bezel, cluster Iamge Segmentation, the colour 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 bezel, cluster image.
Described image-preprocessing device also comprises: black channel obtains subset, be 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 air light value obtains subset, be connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel to be connected respectively to obtain described bezel, cluster image and described black channel, multiple pixels that black channel pixel value in described bezel, cluster image is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, the gray value air light value as a whole of the pixel of maximum gradation value will be had in described set of pixels to be tested.
Described image-preprocessing device also comprises: atmospheric scattering light value obtains subset, be connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described bezel, cluster image, 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 to carry out filtering process to obtain filtered target pixel value to described target pixel value, target pixel value is deducted filtered target pixel value to obtain object pixel difference, EPGF is used to carry out filtering process to obtain filtered target pixel value difference to object pixel difference, filtered target pixel value is deducted filtered target pixel value difference and remove fiducial value to obtain haze, haze is removed intensity and be multiplied by haze removal fiducial value to obtain haze removal threshold value, get haze and remove minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximum in comparison reference and 0 as each pixel.
Described image-preprocessing device also comprises: medium transmission rate obtains subset, obtain subset and described atmospheric scattering light value with described overall air light value to obtain subset and be connected respectively, the atmospheric scattering light value of each pixel is removed value divided by overall air light value to obtain, deducts 1 described except value is to obtain the medium transmission rate of each pixel.
Described image-preprocessing device also comprises: sharpening image acquisition subset, with described Region dividing subset, described overall air light value obtains subset and is connected respectively with described medium transmission rate acquisition subset, the medium transmission rate of each pixel is deducted to obtain the first difference by 1, described first difference is multiplied by overall air light value to obtain product value, the pixel value of each pixel in described bezel, cluster image is deducted described product value to obtain the second difference, by described second difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described bezel, cluster image, the pixel value of each pixel comprises the R of each pixel in described bezel, cluster image, 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 mist elimination bezel, cluster image of all pixels.
Described digital signal processor 1, after receiving described mist elimination bezel, cluster image, carries out image sharpening, self adaptation recursive filtering and OCR identifying processing, to obtain electric energy meter reading successively to described mist elimination bezel, cluster image.
Wherein, described haze Concentration Testing subset is also built-in with static storage cell, and for prestoring comparison table, described comparison table saves haze concentration and haze and removes corresponding relation between intensity.
More specifically, in the described automatic copying electric meter platform gathered based on cmos image, described cmos image sensor is the OV7640 transducer of Omnivision company.
Wherein, in described platform, described Region dividing subset, described black channel obtain subset, described overall air 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 air 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 board; Alternatively, described Region dividing subset, described black channel obtain subset, described overall air 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 realize the mist elimination haze of image by a series of images treatment facility, to obtain the image of sharpening, improves the visibility 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 civil area comprises road monitoring, target following 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 air light value and each pixel, namely when known haze image, according to the medium transmission rate of overall air light value with each pixel, sharpening image can be solved.
There are some effective and through verifying means in the solving of medium transmission rate for overall air light value and each pixel, such as, for the medium transmission rate of each pixel, need the atmospheric scattering light value obtaining overall air light value and each pixel, and the atmospheric scattering light value of each pixel can obtain carrying out the Gaussian smoothing filter at twice maintenance edge to the pixel value of each pixel in haze image, therebetween, the intensity that haze is removed is adjustable, and overall air light value (namely makes the black channel value of some pixels very low by the black channel obtaining haze image in haze image, black channel is R, G, one in B tri-Color Channel), in haze image, obtain by finding the maximum pixel of gray value in multiple pixels that searching black channel pixel value is bigger than normal, be about to search out, the gray value air light value as a whole of the pixel that gray 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 parameters can see above content.
By the discussion to haze image formation basic theory, build the relation between haze image and sharpening image, by this relation of multiple Parametric Representation, subsequently by the multiple parameter values that obtain and haze image and the higher image of reducible acquisition definition, some statistical means and empirical means have been used in acquisition due to parameter, therefore the image that described definition is higher can not be equal to real image completely, but there is the mist elimination haze effect of certain degree, for the every field operation under haze weather provides effective guarantee.
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 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 additional interface control line of parallel data transmission or claiming the characteristic of interactive 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 at bad weather for existing electric energy meter reading ineffective systems, by adopting IMAQ and image processing techniques targetedly, substitute manual metering mode, improve meter reading efficiency, simultaneously, the image preprocess apparatus added based on atmospheric attenuation model realizes the process of mist elimination haze, reduce haze on the impact of quality of image of checking meter, avoid electric energy meter reading image data disturbed.
Be understandable that, although the present invention with preferred 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 under technical solution of the present invention ambit, the technology contents of above-mentioned announcement all can be utilized to make many possible variations and modification to technical solution of the present invention, or be revised as the Equivalent embodiments of equivalent variations.Therefore, every content not departing from technical solution of the present invention, according to technical spirit of 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 (3)
1. based on cmos image collection, electric energy meter is carried out to a method for automatic data logging, it comprises:
(1) take to obtain bezel, cluster image by the bezel, cluster of cmos image sensor to electric energy meter;
(2) by digital signal processor, image procossing is carried out to the bezel, cluster image that step (1) obtains, to obtain electric energy meter reading;
(3) respectively by FLASH memory and LCD display the electric energy meter reading that step (2) obtains is stored and shown.
2. the method for claim 1, 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 and FLASH memory, and wherein said cmos image sensor, described LCD display are connected with described digital signal processor respectively with described FLASH memory.
3. method as claimed in claim 2, wherein said automatic copying electric meter platform also comprises:
CSI interface, between cmos image sensor and image-preprocessing device, sends image-preprocessing device to for the bezel, cluster image taken by described cmos image sensor;
First parallel port, between described digital signal processor and described LCD display, the electric energy meter reading for being obtained by described digital signal processor sends described LCD display to show;
Second parallel port, between described digital signal processor and described FLASH memory, the electric energy meter reading for being obtained by described digital signal processor sends described FLASH memory to store;
Power supply, comprise solar powered device, storage battery, diverter switch and electric pressure converter, described diverter switch is connected respectively with described solar powered device and described storage battery, determine whether be switched to described solar powered device to be powered by described solar powered device according to storage battery dump energy, described electric pressure converter is connected with described diverter switch, with the 5V voltage transitions will inputted by diverter switch for 3.3V voltage;
Image-preprocessing device, between described CSI interface and described digital signal processor, for receiving described bezel, cluster image, mist elimination process is carried out to obtain mist elimination bezel, cluster image to described bezel, cluster image, described mist elimination bezel, cluster image is inputted described digital signal processor to carry out image procossing to obtain electric energy meter reading;
Described image-preprocessing device also comprises:
Store subset, for prestoring sky upper limit gray threshold and sky lower limit gray threshold, described sky upper limit gray threshold and described sky lower limit gray threshold are for separating of the sky areas of publishing 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 meter position in real time, and removes intensity according to haze concentration determination haze, and described haze removes intensity value between 0 to 1;
Region dividing subset, connect described CSI interface to receive described bezel, cluster image, gray processing process is carried out to obtain gray processing bezel, cluster image to described bezel, cluster image, also be connected with storage subset, the pixel identification of gray value in described gray processing bezel, cluster image between described sky upper limit gray threshold and described sky lower limit gray threshold is formed gray processing sky sub pattern, described gray processing sky sub pattern is gone out to obtain the non-sky subgraph of gray processing from described gray processing bezel, cluster Iamge Segmentation, the colour 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 bezel, cluster image,
Black channel obtains subset, be 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 air light value obtains subset, be connected to obtain presetted pixel value threshold value with described storage subset, obtain subset with described Region dividing subset and described black channel to be connected respectively to obtain described bezel, cluster image and described black channel, multiple pixels that black channel pixel value in described bezel, cluster image is more than or equal to presetted pixel value threshold value are formed set of pixels to be tested, the gray value air light value as a whole of the pixel of maximum gradation value will be had in described set of pixels to be tested;
Atmospheric scattering light value obtains subset, be connected respectively with described Region dividing subset and described haze Concentration Testing subset, to each pixel of described bezel, cluster image, 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 to carry out filtering process to obtain filtered target pixel value to described target pixel value, target pixel value is deducted filtered target pixel value to obtain object pixel difference, EPGF is used to carry out filtering process to obtain filtered target pixel value difference to object pixel difference, filtered target pixel value is deducted filtered target pixel value difference and remove fiducial value to obtain haze, haze is removed intensity and be multiplied by haze removal fiducial value to obtain haze removal threshold value, get haze and remove minimum value in threshold value and target pixel value as comparison reference, get the atmospheric scattering light value of the maximum in comparison reference and 0 as each pixel,
Medium transmission rate obtains subset, obtain subset and described atmospheric scattering light value with described overall air light value to obtain subset and be connected respectively, the atmospheric scattering light value of each pixel is removed value divided by overall air light value to obtain, deducts 1 described except value is to obtain the medium transmission rate of each pixel;
Sharpening image acquisition subset, with described Region dividing subset, described overall air light value obtains subset and is connected respectively with described medium transmission rate acquisition subset, the medium transmission rate of each pixel is deducted to obtain the first difference by 1, described first difference is multiplied by overall air light value to obtain product value, the pixel value of each pixel in described bezel, cluster image is deducted described product value to obtain the second difference, by described second difference divided by the medium transmission rate of each pixel to obtain the sharpening pixel value of each pixel, in described bezel, cluster image, the pixel value of each pixel comprises the R of each pixel in described bezel, cluster image, 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 mist elimination bezel, cluster image of all pixels,
Wherein, described haze Concentration Testing subset is also built-in with static storage cell, and for prestoring comparison table, described comparison table saves haze concentration and haze and removes corresponding relation between intensity;
The bezel, cluster image of described cmos image sensor shooting sends image-preprocessing device to, described image-preprocessing device carries out mist elimination process to obtain mist elimination bezel, cluster image to described bezel, cluster image, image sharpening, self adaptation recursive filtering and OCR identifying processing is carried out successively, to obtain electric energy meter reading after described mist elimination bezel, cluster image is inputted described digital signal processor.
Applications Claiming Priority (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 (2)
| Publication Number | Publication Date |
|---|---|
| CN105120244A true CN105120244A (en) | 2015-12-02 |
| CN105120244B CN105120244B (en) | 2016-07-13 |
Family
ID=53349647
Family Applications (7)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| 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 |
| 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 |
| CN201610122886.6A Withdrawn CN105578154A (en) | 2015-04-01 | 2015-04-01 | Platform and method for automatically reading electric energy meter 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 |
| 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 |
| 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 |
| 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 |
Family Applications Before (4)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| 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 |
| 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 |
| CN201610122886.6A Withdrawn CN105578154A (en) | 2015-04-01 | 2015-04-01 | Platform and method for automatically reading electric energy meter 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 |
Family Applications After (2)
| 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 |
| 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 |
Country Status (1)
| Country | Link |
|---|---|
| CN (7) | CN105635692A (en) |
Families Citing this family (4)
| 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 |
| 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 |
| 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 |
| CN113139562A (en) * | 2020-01-19 | 2021-07-20 | 中移物联网有限公司 | Ammeter reading method, meter reading terminal and meter reading platform |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
| 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 |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005253023A (en) * | 2004-03-08 | 2005-09-15 | Iwao Yuasa | Remote surveillance apparatus |
| 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 |
| CN100498819C (en) * | 2006-12-31 | 2009-06-10 | 沈阳工业大学 | Automatic recognising method for digital telemetering image of flow meter and digital telemetering recording 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 |
| 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 |
| CN102170574B (en) * | 2011-05-23 | 2012-12-26 | 北京工业大学 | Real-time video defogging system |
| JP6094903B2 (en) * | 2012-04-18 | 2017-03-15 | パナソニックＩｐマネジメント株式会社 | Receiving apparatus and receiving side image processing method |
| 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 |
| CN103067512A (en) * | 2012-12-31 | 2013-04-24 | 邢台县供电有限责任公司 | Intelligent meter reading system |
| 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 |
| 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 |
-
2015
- 2015-04-01 CN CN201610122883.2A patent/CN105635692A/en not_active Withdrawn
- 2015-04-01 CN CN201610122882.8A patent/CN105635546A/en not_active Withdrawn
- 2015-04-01 CN CN201610122886.6A patent/CN105578154A/en not_active Withdrawn
- 2015-04-01 CN CN201610122885.1A patent/CN105592253A/en not_active Withdrawn
- 2015-04-01 CN CN201510631355.5A patent/CN105120244B/en not_active Expired - Fee Related
- 2015-04-01 CN CN201510152400.9A patent/CN104702920B/en not_active Expired - Fee Related
- 2015-04-01 CN CN201610122879.6A patent/CN105809165A/en not_active Withdrawn
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
| 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 |
Non-Patent Citations (1)
| Title |
|---|
| 唐慧强等: "《无线网络化抄表与节能控制系统设计》", 《测控技术》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105635692A (en) | 2016-06-01 |
| CN105635546A (en) | 2016-06-01 |
| CN105809165A (en) | 2016-07-27 |
| CN105120244B (en) | 2016-07-13 |
| CN104702920B (en) | 2016-03-16 |
| CN104702920A (en) | 2015-06-10 |
| CN105592253A (en) | 2016-05-18 |
| CN105578154A (en) | 2016-05-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104715488A (en) | Method for detecting damage degree of outdoor electric energy meter | |
| CN104702920B (en) | Based on cmos image collection, electric energy meter is carried out to the method for automatic data logging | |
| CN104700101B (en) | Electric energy meter model detection method based on visual recognition | |
| CN107766861A (en) | The recognition methods of character image clothing color, device and electronic equipment | |
| CN106373365A (en) | Camera shooting type remote water meter reading system achieving fast recognition and reading recognition method thereof | |
| CN105372324B (en) | The defects of based on magneto-optic imaging method detection method | |
| CN102901444A (en) | Method for detecting component size based on matching pursuit (MP) wavelet filtering and detecting system thereof | |
| CN107247914A (en) | A kind of visual identifying system of direct-reading water meter and its recognition methods | |
| CN104735418B (en) | Electric energy meter wireless kilowatt meter reading-out system | |
| CN104700425A (en) | Detecting platform for damaged degrees of outdoor electric energy meter | |
| CN105791778A (en) | CMOS image acquisition-based watt-hour meter automatic meter reading platform | |
| CN115019297B (en) | Real-time license plate detection and identification method and device based on color augmentation | |
| CN105791755A (en) | Method for automatically reading power meter by using automatic meter reading platform based on image collection | |
| CN104735420B (en) | Based on the automatic copying electric meter platform that cmos image gathers | |
| CN105657230A (en) | Method for automatically reading electric energy meter through automatic meter reading platform | |
| CN104714068A (en) | Robot for outdoor electric energy meter to read meter | |
| CN104700107A (en) | Electric energy meter reading recognition apparatus based on image processing | |
| CN105791779A (en) | CMOS image acquisition-based watt-hour meter automatic meter reading platform | |
| CN105828042A (en) | CMOS image collection-based electric energy meter automatic meter reading platform | |
| CN105791759A (en) | Method for automatically reading power meter based on image collection | |
| CN105791758A (en) | Method for automatically reading power meter | |
| CN105791756A (en) | Power meter automatic reading platform based on image collection and automatic meter reading method | |
| CN105791757A (en) | Power meter automatic reading platform and automatic meter reading method thereof | |
| CN105740849A (en) | Method for performing automatic data logging on electric energy meter based on image collection | |
| Wang et al. | Water meter digital timing recognition system based on edge calculation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C41 | Transfer of patent application or patent right or utility model | ||
| CB03 | Change of inventor or designer information |
Inventor after: Hu Xiangbin Inventor after: Yu Xiaolei Inventor after: Yi Jingjing Inventor after: Li Chunzhi Inventor after: Miao Shuping Inventor after: Gao Tongwen Inventor after: Xie Xuejun Inventor after: Gong Longteng Inventor after: Hu Yuntao Inventor after: Feng Jianmin Inventor after: Wen Hui Inventor before: The inventor has waived the right to be mentioned |
|
| COR | Change of bibliographic data | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20160612 Address after: 272499 No. 29, Wenhua Road, Jiaxiang County, Shandong, Jining Applicant after: JIAXIANG POWER SUPPLY COMPANY, STATE GRID SHANDONG ELECTRIC POWER COMPANY Applicant after: State Grid Corporation of China Address before: 211106, No. 19, general road, Jiangning economic and Technological Development Zone, Nanjing, Jiangsu Applicant before: Hao Hongjuan |
|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20160713 Termination date: 20170401 |