CN110782432B - Automatic defogging method for image monitoring device - Google Patents
Automatic defogging method for image monitoring device Download PDFInfo
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- CN110782432B CN110782432B CN201910977788.4A CN201910977788A CN110782432B CN 110782432 B CN110782432 B CN 110782432B CN 201910977788 A CN201910977788 A CN 201910977788A CN 110782432 B CN110782432 B CN 110782432B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G03—PHOTOGRAPHY; CINEMATOGRAPHY; ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ELECTROGRAPHY; HOLOGRAPHY
- G03B—APPARATUS OR ARRANGEMENTS FOR TAKING PHOTOGRAPHS OR FOR PROJECTING OR VIEWING THEM; APPARATUS OR ARRANGEMENTS EMPLOYING ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ACCESSORIES THEREFOR
- G03B17/00—Details of cameras or camera bodies; Accessories therefor
- G03B17/55—Details of cameras or camera bodies; Accessories therefor with provision for heating or cooling, e.g. in aircraft
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
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Abstract
The invention relates to the technical field of image monitoring of power transmission lines, in particular to an automatic defogging method of an image monitoring device sum And the dispersion sigma of the matrix T, and then judging the total intensity T of the characteristic value sum And a set characteristic value threshold value theta th Dispersion sigma of matrix T and set dispersion threshold sigma th If the dispersion sigma of the matrix T is smaller than the dispersion threshold sigma th And the total intensity of the characteristic value T sum Less than a set threshold value theta th The water mist is condensed on the front glass of the lens of the image monitoring device, and the front glass of the lens is electrified and heated, so that the water mist or water drops on the front glass of the lens are evaporated, the accuracy of judging the water mist or water drops in the front glass of the lens is improved, and meanwhile, the automatic defogging control function is realized.
Description
Technical Field
The invention relates to the technical field of image monitoring of power transmission lines, in particular to an automatic defogging method for an image monitoring device.
Background
In recent years, with the development of science and technology, high-voltage transmission lines in China make great progress in monitoring intellectualization and visualization. At present, most of special image monitoring devices for electric power have AI identification capability, and can identify external damage risk factors such as tower cranes, ultrahigh trees and the like. The image monitoring device of the power transmission line is generally arranged on a field power transmission tower and is powered by solar energy and a battery. Day and night temperature difference and local environment's meteorological change lead to condensation drop on the monitoring device lens glass board easily and produce the water smoke, can seriously influence the camera lens formation of image, and the monitoring image can become very fuzzy unclear. If at this moment, illegal operation or other external force damage behaviors exist around the power transmission line, if the problem cannot be found in time, the consequences will be serious.
Most of the prior electric transmission line image monitoring devices do not have corresponding demisting measures. Like the individual security camera of the image monitoring device, the heater and the fan are arranged inside the individual security camera, and the defogging function can only be manually started and closed, so that the individual security camera is very inconvenient in practical application. In the field of security, a great number of cameras adopt a camera system similar to the camera system in the patent application number: 201210302406.6, which automatically controls the defogging function to be turned on or off by combining a temperature and humidity sensor with different judgment logics. According to the scheme, misjudgment is easy to occur, electric energy is wasted, or a defogging effect cannot be achieved.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method overcomes the defects of the prior art and provides the automatic defogging method of the image monitoring device with the automatic defogging control function.
The technical scheme adopted by the invention for solving the technical problem is as follows: the automatic defogging method for the image monitoring device comprises the following steps:
step 1: collecting an image;
and 2, step: calculating a characteristic value matrix T of the image pixel;
and 3, step 3: calculating the total eigenvalue intensity T of the eigenvalue matrix T sum And the dispersion σ of the matrix T;
and 4, step 4: respectively judging the total intensity T of the characteristic values sum And a set characteristic value threshold value theta th Dispersion sigma of matrix T and set dispersion threshold sigma th If the dispersion sigma of the matrix T is smaller than the dispersion threshold sigma th And the total intensity of the characteristic value T sum Less than a set threshold value theta th The water mist is condensed on the front glass of the lens of the image monitoring device, otherwise, the water mist does not exist;
and 5: when the water mist is condensed on the lens front glass of the image monitoring device, judging whether the lens front glass is heated or not, and if the lens front glass is not heated, electrifying and heating the lens front glass;
and 6: mirror with mirror headContinuous statistical analysis of total intensity T of characteristic value in heating process of front glass sum If the total intensity T of the characteristic value is sum And when no obvious change exists in the set time period, the front glass of the lens is powered off and heating is stopped.
The method calculates the total intensity T of the characteristic value of the acquired image sum Comparing the dispersion sigma of the matrix T with a correspondingly set threshold value to judge whether the front glass of the lens condenses the water fog column, wherein the total intensity T of the characteristic value sum The comparative analysis of the matrix T is mainly used for eliminating abnormal conditions of lens shielding, haze weather and the like similar to water mist condensation and reducing the probability of misjudgment. When judging that glass condenses to have the water smoke before the camera lens, to glass ohmic heating before the camera lens for the water smoke, the drop evaporation that glass condenses before the camera lens, thereby realize the function of image prison device defogging, make the image of prison shooting clear, the staff of being convenient for discerns the hidden danger that exists in the image.
The step 2 comprises the following substeps:
2-1): acquiring gray data of an image and expressing the gray data by using a two-dimensional matrix to obtain a gray image matrix Y;
2-2): multiplying the gray level image matrix Y with a set filter matrix M to obtain a characteristic value of each pixel point of the image;
2-3): the image matrix is divided into a plurality of n multiplied by n dimensional sub-windows, and a characteristic value matrix T of each sub-window is obtained, wherein n is a natural number.
The filtering matrix M is an enhanced filtering matrix obtained by sequentially cascading a plurality of k x k dimensionality filtering matrixes, and k is a natural number.
The filter matrix M is obtained by sequentially cascading 3 filter matrices of 5 multiplied by 5 dimensionalities, and the calculation formula of the filter matrix M is as follows:
wherein:
The sub-window is an 8 x 8 dimensional sub-window.
Calculating the total intensity T of the characteristic value in the step 3 sum Obtained by summing the eigenvalue matrix T of each sub-window.
According to the characteristic value total intensity T sum Calculating the mean value T avg According to the mean value T avg Calculating the dispersion sigma of the eigenvalue matrix T of each sub-window, wherein the calculation formula of the dispersion sigma is as follows:
in the formula, i is the row number of the eigenvalue matrix T, and j is the column number of the eigenvalue matrix T.
Compared with the prior art, the invention has the following beneficial effects:
the automatic defogging method of the image monitoring device provided by the invention realizes a full-automatic defogging control function, does not need human interference, detects and analyzes whether water mist or water drops exist in the front glass of the lens according to the collected image, and further can heat the front glass of the lens when the water mist or water drops exist, so that the water mist or water drops are evaporated, the accuracy of detecting whether the water mist or water drops exist is higher, and electric power operation and maintenance personnel can save worry and labor and work conveniently.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
Embodiments of the invention are further described below with reference to the accompanying drawings:
examples
As shown in fig. 1, the method for automatically defogging an image surveillance device includes the following steps:
step 1: collecting an image;
step 2: calculating a characteristic value matrix T of the image pixel, wherein the step 2 comprises the following sub-steps:
2-1): acquiring gray scale data of an image and expressing the gray scale data by a two-dimensional matrix to obtain a gray scale image matrix Y,
2-2): and multiplying the gray level image matrix Y with a set filter matrix M to obtain a characteristic value of each pixel point of the image, wherein the filter matrix M is an enhanced filter matrix obtained by sequentially cascading a plurality of k multiplied by k dimensionality filter matrices, and k is a natural number. The filter matrix M in this embodiment is obtained by sequentially cascading 3 filter matrices of 5 × 5 dimensions, and the calculation formula of the filter matrix M is as follows:
wherein:
If 3 filtering matrixes of 3 × 3 dimensions are sequentially cascaded to obtain the filtering matrix M, the characteristic effect of obtaining the characteristic value of each pixel point is not good as that of the filtering matrix M obtained by sequentially cascading the filtering matrixes of 5 × 5 dimensions, but the filtering matrix M obtained by sequentially cascading the filtering matrixes of 7 × 7 and other larger dimensions is adopted, so that the resource cost is high, and the calculation time is long, therefore, the filtering matrix M obtained by sequentially cascading the filtering matrixes of 3 × 5 dimensions is preferred, the characteristic effect of the extracted characteristic value can be ensured, and the resource cost and the calculation time can be controlled within a reasonable range.
2-3): the image matrix is divided into a plurality of n × n dimensional sub-windows, a characteristic value matrix T of each sub-window is obtained, n is a natural number, and the sub-window is set to be an 8 × 8 dimensional sub-window in this embodiment.
And step 3: calculating the total eigenvalue intensity T of the eigenvalue matrix T sum And the dispersion sigma of the matrix T, and calculating the total intensity T of the characteristic value sum Obtained by summing the eigenvalue matrix T of each sub-window according to the total intensity T of the eigenvalues sum Calculating the mean value T avg According to the mean value T avg Calculating the dispersion sigma of the eigenvalue matrix T of each sub-window, wherein the calculation formula of the dispersion sigma is as follows:
in the formula, i is the row number of the eigenvalue matrix T, j is the column number of the eigenvalue matrix T, in this embodiment, since an 8 × 8 dimensional sub-window is set, i < 8 and j < 8 are set in the calculation formula of the dispersion σ, that is, the values of i and j in the formula are both smaller than the dimension n of the actual sub-window.
And 4, step 4: respectively judging the total intensity T of the characteristic values sum With a set threshold value theta of the characteristic value th Dispersion sigma and a set dispersion threshold sigma th If the dispersion sigma is smaller than the dispersion threshold sigma th And total intensity of characteristic value T sum Less than a set threshold value theta th The water mist is condensed on the front glass of the lens of the image monitoring device, otherwise, the water mist does not exist;
and 5: when the water mist is condensed on the lens front glass of the image monitoring device, judging whether the lens front glass is heated or not, and if the lens front glass is not heated, electrifying and heating the lens front glass;
step 6: continuous statistical analysis of total intensity T of characteristic value in heating process of front glass of lens sum If the total intensity T of the characteristic value is sum And if no obvious change exists in the set time period, the lens front glass is powered off and heating is stopped.
The image monitoring device comprises a power supply battery, a main control module, an image acquisition module, a lens and lens front glass, wherein the image acquisition module adopts a CMOS image sensor, the main control module is used for carrying out ISP processing, image analysis, logic operation, control instruction sending and the like on an image, the lens front glass is electric heating transparent glass, and the power supply battery supplies power to each module.
The method calculates the total intensity T of the characteristic value of the acquired image sum Comparing the dispersion sigma of the matrix T with a correspondingly set threshold value to judge whether the front glass of the lens condenses the water fog column, wherein the total intensity T of the characteristic value sum The comparative analysis of the dispersion sigma mainly aims to eliminate abnormal conditions of lens shielding, haze weather and the like similar to water mist condensation, and reduces the probability of misjudgment. When judging that glass condenses to have the water smoke before the camera lens, to glass ohmic heating before the camera lens for the water smoke, the drop evaporation that glass condenses before the camera lens, thereby realize the function of image prison device defogging, make the image of prison shooting clear, the staff of being convenient for discerns the hidden danger that exists in the image.
Claims (5)
1. An automatic defogging method for an image monitoring device is characterized by comprising the following steps:
step 1: collecting an image;
step 2: calculating a characteristic value matrix T of the image pixel; the step 2 comprises the following substeps:
2-1): acquiring gray data of an image and expressing the gray data by using a two-dimensional matrix to obtain a gray image matrix Y;
2-2): multiplying the gray level image matrix Y with a set filter matrix M to obtain a characteristic value of each pixel point of the image;
2-3): dividing an image matrix into a plurality of n multiplied by n dimensional sub-windows, and acquiring a characteristic value matrix T of each sub-window, wherein n is a natural number;
and step 3: calculating the total eigenvalue intensity T of the eigenvalue matrix T sum And the dispersion σ of the matrix T; calculating the total intensity T of the characteristic value in the step 3 sum The eigenvalue matrix T of each sub-window is summed;
and 4, step 4: respectively judging the total intensity T of the characteristic values sum With a set threshold value theta of the characteristic value th Dispersion sigma of matrix T and set dispersion threshold sigma th If the dispersion σ of the matrix T is smaller than the dispersion threshold σ th And the total intensity of the characteristic value T sum Less than a set threshold value theta th The water mist is condensed on the front glass of the lens of the image monitoring device, otherwise, the water mist does not exist;
and 5: when the water mist is condensed on the lens front glass of the image monitoring device, judging whether the lens front glass is heated or not, and electrifying and heating the lens front glass if the lens front glass is not heated;
and 6: continuous statistical analysis of total intensity T of characteristic value in heating process of front glass of lens sum If the total intensity T of the characteristic value is sum And when no obvious change exists in the set time period, the front glass of the lens is powered off and heating is stopped.
2. The method for automatically defogging an image surveillance device according to claim 1, wherein said filter matrix M is an enhanced filter matrix obtained by sequentially cascading a plurality of k x k dimensional filter matrices, k being a natural number.
3. The method for automatically defogging an image surveillance device according to claim 2, wherein the filter matrix M is obtained by sequentially cascading 3 filter matrices of 5 × 5 dimensions, and the calculation formula of the filter matrix M is as follows:
wherein:
4. The method of claim 3, wherein the sub-window is an 8 x 8 dimensional sub-window.
5. A method for automatically defogging an image capture device according to claim 4 wherein said total intensity T is based on said characteristic value sum Calculating the mean value T avg According to the mean value T avg And calculating the dispersion sigma of the eigenvalue matrix T of each sub-window.
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| CN108259708A (en) * | 2018-01-17 | 2018-07-06 | 国家安全生产监督管理总局通信信息中心 | There are mist method for processing video frequency and computer readable storage medium |
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Patent Citations (4)
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| CN104143185A (en) * | 2014-06-25 | 2014-11-12 | 东软集团股份有限公司 | Blemish zone detecting method |
| CN105966358A (en) * | 2015-11-06 | 2016-09-28 | 武汉理工大学 | Detection algorithm for raindrops on automobile front windshield |
| CN106657716A (en) * | 2016-12-29 | 2017-05-10 | 惠州华阳通用电子有限公司 | Field of view clearing method and device for electronic in-car rearview mirror |
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