CN109117855A - Abnormal power equipment image identification system - Google Patents
Abnormal power equipment image identification system Download PDFInfo
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- CN109117855A CN109117855A CN201810883766.7A CN201810883766A CN109117855A CN 109117855 A CN109117855 A CN 109117855A CN 201810883766 A CN201810883766 A CN 201810883766A CN 109117855 A CN109117855 A CN 109117855A
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- 238000003708 edge detection Methods 0.000 claims description 26
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- 235000013399 edible fruits Nutrition 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 abstract description 10
- 238000001514 detection method Methods 0.000 abstract description 9
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- 230000005856 abnormality Effects 0.000 abstract description 3
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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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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Abstract
The invention discloses a kind of abnormal power equipment image identification system, system includes: Image Acquisition terminal, processor and mobile terminal.Described image acquisition terminal is set near corresponding power equipment, for obtaining corresponding power equipment image；The processor, for handling corresponding power equipment image, and the judgement result for generating corresponding power equipment safety state is sent to mobile terminal；The mobile terminal, for correspondingly being shown according to the judgement result.The invention has the benefit that the present invention overcomes artificial detection power equipment whether Yi Chang drawback, image recognition processing is carried out by the corresponding power equipment image acquired to Image Acquisition terminal, it is whether abnormal that power equipment in power equipment image can be automatically identified, and staff is reminded by mobile terminal, the precision and accuracy to the identification of power equipment abnormality detection are improved, the objectivity, real-time and accuracy of monitoring are also improved.
Description
Technical field
The present invention relates to abnormal images to identify field, and in particular to a kind of abnormal power equipment image identification system.
Background technique
With the continuous expansion of China's electric power networks scale, the safe and reliable operation of the power equipments such as substation is heavy to closing
It wants, and the operating status of power equipment is to determine one of the key factor of its safe and stable operation.
A kind of monitoring side that picture control is power equipment operating status is carried out to power equipment by video monitoring system
Formula.Existing video monitoring system only has video monitoring function and recording function, cannot carry out intelligentized master to monitoring objective
Dynamic discriminance analysis needs operator's moment observation analysis image, virtually increases only by a large amount of image transmitting to dispatching terminal
The work load of operator is added；Meanwhile the subjectivity of human eye fatigable weakness and artificial judgment, it has seriously affected electric power and has set
Standby monitoring running state the degree of automation further increases；In addition, the operating status of many high-tension apparatuses is difficult to be converted into electricity
Signal, the influence in signal conversion and transmission process vulnerable to strong-electromagnetic field；The operating parameter of important equipment needs real-time monitoring,
It is difficult to meet requirement of real-time using manual patrol, and the sense of responsibility of floor manager, working attitude and mental status seriously affect
The result of detection；Moreover, human eye is difficult to differentiate the grey scale change of fine image, it is difficult to which objective judgement power equipment whether there is
It is abnormal.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of normal power equipment image identification system.
The purpose of the present invention is realized using following technical scheme:
A kind of abnormal power equipment image identification system, the system include: that Image Acquisition terminal, processor and movement are whole
End.Described image acquisition terminal is set near corresponding power equipment, for obtaining corresponding power equipment image；The place
Device is managed, for handling corresponding power equipment image, and generates the judgement of corresponding power equipment safety state
As a result it is sent to mobile terminal；The mobile terminal, for correspondingly being shown according to the judgement result.
The invention has the benefit that the present invention overcomes artificial detection power equipment whether Yi Chang drawback, by right
The corresponding power equipment image of Image Acquisition terminal acquisition carries out image recognition processing, can automatically identify power equipment figure
Whether power equipment is abnormal as in, and reminds staff by mobile terminal, improves and identifies to power equipment abnormality detection
Precision and accuracy, also improve the objectivity, real-time and accuracy of monitoring.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is structure chart of the invention；
Fig. 2 is the frame construction drawing of processor 2 of the present invention.
Appended drawing reference: Image Acquisition terminal 1；Processor 2；Mobile terminal 3；Image denoising module 4；Image Edge-Detection mould
Block 5；Image enhancement module 6；Image characteristics extraction module 7；Characteristics of image identification module 8；Security feature database 9；Store mould
Block 10.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of abnormal power equipment image identification system, the system includes: Image Acquisition terminal 1, processor 2
With mobile terminal 3.Image Acquisition terminal 1 is set near corresponding power equipment, for obtaining corresponding power equipment image；
Processor 2 for handling corresponding power equipment image, and generates the judgement knot of corresponding power equipment safety state
Fruit is sent to mobile terminal 3；Mobile terminal 3, for correspondingly being shown according to judgement result.
The utility model has the advantages that the present invention overcomes artificial detection power equipment whether Yi Chang drawback, by whole to Image Acquisition
The corresponding power equipment image of end acquisition carries out image recognition processing, can automatically identify electric power in power equipment image and set
It is standby whether abnormal, and staff is reminded by mobile terminal, improve precision and standard to the identification of power equipment abnormality detection
True property also improves the objectivity, real-time and accuracy of monitoring.
Preferably, Image Acquisition terminal 1 is CCD camera.
Preferably, referring to fig. 2, processor 2 includes image denoising module 4, Image Edge-Detection module 5, Image Enhancement Based
Block 6, image characteristics extraction module 7, characteristics of image identification module 8, security feature database 9 and memory module 10.Image denoising
Module 4 is used to remove the random noise in corresponding power equipment image；Image Edge-Detection module 5 is used for the electric power to denoising
Equipment image carries out edge detection；Image enhancement module 6 is for carrying out at enhancing the power equipment image after edge detection
Reason；Image characteristics extraction module 7 is used to extract the feature of corresponding power equipment image from enhanced power equipment image
Vector；Security feature database 9 is used to store the security feature vector of trained corresponding power equipment image；Image is special
Sign identification module 8 be used for the security feature in the feature vector that obtains image characteristics extraction module and security feature database to
Amount is matched, and whether abnormal judges corresponding power equipment, and is generated corresponding judgement result and be sent to mobile terminal；Storage
Module 10 is used to store the power equipment image of Image Acquisition terminal acquisition.
Preferably, the random noise in the corresponding power equipment image of removal, specifically: to corresponding
Power equipment image carries out gray processing processing, and is successively denoised point by point to the corresponding power equipment image after gray processing,
The denoising estimated value of each pixel in corresponding power equipment image is obtained, and using denoising estimated value as new gray scale
Value, the set that all pixels point is constituted at this time are the corresponding power equipment image after denoising；Wherein, corresponding electric power
The denoising estimated value of pixel p (i, j) is calculated using following formula in equipment image:
In formula,For the denoising estimated value of pixel p (i, j), be pixel p denoising after gray value, i, j points
Not Wei pixel p abscissa and ordinate, D (p) is regularization parameter about pixel p, and Ω, which is with pixel p, is
The heart, size are the search window of A × A, and q is any pixel point in search window,For pixel p's and pixel q
Gauss weighted euclidean distance, α are the standard deviation of Gaussian function, and h is smoothing parameter, and G (p) is the gray value of pixel p, and G (q) is
The gray value of pixel q,For the gray variance for the image block that all pixels point in search window is constituted, σ is corresponding electricity
The gray variance of power equipment image, η are a positive number factor of setting, are zero its purpose is to prevent denominator.
The utility model has the advantages that carrying out denoising using power equipment image of the above method to acquisition, this method is by successively
The denoising estimated value of all pixels point in the image is calculated, and then completes denoising operation, the denoising method is simple, denoises speed
Fastly, the Gauss weighted euclidean distance information between pixel is not only allowed for, it is also contemplated that residual pixel point and mesh in search window
Mark the shadow of the gray variance of the gray variance and power equipment image of image block in the relationship and search window of pixel gray value
It rings, to farthest remain the edge and minutia in the power equipment image, improves denoising effect, while
Be conducive to the subsequent accuracy extracted to power equipment image feature vector, improve detection accuracy.
Preferably, the power equipment image of described pair of denoising carries out edge detection, specifically:
(1) taking the central pixel point in the sliding window that size is 3 × 3 is edge measuring point to be checked, according to laterally detection side
Three regions: L, M, R are divided into the sliding window 3 × 3, wherein L is located on the left of sliding window, M is located in sliding window
Between, R be located on the right side of sliding window, judge whether edge measuring point to be checked is marginal point using edge detection formula, wherein the side
Edge detection formula are as follows:
In formula, H (k) is the characteristic value of edge measuring point k to be checked, GLIt (a) is the gray value of a-th of pixel in the L of region, GM
It (a) is the gray value of a-th of pixel in the M of region, GRIt (a) is the gray value of a-th of pixel in the R of region, and a=1,2,3；
G (k) is the gray value of edge measuring point k to be checked；
As H (k) >=T, then edge measuring point k to be checked is marginal point, conversely, edge measuring point k to be checked is not marginal point,
In, T is the threshold value of setting；
(2) all pixels point in the corresponding power equipment image after traversal denoising, and the method inhibited using non-extreme value
Edge positioning is carried out to obtained marginal point, the set of the final marginal point of corresponding power equipment image can be obtained；
(3) the corresponding electric power after denoising is set according to the set for the final marginal point for obtaining corresponding power equipment image
Standby image is split, the power equipment image after edge detection can be obtained.
The utility model has the advantages that judging whether the central pixel point in sliding window is corresponding by one sliding window of setting
The marginal point of power equipment image, and all pictures in the corresponding power equipment image after denoising are successively traversed using sliding window
Whether vegetarian refreshments, the way can be adaptively that marginal point differentiates to each pixel, not only retain corresponding electric power
In the image border point of equipment image while minutia, it is also able to detect that clearly marginal information, while in order into one
Step improves edge detection precision, is repositioned using the method that non-extreme value inhibits to the marginal point detected, can be into one
Step ground removal non-edge point, so that it is the edge clear extracted, complete, accurate, it is more advantageous to corresponding power equipment image
The accurate segmentation of region, the power equipment image after obtaining edge detection set convenient for the subsequent electric power to after edge detection
The extraction and identification of the feature vector of standby image.
Preferably, the described pair of power equipment image after edge detection carries out enhancing processing, specifically public using enhancing
Formula calculates all pixels point enhancing treated gray value in the power equipment image after edge detection, and treated for the enhancing
The set that pixel is constituted is enhanced power equipment image, wherein the enhancing formula are as follows:
In formula, Ge(x, y) is the gray value of enhanced pixel r (x, y), and G (x, y) is that the electric power after edge detection is set
The gray value of pixel r (x, y) in standby image, μ (x, y) be about edge detection after power equipment image in pixel r (x,
Y) along the control coefrficient of gradient direction,For the second-order partial differential coefficient at pixel r (x, y) along gradient direction n,For the second-order partial differential coefficient at pixel r (x, y) along the tangential direction s orthogonal with gradient direction；
Wherein, about pixel r (x, y) in the power equipment image after the edge detection along the control system of gradient direction
Number μ (x, y) is obtained by following mode:
(1) in the power equipment image after calculating the edge detection using following formula at each pixel in 3 × 3 neighborhoods
Local variance, wherein the formula of the local variance about pixel r (x, y) are as follows:
In formula, χ2(x, y) is the local variance of pixel r (x, y), and G (x+s, y+t) is the picture that coordinate is (x+s, y+t)
The gray value of vegetarian refreshments,For the gray value mean value of all pixels point in neighborhood；
(2) using normalization formula to obtained local variance χ2(x, y) is normalized, its local variance is returned
One changes into the region of 0-255, wherein normalizing formula are as follows:
In formula,For the local variance after the normalization of pixel r (x, y), Max χ2With Min χ2It is respectively described
The maximum value and minimum value of power equipment image local variance after edge detection；
(3) according to obtained normalized value, pixel r (x, y) is calculated along the control coefrficient of gradient direction using following formula；
In formula, μ (x, y) is control coefrficient of the pixel r (x, y) along gradient direction, and ζ is the variance threshold values of setting.
The utility model has the advantages that enhancing processing is carried out to the power equipment image after the edge detection using above-mentioned algorithm, the calculation
Method avoids edge and overshoot phenomenon occurs, simultaneously while enhancing the power equipment image detail feature after edge detection
Also the residual noise in power equipment image after restrained effectively the edge detection, so that enhanced power equipment figure
As the minutia of corresponding power equipment can be highlighted, convenient for the feature vector of the corresponding power equipment of subsequent extracted, in favor of
Whether accurate judgement is carried out extremely to corresponding power equipment image.
Preferably, the safety in the feature vector that image characteristics extraction module 7 is obtained and security feature database 9
Feature vector is matched, and whether abnormal judges corresponding power equipment, and is generated corresponding judgement result and be sent to movement eventually
End, specifically: the feature vector for obtaining image characteristics extraction module 7In trained security feature database 9
The corresponding security feature vector of corresponding power equipmentIt is matched, if described eigenvectorWith the security feature vectorMeetThen judge that collected power equipment is without exception, otherwise determines that collected power equipment is abnormal, it is defeated
Out determine as a result, and by determine result be sent to the mobile terminal, whereinIt is set for the electric power in security feature database 9
Standby corresponding security feature vector, λ are the customized similarity factor.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered
Work as understanding, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (5)
1. a kind of abnormal power equipment image identification system, characterized in that include: that Image Acquisition terminal, processor and movement are whole
End；
Described image acquisition terminal is set near corresponding power equipment, for obtaining corresponding power equipment image；
The processor for handling corresponding power equipment image, and generates corresponding power equipment safety
The judgement result of state is sent to mobile terminal；
The mobile terminal, for correspondingly being shown according to the judgement result.
2. abnormal power equipment image identification system according to claim 1, characterized in that described image acquisition terminal is
CCD camera.
3. abnormal power equipment image identification system according to claim 1, characterized in that the processor includes image
Denoise module, Image Edge-Detection module, image enhancement module, image characteristics extraction module, characteristics of image identification module, safety
Property data base and memory module；
Described image denoises module, for removing the random noise in corresponding power equipment image；
Described image edge detection module, for carrying out edge detection to the power equipment image of denoising；
Described image enhances module, for carrying out enhancing processing to the power equipment image after edge detection；
Described image characteristic extracting module, for extracting corresponding power equipment image from enhanced power equipment image
Feature vector；
The security feature database, for storing the security feature vector of trained corresponding power equipment image；
Described image feature recognition module, feature vector and the Special safety for obtaining described image characteristic extracting module
Security feature vector in sign database is matched, and whether abnormal judges corresponding power equipment, and generates corresponding judgement
As a result it is sent to mobile terminal；
The memory module, for storing the power equipment image of described image acquisition terminal acquisition.
4. abnormal power equipment image identification system according to claim 3, characterized in that the removal is corresponding
Random noise in power equipment image, specifically: gray processing processing is carried out to corresponding power equipment image, and successively
Corresponding power equipment image after gray processing is denoised point by point, obtains each picture in corresponding power equipment image
The denoising estimated value of vegetarian refreshments, and using denoising estimated value as new gray value, the set that all pixels point is constituted at this time is to go
Corresponding power equipment image after making an uproar；Wherein, the denoising of pixel p (i, j) is estimated in corresponding power equipment image
Value is calculated using following formula:
In formula,For the denoising estimated value of pixel p (i, j), i, j are respectively the abscissa and ordinate of pixel p, D (p)
For the regularization parameter about pixel p, Ω is centered on pixel p, and size is the search window of A × A, and q is in search window
Any pixel point,For the Gauss weighted euclidean distance of pixel p and pixel q, α is the mark of Gaussian function
Quasi- poor, h is smoothing parameter, and G (p) is the gray value of pixel p, and G (q) is the gray value of pixel q,For institute in search window
The gray variance for the image block for having pixel to constitute, σ are the gray variance of corresponding power equipment image, and η is setting
A positive number factor.
5. abnormal power equipment image identification system according to claim 4, which is characterized in that described by described image spy
The feature vector that sign extraction module obtains is matched with the security feature vector in the security feature database, and judgement is corresponding
Power equipment it is whether abnormal, and generate it is corresponding determine that result is sent to mobile terminal, specifically: described image feature is mentioned
The feature vector that modulus block obtainsTo the corresponding peace of corresponding power equipment in the trained security feature database
Full feature vectorIt is matched, if described eigenvectorWith the security feature vectorMeetThen sentence
The collected power equipment that breaks is without exception, otherwise determines that collected power equipment is abnormal, and output determines as a result, and will determine to tie
Fruit is sent to the mobile terminal, whereinFor the corresponding security feature of the power equipment in the security feature database to
Amount, λ are the customized similarity factor.
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| CN201810883766.7A CN109117855A (en) | 2018-08-06 | 2018-08-06 | Abnormal power equipment image identification system |
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| CN201810883766.7A CN109117855A (en) | 2018-08-06 | 2018-08-06 | Abnormal power equipment image identification system |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110493574A (en) * | 2019-08-27 | 2019-11-22 | 深圳供电局有限公司 | Safety supervision visualization system based on Streaming Media and AI technology |
| CN112257610A (en) * | 2020-10-23 | 2021-01-22 | 岭东核电有限公司 | Nuclear power equipment monitoring method and device, computer equipment and storage medium |
| CN112712111A (en) * | 2020-12-23 | 2021-04-27 | 南方电网深圳数字电网研究院有限公司 | Device state detection method, electronic device, and storage medium |
| CN114407759A (en) * | 2022-02-25 | 2022-04-29 | 新泰市日进化工科技有限公司 | Safety monitoring system and monitoring method for liquid ammonia unloading |
| CN114494778A (en) * | 2022-01-25 | 2022-05-13 | 南方电网物资有限公司 | Image acquisition processing system for remote monitoring of power equipment and control method thereof |
-
2018
- 2018-08-06 CN CN201810883766.7A patent/CN109117855A/en not_active Withdrawn
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110493574A (en) * | 2019-08-27 | 2019-11-22 | 深圳供电局有限公司 | Safety supervision visualization system based on Streaming Media and AI technology |
| CN110493574B (en) * | 2019-08-27 | 2021-06-11 | 深圳供电局有限公司 | Security monitoring visualization system based on streaming media and AI technology |
| CN112257610A (en) * | 2020-10-23 | 2021-01-22 | 岭东核电有限公司 | Nuclear power equipment monitoring method and device, computer equipment and storage medium |
| CN112257610B (en) * | 2020-10-23 | 2024-05-07 | 岭东核电有限公司 | Nuclear power equipment monitoring method and device, computer equipment and storage medium |
| CN112712111A (en) * | 2020-12-23 | 2021-04-27 | 南方电网深圳数字电网研究院有限公司 | Device state detection method, electronic device, and storage medium |
| CN114494778A (en) * | 2022-01-25 | 2022-05-13 | 南方电网物资有限公司 | Image acquisition processing system for remote monitoring of power equipment and control method thereof |
| CN114407759A (en) * | 2022-02-25 | 2022-04-29 | 新泰市日进化工科技有限公司 | Safety monitoring system and monitoring method for liquid ammonia unloading |
| CN114407759B (en) * | 2022-02-25 | 2022-11-22 | 新泰市日进化工科技有限公司 | Safety monitoring system and monitoring method for liquid ammonia unloading |
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Application publication date: 20190101 |