CN109858474A - Detection and identification method for transformer oil surface temperature controller - Google Patents
Detection and identification method for transformer oil surface temperature controller Download PDFInfo
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Abstract
The invention discloses a detection and identification method for a transformer oil level temperature controller, which comprises the following steps that firstly, an inspection robot arrives at a designated position to obtain a picture of the transformer oil level temperature controller to be detected; secondly, screening a final target image of a target transformer oil surface temperature controller area based on the to-be-detected transformer oil surface temperature controller picture; then, reading in a target image, and adopting different image preprocessing operations according to the color of the pointer; then, positioning the instrument by using a target detector, dividing a dial area, and eliminating interference factors outside the dial of the transformer oil surface temperature controller; then, extracting the pointer outline by using a corresponding method according to the color of the pointer; finally, calculating the rotation direction of the pointer and the reading of the pointer by using a cosine law; the method can detect and identify the transformer oil surface temperature controller under various illumination and posture changes.
Description
Technical field
The present invention relates to electric inspection process robot fields, detect and identify more particularly to a kind of transformer oil surface temperature controller
Method.
Background technique
Electric inspection process robot needs to realize autonomous localization and navigation in substation, the identification of field instrument registration, fills automatically
The basic functions such as electricity.Wherein the function of core is exactly the registration of the instrument and meter of detection and the live power equipment of identification.
Currently, electric inspection process robot is during detecting and identifying transformer oil surface temperature controller registration, there are larger
Difficulty.Most of transformer oil surface temperature controller does not have the transporting function of intelligence instrument due to cost and history,
It can only be that crusing robot goes to read instrument registration by the method for computer vision.And accurately identify transformer oil surface temperature controller
The premise of registration is accurately to detect the position of transformer oil surface temperature controller in visual pattern, and most of transformer oil surface temperature controls
Device is in outdoor, and most methods, are detected and identified using traditional image processing means at present, in illumination condition variation
In the case of, detection effect is bad, and generally a kind of illumination condition just needs one group of parameter, and in identification process, there is also outdoors not
Situations such as same illumination, crusing robot different shooting angles.This just needs to propose a kind of more general detection and recognition methods,
Cope with the detection of transformer oil surface temperature controller and identification mission under the conditions of different illumination, posture.
Summary of the invention
The purpose of the present invention is to provide a kind of detection of transformer oil surface temperature controller and recognition methods, become for scene instruction
Registration identification problem of the depressor oil surface thermostat under the working conditions such as different illumination, posture optimizes.
The technical solution of the object of the invention are as follows: a kind of detection of transformer oil surface temperature controller and recognition methods, the side
Method includes,
Step 1: crusing robot reaches designated position and obtains transformer oil surface temperature controller picture to be detected；
Step 2: being based on the transformer oil surface temperature controller picture to be detected, screen target transformer oil surface temperature controller region
Final goal image；
Step 3: reading in the final goal image, pretreatment operation is carried out to the final goal image；
Step 4: positioning instrument using object detector, be partitioned into transformer oil surface temperature controller in the final goal image
Dial plate region；
Step 5: according to color of pointer, extracting pointer profile；
Step 6: calculate the reading of pointer direction of rotation and direction:
The profile point set of the pointer profile is extracted,
It finds in the profile meeting point at a distance of maximum two points, straight line is done with this two o'clock；
Continue to find the point pair on pointer profile, form it into the line segment vertical with the straight line, and from described centering
Find the point pair of lie farthest away；
The point of the lie farthest away is the transformer oil surface temperature controller to the line segment of formation and the point of the straight line intersection
The center of circle, establish coordinate system by origin of the center of circle；
Based on any two in vertical axis in the vector and coordinate system that apart maximum two points are formed in profile meeting point
Point vector, the reading of pointer direction of rotation and direction is calculated using the cosine law.
Further, the reading that pointer direction of rotation and direction are calculated using the cosine law are as follows:
AOBC=| | AO | | | | BC | | cos θ
Θ=antcos (AOBC/ | | AO | | * | | BC | |)
Wherein, A is pointer vertex, and O is the center of circle, and AO is pointer vector, BC is any vector on vertical axis, and AOBC is
The inner product of vector, θ are the angle of two vectors.
Further, further include in the step 1,
Using transformer oil surface temperature controller image data set training classifier, and one is chosen for each inspection point and is patrolled at this
The transformer oil surface temperature controller of cautious shooting image placed in the middle is as template image.
Further, the step 2 further includes,
The target transformer oil surface temperature controller region is carried out using plum forests Fourier transformation and phase coherent techniques thick
Positioning obtains coarse positioning target transformer oil surface temperature controller region；
The target transformer oil surface temperature controller region is accurately positioned using the method for machine learning, will it is described to
It detects transformer oil surface temperature controller picture and is sent into classifier, obtain multiple object candidate areas；
It calculates perceptual hash index, mutual information index and the friendship of the multiple object candidate area and compares, and to described more
A object candidate area is screened, and final goal image is obtained；
Further, the friendship for calculating the multiple object candidate area and ratio, mutual information index and perceptual hash index,
And the multiple object candidate area is screened, obtaining final goal image includes:
The friendship of each object candidate area Yu coarse positioning target transformer oil surface temperature controller region is sought respectively
And compare；
Calculate separately the mutual information index of each object candidate area image and template image；
The transformer oil surface temperature controller area image in each object candidate area image and template image is sought respectively
Perceptual hash index；
By the friendship of each object candidate area and three kinds of ratio, mutual information index and perceptual hash index indexs are done
Ranking operation finds out the confidence level of each object candidate area, using the maximum object candidate area of confidence level as standby
Select testing result；
If the friendship of alternative testing result is simultaneously less than given threshold thresholdIOU than meeting, and (pHash+1/I (G(X),
H(y))) be greater than threshold value thresholdA when, using the coarse positioning target transformer oil surface temperature controller region as final goal figure
Picture；Otherwise, using the alternative testing result as final goal image；Wherein, pHash is that alternative testing result perceptual hash refers to
Mark, I (G(X),H(Y)) it is mutual information index.
Further, the calculation formula of the confidence level are as follows:
Confidence=1- (pHash+1/I (G(X),H(y)))/(IOU+D)
In formula, I (G(X),H(Y)) be the target candidate area mutual information index, pHash be the target candidate area sense
Know Hash index, IOU is the friendship of object candidate area and coarse positioning target transformer temperature table section and than index, and D is setting
Constant.
Further, the threshold value thresholdIOU value range 0.1~0.4, the threshold value thresholdA value model
Enclose 10~50.
Further, include: to the final goal image pretreatment operation in the step 3
If pointer is red, HSV format is converted by the final goal image；
If pointer is non-red, by final goal image gray processing processing.
Further, pointer profile is extracted in the step 5 includes,
If pointer is red, specifically includes the following steps:
Red area is extracted using the color continuity of HSV, H value range is (0,10) (156,180), channel S value
Range is (43,255), and the channel V value range is (46,255)；
Carry out closed operation；
Extract pointer profile；
If the non-red of pointer, specifically includes the following steps:
Histogram equalization, gaussian filtering are carried out to image；
Using Otsu algorithm to dial plate region binaryzation, black pointer is become into white area, other backgrounds become black；
Opening operation is carried out, pointer profile is extracted.
Compared with prior art, the present invention its remarkable advantage are as follows: (1) present invention has merged the existing positioning letter of robot
Breath.Due to the accuracy of robot navigation's location technology, position error is less than 5cm, solves robot location's not timing, target
The big problem of scale, angle change；(2) variations such as graphical rule, the rotation that camera is taken are smaller, utilize phase on this basis
Position is related to carry out rough detection, for image illumination variation, detect the attitudes vibration of target, carried out using the method for machine learning
The problems such as accurate detection solves target and is illuminated by the light influence greatly, such as excessively bright, excessively dark；(3) after detecting target image, will refer to
Needle respectively pre-processes image with non-red by red, and by closed operation, the operations such as histogram equalization improve transformation
Device oil surface thermostat registration recognition accuracy, preferably solves the transformer oil surface temperature controller under the conditions of different illumination, posture
Registration identifies problem, improves robot routing inspection efficiency.(4) hsv color space is utilized, the shadow that illumination identifies registration is reduced
It rings.
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention can be by specification, right
Pointed structure is achieved and obtained in claim and attached drawing.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is the present invention
Some embodiments for those of ordinary skill in the art without creative efforts, can also basis
These attached drawings obtain other attached drawings.
Fig. 1 is transformer oil surface temperature controller detection a kind of in the embodiment of the present invention and recognition methods flow diagram；
Fig. 2 is perceptual hash calculating process schematic diagram in the embodiment of the present invention；
Fig. 3 is that pointer direction of rotation and the direction reading cosine law calculate schematic diagram in the embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical solution in the embodiment of the present invention clearly and completely illustrated, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
As shown in Figure 1, a kind of detection of transformer oil surface temperature controller and recognition methods are described in the embodiment of the present invention, including
Following steps:
Step 1: crusing robot reaches designated position and obtains transformer oil surface temperature controller picture to be detected；
Step 2: the transformer oil surface temperature controller picture to be detected is based on, to transformer oil surface temperature controller to be detected area
Domain carries out coarse positioning and accurate positioning, and screening object candidate area obtains final goal image；
Step 3: reading in the final goal image, different image pretreatment operations is used according to color of pointer；
Step 4: the target transformer oil surface temperature controller region is positioned with object detector, so that meter location is in picture
Then center is partitioned into dial plate region；To exclude the disturbing factor outside transformer oil surface temperature controller dial plate using this method；Then
The middle section of picture after pretreatment is directly partitioned into according to coordinate, this region includes dial plate, consequently facilitating pointer extracting.
Step 5: pointer profile is extracted according to presetting method according to color of pointer；
Step 6: the reading of pointer direction of rotation and direction is calculated using the cosine law；
Further, step 1 concrete operations are as follows: utilize the Instrument image data set training of transformer oil surface temperature controller
Classifier, and a transformer oil surface temperature controller in inspection point shooting image placed in the middle is chosen as mould for each inspection point
Plate image, crusing robot reach specified inspection point and obtain instrument picture to be detected.Preferably, the classifier is Adaboost
Classifier.
Further, the concrete operations of the step 2 are as follows:
Step 2.1: using plum forests Fourier transformation and phase coherent techniques to transformer oil surface temperature controller picture to be detected
In target transformer oil surface temperature controller region carry out coarse positioning；
Step 2.2: target transformer oil surface temperature controller region is accurately positioned using the method for machine learning, it will be to
It detects transformer oil surface temperature controller picture and is sent into trained listening group, obtain several object candidate areas；
Step 2.3: calculating perceptual hash, mutual information and the friendship of multiple candidate regions and three kinds of parameter indexes of ratio, screen mesh
Mark candidate region obtains final goal.
Further, step 2.3 the following steps are included:
Step 2.3.1: respectively by each object candidate area and the coarse positioning target transformer oil surface temperature controller
It asks friendship and than parameter IOU in region；
Step 2.3.2: respectively by the transformer oil surface temperature controller area in each object candidate area image and template image
Area image does perceptual hash calculating, obtains perceptual hash index；
Step 2.3.3: the mutual information index of each the object candidate area image and template image is calculated separately；
Step 2.3.4: refer to by the friendship of each object candidate area and than IOU, mutual information index and perceptual hash
It marks three kinds of indexs and does the confidence level that weighting finds out each object candidate area, the maximum object candidate area of confidence level is made
For alternative testing result；
Step 2.3.5: if the IOU of alternative testing result meets while being less than given threshold thresholdIOU, and
(pHash+1/I(G(X),H(y))) be greater than threshold value thresholdA when, will in step 2 determine the coarse positioning target transformer
Oil surface thermostat region is as final goal image, and otherwise in case selecting testing result as final goal image, pHash is alternative
Testing result perceptual hash index, I (G(X),H(Y)) it is mutual information index.
Preferably, each object candidate area and the coarse positioning target instrument region are asked into friendship in step 2.3.1
And the formula than parameter IOU are as follows:
In formula, C is coarse positioning target instrument region, niFor object candidate area.
Preferably, respectively by the gauge field in each object candidate area image and template image in step 2.3.2
Area image does perceptual hash calculating method particularly includes:
A1, pretreatment: downscaled images size, and by image gray processing；
A2, dct transform: dct transform is carried out to pretreated image, obtains matrix F (u, v)；
A3, DCT matrix is reduced, as shown in Fig. 2, the feature of entire image concentrates on upper left corner low frequency region, we are extracted
Matrix top left corner pixel 8*8 matrix, the eigenmatrix as the image；
A4, matrix binaryzation, as shown in Fig. 2, average to matrix, and will be greater than mean value sets 1, sets 0 less than mean value；
A5, generate cryptographic Hash: the sequence for being 64 by the matrix arrangement after binaryzation, which is the Kazakhstan of input picture
Uncommon sequence；
The Hamming distance of the feature vector of A6, calculating object candidate area image and template image, perceptually Hash refers to
Mark.
Preferably, in step 2.3.3 each the object candidate area image and the template image mutual information index
Calculation formula are as follows:
G(X)、H(Y)The respectively number of template image and candidate image gray-scale pixels, W, H are respectively candidate region image
It is wide, high.
Preferably, in step 2.3.4 confidence level calculation formula are as follows:
Confidence=1- (pHash+1/I (G(X),H(y)))/(IOU+D)
In formula, I (G(X),H(Y)) it is mutual information index, pHash is perceptual hash index, and for IOU to hand over and than index, D is to set
Fixed constant.
Preferably, threshold value thresholdIOU value range 0.1~0.4, threshold value thresholdA value range 10~50.
Further, as shown in Figure 1, wherein robot obtains transformer to specified inspection point, shooting in the step 3
Pointer is divided into red and non-red two types, corresponding different image processing method by oil surface thermostat picture.Because will
Picture is placed under hsv color space, and black is easy to obscure under HSV space vulnerable to illumination effect, and red is in HSV space
Under be not easily susceptible to illumination effect, therefore for red pointer, picture is gone into HSV format, for non-red pointer, by picture ash
Degreeization processing.
Further, in Fig. 1, pointer is extracted using correlation method according to color of pointer.Under hsv color space, such as table 1
Shown, distribution of color is continuous, therefore according to following table, is red feature using color of pointer, extracts pointer part.For
Non- red pointer, picture gray processing is handled.
Table 1, the color value range under hsv color space
Further, step 5 concrete operations are as follows:
For red pointer:
Step 5.1.1: pointer is red, since transformer oil surface temperature controller other parts do not have red area, utilizes HSV
Color continuity extract red area, H value in (0,10) (156,180), channel S value in (43,255), the channel V (46,
255)；
Step 5.1.2: carrying out closed operation, reduces noise point to the interference for extracting pointer and makes profile round and smooth.
Step 5.1.3: area maximum red area, as pointer profile are extracted.
For non-red pointer:
Step 5.2.1: carrying out histogram equalization, gaussian filtering to image, reduces illumination interference.
Step 5.2.2: since transformer oil surface temperature controller pointer and dial plate color difference are obvious, therefore Otsu algorithm pair is utilized
Black pointer is become white area by dial plate region binaryzation, other backgrounds become black.
Step 5.2.3: carrying out opening operation, extracts pointer profile.
Further, as shown in figure 3, step 6 concrete operations are as follows:
Step 6.1: finding pointer area obtained in step 5 and extract the profile point set in the region, find out the set
In at a distance of remote two points most, straight line a is done with this two o'clock, looks for the point pair on profile, the straight line b formed it into perpendicular to straight line a,
The point pair of lie farthest away is found in from these.
Step 6.2: the point of straight line a, b intersection, the as center of circle O of transformer oil surface temperature controller establish coordinate by origin of O
System.
Step 6.3: the shape due to pointer integral into triangle, the vertex A and center of circle O lie farthest away of pointer, according to this
It is vertex A that feature, which finds point farthest in profile point set, is taken up an official post intention amount BC using vector AO and vertical axis, passes through cosine
Theorem calculates the reading of pointer direction of rotation and direction:
AOBC=| | AO | | | | BC | | cos θ
Θ=antcos (AOBC/ | | AO | | * | | BC | |)
Wherein AO pointer vector, BC are any vector on the vertical axis of reference axis, and AOBC is the inner product of vector, and θ is
The angle of two vectors.
Compared with prior art, the present invention its remarkable advantage are as follows: (1) present invention has merged the existing positioning letter of robot
Breath.Due to the accuracy of robot navigation's location technology, position error is less than 5cm, solves robot location's not timing, target
The big problem of scale, angle change；(2) variations such as graphical rule, the rotation that camera is taken are smaller, utilize phase on this basis
Position is related to carry out rough detection, for image illumination variation, detect the attitudes vibration of target, carried out using the method for machine learning
The problems such as accurate detection solves target and is illuminated by the light influence greatly, such as excessively bright, excessively dark；(3) after detecting target image, will refer to
Needle respectively pre-processes image with non-red by red, and by closed operation, the operations such as histogram equalization improve transformation
Device oil surface thermostat registration recognition accuracy, preferably solves the transformer oil surface temperature controller under the conditions of different illumination, posture
Registration identifies problem, improves robot routing inspection efficiency.(4) hsv color space is utilized, the shadow that illumination identifies registration is reduced
It rings.
Although the present invention is described in detail referring to the foregoing embodiments, those skilled in the art should manage
Solution: it is still possible to modify the technical solutions described in the foregoing embodiments, or to part of technical characteristic into
Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The spirit and scope of scheme.
Claims (9)
1. a kind of transformer oil surface temperature controller detection and recognition methods, which is characterized in that the method includes,
Step 1: crusing robot reaches designated position and obtains transformer oil surface temperature controller picture to be detected；
Step 2: being based on the transformer oil surface temperature controller picture to be detected, screening target transformer oil surface temperature controller region is most
Whole target image；
Step 3: reading in the final goal image, pretreatment operation is carried out to the final goal image；
Step 4: positioning instrument using object detector, be partitioned into the table of transformer oil surface temperature controller in the final goal image
Disk area；
Step 5: according to color of pointer, extracting pointer profile；
Step 6: calculate the reading of pointer direction of rotation and direction:
Extract the profile point set of the pointer profile；
It finds in the profile meeting point at a distance of maximum two points, straight line is done with this two o'clock；
Continue to find the point pair on pointer profile, forms it into the line segment vertical with the straight line, and find in from the point
The point pair of lie farthest away；
The point of the lie farthest away is the circle of the transformer oil surface temperature controller to the line segment of formation and the point of the straight line intersection
The heart establishes coordinate system by origin of the center of circle；
Based on any two points in profile meeting point in the vector and coordinate system that maximum two points are formed in vertical axis to
Amount calculates the reading of pointer direction of rotation and direction using the cosine law.
2. transformer oil surface temperature controller detection according to claim 1 and recognition methods, which is characterized in that more than the utilization
String theorem calculates the reading of pointer direction of rotation and direction are as follows:
AOBC=| | AO | | | | BC | | cos θ
Θ=antcos (AOBC/ | | AO | | * | | BC | |)
Wherein, A is pointer vertex, and O is the center of circle, and AO is pointer vector, BC is any vector on vertical axis, and AOBC is vector
Inner product, θ be two vectors angle.
3. transformer oil surface temperature controller detection according to claim 1 and recognition methods, which is characterized in that the step 1
In further include,
One is chosen in the inspection point using transformer oil surface temperature controller image data set training classifier, and for each inspection point
The transformer oil surface temperature controller of shooting image placed in the middle is as template image.
4. transformer oil surface temperature controller detection according to claim 1 and recognition methods, which is characterized in that the step 2
Further include,
Coarse positioning is carried out to the target transformer oil surface temperature controller region using plum forests Fourier transformation and phase coherent techniques,
Obtain coarse positioning target transformer oil surface temperature controller region；
The target transformer oil surface temperature controller region is accurately positioned using the method for machine learning, it will be described to be detected
Transformer oil surface temperature controller picture is sent into classifier, obtains multiple object candidate areas；
It calculates perceptual hash index, mutual information index and the friendship of the multiple object candidate area and compares, and to the multiple mesh
Mark candidate region is screened, and final goal image is obtained.
5. transformer oil surface temperature controller detection according to claim 4 and recognition methods, which is characterized in that the calculating institute
The friendship of multiple object candidate areas and ratio, mutual information index and perceptual hash index are stated, and to the multiple object candidate area
It is screened, obtaining final goal image includes:
The friendship of each object candidate area and coarse positioning target transformer oil surface temperature controller region and ratio are sought respectively；
Calculate separately the mutual information index of each object candidate area image and template image；
The perception of the transformer oil surface temperature controller area image in each object candidate area image and template image is sought respectively
Hash index；
By the friendship of each object candidate area and three kinds of ratio, mutual information index and perceptual hash index indexs weight
Operation finds out the confidence level of each object candidate area, and the maximum object candidate area of confidence level is alternately examined
Survey result；
If the satisfaction of alternative testing result is less than given threshold thresholdIOU, and (pHash+1/I (G(X),H(y))) it is greater than threshold
When value thresholdA, using the coarse positioning target transformer oil surface temperature controller region as final goal image；Otherwise, with institute
Alternative testing result is stated as final goal image；Wherein, pHash is alternative testing result perceptual hash index, I (G(X),
H(Y)) it is mutual information index.
6. transformer oil surface temperature controller detection according to claim 5 and recognition methods, which is characterized in that the confidence level
Calculation formula are as follows:
Confidence=1- (pHash+1/I (G(X),H(y)))/(IOU+D)
In formula, I (G(X),H(Y)) be the target candidate area mutual information index, pHash be the target candidate area perception breathe out
Uncommon index, IOU are the friendship of object candidate area and coarse positioning target transformer temperature table section and than index, D be set it is normal
Number.
7. transformer oil surface temperature controller detection according to claim 5 and recognition methods, which is characterized in that the threshold value
ThresholdIOU value range 0.1~0.4, the threshold value thresholdA value range 10~50.
8. transformer oil surface temperature controller detection according to claim 1 and recognition methods, which is characterized in that the step 3
In include: to the final goal image pretreatment operation
If pointer is red, HSV format is converted by the final goal image；
If pointer is non-red, by final goal image gray processing processing.
9. transformer oil surface temperature controller detection according to claim 1 and recognition methods, which is characterized in that the step 5
Middle extraction pointer profile includes,
If pointer is red, specifically includes the following steps:
Red area is extracted using the color continuity of HSV, H value range is (0,10) (156,180), channel S value range
For (43,255), the channel V value range is (46,255)；
Carry out closed operation；
Extract pointer profile；
If the non-red of pointer, specifically includes the following steps:
Histogram equalization, gaussian filtering are carried out to image；
Using Otsu algorithm to dial plate region binaryzation, black pointer is become into white area, other backgrounds become black；
Opening operation is carried out, pointer profile is extracted.
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| CN115661760A (en) * | 2022-12-14 | 2023-01-31 | 南京迈能能源科技有限公司 | Transformer oil temperature meter reading identification method, system, storage medium and equipment |
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