CN108256517B - Laser radar-based insulator string identification method - Google Patents

Laser radar-based insulator string identification method Download PDF

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CN108256517B
CN108256517B CN201611234430.5A CN201611234430A CN108256517B CN 108256517 B CN108256517 B CN 108256517B CN 201611234430 A CN201611234430 A CN 201611234430A CN 108256517 B CN108256517 B CN 108256517B
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insulator string
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depth image
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CN108256517A (en
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姜勇
刘国伟
王洪光
朱正国
宋屹峰
陈鹏
何斌斌
刘澈
胡冉
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Shenyang Institute of Automation of CAS
Shenzhen Power Supply Bureau Co Ltd
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Shenyang Institute of Automation of CAS
Shenzhen Power Supply Bureau Co Ltd
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Abstract

The invention relates to an insulator string identification method based on a laser radar, which comprises the following steps: acquiring a depth image: obtaining a depth image through laser radar equipment arranged on the robot; preprocessing of the depth image: preprocessing the depth image with the insulator string to obtain an interested area; feature extraction of the depth image: in the interested region, establishing a depth characteristic curve model, constructing a depth periodic characteristic matrix and a width vector, and extracting insulator string segment number characteristics and disc diameter variation characteristics; identifying the type of an insulator string: and identifying the type of the insulator string according to the extracted features. According to the invention, the insulator string is identified by utilizing the gray scale periodic characteristic and the gray scale threshold characteristic according to the characteristics of the insulator string. The accuracy of the recognition result is high, the working efficiency is improved, and a basis is provided for the inspection robot of the transformer substation to complete the washing task.

Description

Laser radar-based insulator string identification method
Technical Field
The invention relates to an autonomous identification method based on laser radar for a substation insulator string, in particular to an automatic identification method of the insulator string based on machine vision.
Background
Insulators operating on-line, in the natural environment, subject to SO2Nitrogen oxides, particulate dust and the like, and a layer of fouling substances is gradually deposited on the surface of the substrate. In dry weather, the insulators can keep a higher insulation level, and the discharge voltage of the insulators is close to that of the insulators in a clean and dry state; when the insulator is in humid weather such as fog, dew and rain, and when ice and snow are melted, the electrolyte in a filthy layer is dissolved due to the fact that the filthy matter on the surface of the insulator absorbs water, the insulation level of the insulator is reduced, leakage current is increased, and flashover accidents occur in severe cases. Therefore, the insulator string needs to be cleaned periodically.
At the present stage, the insulator string is cleaned by professional personnel of a transformer substation. When cleaning, the problems of high labor intensity, high danger and the like exist.
As an advanced technical means, the transformer substation inspection robot can replace manpower to carry out insulator string washing tasks. In order to complete the task, the robot needs to automatically identify the insulator string. Therefore, an insulator string automatic identification method with high automation degree needs to be provided.
Because the insulator string is in the outdoor environment, the illumination change is complex, and the structure built in the transformer substation is complex. Through laser radar detection, the influence of illumination on the sensor can be avoided, the influence of a complex background can be overcome, and the outdoor environment can be well adapted. And at present, an insulator string automatic identification method based on laser radar is not reported.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a laser radar-based insulator string automatic identification method with high identification precision.
The technical scheme adopted by the invention for solving the technical problems is as follows: the laser radar-based insulator string identification method comprises the following steps of:
acquiring a depth image: obtaining a depth image through laser radar equipment arranged on the robot;
preprocessing of the depth image: preprocessing the depth image with the insulator string to obtain an interested area;
feature extraction of the depth image: in the interested region, establishing a depth characteristic curve model, constructing a depth periodic characteristic matrix and a width vector, and extracting insulator string segment number characteristics and disc diameter variation characteristics;
identifying the type of an insulator string: and identifying the type of the insulator string according to the extracted features.
The depth image preprocessing comprises the following steps:
(2.1) carrying out binarization processing on the depth image, separating a foreground and a background in the depth image, and eliminating background interference;
(2.2) carrying out expansion operation on the processed binary image, and then carrying out corrosion operation to eliminate interference pixel points on the binary image;
and (2.3) selecting the maximum connected region, extracting the region of interest, and finishing pretreatment.
The depth characteristic curve model is established as follows:
extracting the left edge of the target area:
Figure BDA0001195051140000021
lik＝k,
Figure BDA0001195051140000022
extracting the right edge of the target area:
Figure BDA0001195051140000023
rik＝k,
Figure BDA0001195051140000024
where D (x, y) is a binary image, k represents the number of rows in the image, t represents the number of columns in the image, lk，rkPixel point locations on the edge;
determining the position on the line in the target area by the left and right edges:
Figure BDA0001195051140000025
mik＝k,
Figure BDA0001195051140000031
in the formula mkAs coordinates of the depth profile pixel points, mikIs a depth profile pixel point row coordinate, mjkThe pixel point row coordinates of the depth characteristic curve are obtained; n isIThe number of image lines;
establishing a depth characteristic curve model:
f(xk,yk)＝0
wherein:
xk＝mik
Figure BDA0001195051140000032
xkas the abscissa of the characteristic curve, i.e. the number of image lines, ykIs the ordinate of the characteristic curve, namely the depth value; l (m)k) Representing a depth image.
The construction of the depth periodic feature matrix comprises the following steps:
Figure BDA0001195051140000033
firstly, the depth characteristic curve f (x) is obtainedk,yk) Maximum and minimum values on 0; building (2)Vertical matrix
Figure BDA0001195051140000034
In the formula:
Figure BDA0001195051140000035
v2kis a depth characteristic curve f (x)k,yk) 0 on the abscissa of the extreme point, v3kIs a depth characteristic curve f (x)k,yk) The ordinate of the extreme point on 0, namely the depth value; k is 1,2 … nI；
Setting a threshold lambdac1,λc2And lambdac1﹤λc2(ii) a Then to
Figure BDA0001195051140000036
Traversing by column to obtain V3,k+1-V3,k＞λc1And V3,k+1-V3,k＜λc2Time V1,kAnd V2,kRecording; when the traversal is finished, the values meeting the conditions are formed into a matrix according to the recording sequence
Figure BDA0001195051140000037
V is to be1,kRecorded in a matrix
Figure BDA0001195051140000038
First row of (v)2,kRecorded in the second row of the matrix, resulting in:
Figure BDA0001195051140000041
n3is the number of matrix columns;
then to
Figure BDA0001195051140000042
Go through the rows by row, order
c1,k＝abs(v11,k+1-v11,k)
c2,k＝v12,k+1-v12,k
c3,k＝v12,k
Obtaining a depth periodic feature matrix
Figure BDA0001195051140000043
Constructing a width vector:
Figure BDA0001195051140000047
in the formula:
Figure BDA0001195051140000044
i＝1,2…nI
(xi1,yi1,zi1) Is the spatial coordinate of the left edge point, (x)i2,yi2,zi2) Is the spatial coordinate of the right edge point; n isIRepresenting the number of image lines.
The method for extracting the insulator string number characteristics and the disc diameter variation characteristics comprises the following steps:
(1) by
Figure BDA0001195051140000045
Extracting insulator string segment number characteristics
First, a threshold value alpha is setcInitializing the flag bit deltacIs 0, the segment number information num (C) is 0;
then to
Figure BDA0001195051140000046
Traversing by columns:
when C is present1,k＝1，C2,k＜αcAnd deltacWhen equal to 0, set the flag bit deltacIs 1, and C is3,kStored in vector T as upper edge of insulator string；
When deltac1 and C1,k0 or C2,k＞αcSetting a flag bit deltacIs 0, adding C3,kStoring the lower edge of the insulator string into the vector T, and adding 1 to the number information num (C);
vector T is the upper and lower edges of the insulator string, where the odd-term element T2n-1The even-numbered element t being the upper edge of the insulator string2nThe lower edge of the insulator string; n is 1,2 … k1；k1Is the length of the vector T.
(2) Extracting disc diameter change characteristics of insulator string from W
And calculating the width variance of the insulator string according to the vector T:
Figure BDA0001195051140000051
Figure BDA0001195051140000052
when d is2< threshold value alphawThe insulator is equal in disc diameter, otherwise, the insulator is variable in disc diameter.
The method for identifying the insulator string type according to the extracted features comprises the following steps:
if num (C) is 1, and d2< threshold value alphawIf the insulator string is a single-section insulator string with the same disc diameter; if num (C) is 2, and d2< threshold value alphawIf num (c) is 1 and d is equal to d2> threshold value alphawAnd the insulator string is a single-section disc-changing insulator string.
The invention has the following beneficial effects and advantages:
1. the invention utilizes the laser depth periodicity characteristic, the insulator string spacing characteristic and the disc diameter characteristic to identify, has high insulator identification accuracy, improves the working efficiency, and provides a basis for the completion of tasks of the transformer substation inspection robot.
2. The invention adopts the laser radar as the sensor, and can accurately identify the insulator string under the backlight condition. In the depth image, the foreground and the background can be accurately separated, and the influence of the complex background on the identification precision is overcome.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a laser depth map;
FIG. 3 is a depth binary map after preprocessing;
FIG. 4 is a depth signature graph;
fig. 5 is a recognition effect diagram.
Detailed Description
The present invention will be described in further detail with reference to examples.
A laser radar-based insulator string automatic identification method comprises the following steps:
(1) acquiring a depth image: installing laser radar equipment on the transformer substation inspection robot, fixing the laser radar equipment on a holder, and rotating the laser radar by a fixed angle to obtain a depth image;
(2) preprocessing of the depth image: and preprocessing the depth image with the insulator string to obtain the interested region.
(3) Feature extraction of the depth image: in the interested region, a depth characteristic curve model is established, a depth periodic characteristic matrix and a width vector are established, and insulator string segment number characteristics and disc diameter variation characteristics are extracted.
(4) Identifying the type of an insulator string: and identifying the type of the insulator string according to the extracted features.
The depth image preprocessing comprises the following steps:
and (2.1) separating the foreground and the background in the depth image through depth binarization processing, and eliminating background interference in the depth image.
And (2.2) processing the depth binary image by adopting image morphology, firstly adopting expansion operation, and then adopting corrosion operation to eliminate interference pixel points on the depth binary image.
And (2.3) selecting the maximum connected domain from the depth binary image, extracting the interested region and finishing the pretreatment.
The feature extraction method of the depth image comprises the following steps:
(3.1) establishing a depth characteristic curve model, wherein the defined depth characteristic curve can reflect the characteristics of the insulator string, and the method comprises the following steps:
extracting the left edge of the target area:
Figure BDA0001195051140000061
lik＝k,
Figure BDA0001195051140000062
extracting the right edge of the target area:
Figure BDA0001195051140000063
rik＝k,
Figure BDA0001195051140000071
where D (x, y) is a depth binary image, k represents the number of rows of the image, t represents the number of columns of the image, lk，rkPixel point locations on the edge. n isIRepresented as the maximum number of lines of the image. From these two sets, a set of positions on the line in the target area can be determined.
Figure BDA0001195051140000072
mik＝k,
Figure BDA0001195051140000073
In the formula mkAs coordinates of the depth profile pixel points, mikIs a depth profile pixel point row coordinate, mjkIs the pixel point column coordinate of the depth characteristic curve.
Establishing a depth characteristic curve model:
f(xk,yk)＝0
wherein:
xk＝mik
Figure BDA0001195051140000074
xkas the abscissa of the characteristic curve, i.e. the number of image lines, ykIs the ordinate of the characteristic curve, namely the depth value; l (m)k) Representing a depth image.
And (3.2) constructing a depth periodic feature matrix and a width vector through a certain rule, and extracting the insulator string number features and the disc diameter variation features.
And analyzing the number characteristics and the width characteristics of the insulator string segments extracted from the depth image to finish the identification of the insulator string.
The flow chart of the laser radar-based insulator string automatic identification method is shown in figure 1. The specific process is as follows:
1. laser radar data acquisition
The laser radar scans the insulator string region to obtain a laser depth image L (x, y), as shown in fig. 2.
2. Image pre-processing
Binarizing the depth image according to formula (1):
Figure BDA0001195051140000081
d (x, y) represents binarization, and M is a threshold value. And then carrying out expansion corrosion operation and marking the maximum connected domain. After pretreatment, the foreground and background can be separated to obtain the insulator string and the erection thereof. As shown in fig. 3.
3. Establishing a depth characteristic curve model
According to the depth periodic variation of the region where the insulator string is located in the depth image, a depth characteristic curve model is established, and the method comprises the following steps:
extracting the left edge of the target area:
Figure BDA0001195051140000082
lik＝k,
Figure BDA0001195051140000083
extracting the right edge of the target area:
Figure BDA0001195051140000084
rik＝k,
Figure BDA0001195051140000085
where D (x, y) is a binary image, k represents the number of rows in the image, t represents the number of columns in the image, lk，rkPixel point locations on the edge. The left edge and the right edge are respectively the left edge and the right edge of the insulator string. From these two sets, a set of positions of the lines in the target area can be determined.
Figure BDA0001195051140000086
mik＝k,
Figure BDA0001195051140000087
In the formula mkAs coordinates of the depth profile pixel points, mikIs a depth profile pixel point row coordinate, mjkIs a depth characteristic curvePixel point column coordinates.
Establishing a depth characteristic curve model:
f(xk,yk)＝0
wherein:
xk＝mik
Figure BDA0001195051140000091
as shown in fig. 4, the abscissa represents the number of image lines, and the ordinate represents the depth value.
4. Constructing depth periodic feature and width feature vectors
(1) Constructing a depth periodic feature matrix:
Figure BDA0001195051140000092
firstly, the depth characteristic curve f (x) is obtainedk,yk) Maximum and minimum values on 0. Building a matrix
Figure BDA0001195051140000093
In the formula:
Figure BDA0001195051140000094
v2kis a depth characteristic curve f (x)k,yk) 0 on the abscissa of the extreme point, v3kIs a depth characteristic curve f (x)k,yk) The ordinate of the extreme point at 0, i.e. the depth value. n is1Representation matrix
Figure BDA0001195051140000095
Number of columns, n2Representation matrix
Figure BDA0001195051140000096
The number of columns.
Setting a threshold lambdac1,λc2，λc1﹤λc2In this embodiment, 3 and 10 are taken, respectively. Then to
Figure BDA0001195051140000097
Go through by column, go v3,k+1-v3,k＞λc1And v is3,k+1-v3,k＜λc2V of time1,kAnd v2,kAnd recording is carried out. When the traversal is finished, the values meeting the conditions are formed into a matrix according to the recording sequence
Figure BDA0001195051140000098
V is to be1,kRecorded in a matrix
Figure BDA0001195051140000099
First row of (v)2,kRecorded in the second row of the matrix, resulting in:
Figure BDA00011950511400000910
n3is the number of matrix columns.
Then go through V1 by column, let
c1,k＝abs(v11,k+1-v11,k)
c2,k＝v12,k+1-v12,k
c3,k＝v12,k
And obtaining a depth periodic feature matrix.
(2) The width feature vector is constructed as follows:
Figure BDA0001195051140000106
in the formula:
Figure BDA0001195051140000101
i＝1，2...nI
(xi1,yi1,zi1) Is the spatial coordinate of the left edge point, (x)i2,yi2,zi2) The spatial coordinates of the right edge point. n isIRepresenting the maximum number of lines of the image. Here, the
5. Extracting insulator string segment number characteristic and disc diameter characteristic
(1) By
Figure BDA0001195051140000102
Extracting insulator string segment number characteristics
First, a threshold value alpha is setcIn this embodiment, 15 is taken. Initialization flag bit deltacIs 0, and the segment number information num (C) is 0.
Then to
Figure BDA0001195051140000103
Traversal is performed by column. When C is present1,k＝1，C2,k＜αcAnd deltacWhen equal to 0, set the flag bit deltacIs 1, and C is3,kAnd storing the vector T as the upper edge of the insulator string. When deltac1 and C1,k0 or C2,k＞αcSetting a flag bit deltacIs 0, adding C3,kAnd storing the lower edge of the insulator string into the vector T, and adding 1 to the segment number information num (C). Traversing to the tail end of the matrix to finish the recognition of the insulator string. Vector T is the upper and lower edges of the insulator string, where the odd-term element T2n-1The even-numbered element t being the upper edge of the insulator string2nThe lower edge of the insulator string. n is 1,2 … k1；k1Is the length of the vector T.
(2) Extracting disc diameter change characteristics of insulator string from W
And calculating the width variance of the insulator string according to the vector T:
Figure BDA0001195051140000104
Figure BDA0001195051140000105
when d is2＜αwWhen is αwFor the threshold, this embodiment takes 0.01. (ii) a And extracting the disc diameter characteristic as equal disc diameter, otherwise, extracting the disc diameter characteristic as variable disc diameter.
6. Insulator string type identification
And identifying three types of pillar insulator strings in the transformer substation according to the section number information and the disc diameter change information. Combining the obtained characteristics of the number of segments and the disc diameter, if num (C) is 1 and d is2< threshold value alphawIf num (c) is 2 and d is equal to d2< threshold value alphawIf num (c) is 1 and d is equal to d2> threshold value alphawAnd the insulator string is a single-section disc-changing insulator string. And (4) identifying three insulator strings commonly used by the transformer substation. As shown in fig. 5, the insulator string is a two-section equal-disc-diameter insulator string.

Claims (4)

1. The laser radar-based insulator string identification method is characterized by comprising the following steps of:
acquiring a depth image: obtaining a depth image through laser radar equipment arranged on the robot;
preprocessing of the depth image: preprocessing the depth image with the insulator string to obtain an interested area;
feature extraction of the depth image: in the interested region, establishing a depth characteristic curve model, constructing a depth periodic characteristic matrix and a width vector, and extracting insulator string segment number characteristics and disc diameter variation characteristics;
identifying the type of an insulator string: identifying the type of the insulator string according to the extracted features;
the depth characteristic curve model is established as follows:
extracting the left edge of the target area:
Figure FDA0003015882920000014
lik＝k,
Figure FDA0003015882920000011
extracting the right edge of the target area:
Figure FDA0003015882920000015
rik＝k,
Figure FDA0003015882920000012
where D (x, y) is a binary image, k represents the number of rows in the image, t represents the number of columns in the image, lk，rkPixel point locations on the edge;
determining the position on the line in the target area by the left and right edges:
Figure FDA0003015882920000016
mik＝k,
Figure FDA0003015882920000013
in the formula mkAs coordinates of the depth profile pixel points, mikIs a depth profile pixel point row coordinate, mjkThe pixel point row coordinates of the depth characteristic curve are obtained; n isIThe number of image lines;
establishing a depth characteristic curve model:
f(xk,yk)＝0
wherein:
xk＝mik
Figure FDA0003015882920000021
xkas the abscissa of the characteristic curve, i.e. the number of image lines, ykIs the ordinate of the characteristic curve, namely the depth value; l (m)k) Representing a depth image;
the construction of the depth periodic feature matrix comprises the following steps:
Figure FDA0003015882920000022
firstly, the depth characteristic curve f (x) is obtainedk,yk) Maximum and minimum values on 0; building a matrix
Figure FDA0003015882920000023
In the formula:
Figure FDA0003015882920000024
v2kis a depth characteristic curve f (x)k,yk) 0 on the abscissa of the extreme point, v3kIs a depth characteristic curve f (x)k,yk) The ordinate of the extreme point on 0, namely the depth value; k is 1,2 … nI；
Setting a threshold lambdac1,λc2And lambdac1﹤λc2(ii) a Then to
Figure FDA0003015882920000025
Traversing by column to obtain V3,k+1-V3,k＞λc1And V3,k+1-V3,k＜λc2Time V1,kAnd V2,kRecording; when traversal is over, the condition will be satisfiedThe values of (A) are formed into a matrix in the order of recording
Figure FDA0003015882920000026
V is to be1,kRecorded in a matrix
Figure FDA0003015882920000027
First row of (v)2,kRecorded in the second row of the matrix, resulting in:
Figure FDA0003015882920000028
n3is the number of matrix columns;
then to
Figure FDA0003015882920000029
Go through the rows by row, order
c1,k＝abs(v11,k+1-v11,k)
c2,k＝v12,k+1-v12,k
c3,k＝v12,k
Obtaining a depth periodic feature matrix
Figure FDA0003015882920000031
Constructing a width vector:
Figure FDA0003015882920000032
in the formula:
Figure FDA0003015882920000033
(xi1,yi1,zi1) Is the spatial coordinate of the left edge point, (x)i2,yi2,zi2) Is the right edgeSpatial coordinates of the points;
nIrepresenting the number of image lines.
2. The lidar-based insulator string recognition method of claim 1, wherein the pre-processing of the depth image comprises the steps of:
(2.1) carrying out binarization processing on the depth image, separating a foreground and a background in the depth image, and eliminating background interference;
(2.2) carrying out expansion operation on the processed binary image, and then carrying out corrosion operation to eliminate interference pixel points on the binary image;
and (2.3) selecting the maximum connected region, extracting the region of interest, and finishing pretreatment.
3. The method for identifying the insulator string based on the laser radar as claimed in claim 1, wherein the step of extracting the insulator string segment number characteristic and the disc diameter change characteristic comprises the following steps of:
(1) by
Figure FDA0003015882920000034
Extracting insulator string segment number characteristics
First, a threshold value alpha is setcInitializing the flag bit deltacIs 0, the segment number information num (C) is 0;
then to
Figure FDA0003015882920000035
Traversing by columns:
when C is present1,k＝1，C2,k＜αcAnd deltacWhen equal to 0, set the flag bit deltacIs 1, and C is3,kStoring the vector T as the upper edge of the insulator string;
when deltac1 and C1,k0 or C2,k＞αcSetting a flag bit deltacIs 0, adding C3,kStoring the lower edge of the insulator string into the vector T, and adding 1 to the number information num (C);
vector T is the upper and lower edges of the insulator string, where the odd-term element T2n-1The even-numbered element t being the upper edge of the insulator string2nThe lower edge of the insulator string; n is 1,2 … k1；k1Is the length of the vector T;
(2) extracting disc diameter change characteristics of insulator string from W
And calculating the width variance of the insulator string according to the vector T:
Figure FDA0003015882920000041
Figure FDA0003015882920000042
when d is2< threshold value alphawThe insulator is equal in disc diameter, otherwise, the insulator is variable in disc diameter.
4. The method for identifying the insulator string based on the lidar according to claim 3, wherein the identifying the type of the insulator string according to the extracted features comprises the following steps:
if num (C) is 1, and d2< threshold value alphawIf the insulator string is a single-section insulator string with the same disc diameter; if num (C) is 2, and d2< threshold value alphawIf num (c) is 1 and d is equal to d2> threshold value alphawAnd the insulator string is a single-section disc-changing insulator string.
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