CN112102206B - Inspection image recognition method for intelligent substation inspection robot - Google Patents
Inspection image recognition method for intelligent substation inspection robot Download PDFInfo
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- 238000007689 inspection Methods 0.000 title claims abstract description 167
- 238000000034 method Methods 0.000 title claims abstract description 24
- 239000011159 matrix material Substances 0.000 claims description 13
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Classifications
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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
- G06T5/90—Dynamic range modification of images or parts thereof
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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
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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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
- H04N23/70—Circuitry for compensating brightness variation in the scene
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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
- H04N23/70—Circuitry for compensating brightness variation in the scene
- H04N23/76—Circuitry for compensating brightness variation in the scene by influencing the image signals
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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
An intelligent substation inspection robot inspection image recognition method comprises the following steps: determining an alternative inspection area in a preset space around the inspection robot; acquiring the movement direction of the inspection robot; dividing the alternative inspection area into an actual inspection area and an abandoned inspection area according to the movement direction; judging whether the darkness of the image in the actual inspection area is lower than a preset value or not; if yes, the brightness of the image in the actual inspection area is increased; if not, shooting the image in the actual inspection area. The inspection image recognition method for the intelligent substation inspection robot provided by the application can automatically judge whether the darkness in the area in front of the inspection robot exceeds a preset value, automatically supplement brightness when the darkness exceeds the preset value, and ensure the quality of the shot pictures or videos.
Description
Technical Field
The invention belongs to the technical field of inspection robots, and particularly relates to an intelligent substation inspection robot inspection image recognition method.
Background
The inspection robot is often set to inspect an object along a predetermined line, and in the operation process, due to insufficient light in the surrounding environment, darkness in front of the inspection robot may be reduced, so that definition of a shot picture or video cannot be guaranteed, and the quality of the picture or video is low.
Disclosure of Invention
In order to solve the problems, the invention provides a method for identifying patrol images of an intelligent substation patrol robot, which comprises the following steps:
determining an alternative inspection area in a preset space around the inspection robot;
Acquiring the movement direction of the inspection robot;
dividing the alternative inspection area into an actual inspection area and an abandoned inspection area according to the movement direction;
judging whether the darkness of the image in the actual inspection area is lower than a preset value or not;
If yes, the brightness of the image in the actual inspection area is increased;
If not, shooting the image in the actual inspection area.
Preferably, the determining the candidate inspection area in the preset space around the inspection robot includes the steps of:
acquiring the current position coordinates of the inspection robot;
acquiring the action distance of a patrol signal of the patrol robot;
And taking the current position coordinate as a circle center and the action distance of the inspection signal as a radius to form a sphere so as to construct an alternative inspection area of the inspection robot.
Preferably, the dividing the alternative inspection area into an actual inspection area and a abandoned inspection area according to the movement direction includes the steps of:
acquiring the alternative inspection area;
Acquiring the movement direction of the inspection robot;
taking the inspection robot as a starting point to make a ray which is parallel to and in the same direction as the movement direction, wherein the ray and the alternative inspection area intersect at an intersection point:
Acquiring the distance between the intersection point and the inspection robot;
Taking the intersection point as a circle center and the distance as a radius as a sphere, wherein the intersection of the sphere and the alternative inspection area is the actual inspection area, and the difference between the alternative inspection area and the sphere is the abandoned inspection area.
Preferably, the calculation formulas of the actual inspection area a and the abandoned inspection area B are as follows:
A＝M∩N，B＝M-N；
Wherein M represents the alternative inspection area and N represents the sphere.
Preferably, the step of determining whether the darkness of the image in the actual inspection area is lower than a preset value includes the following steps:
transmitting light rays with preset pixel values into the actual inspection area;
Shooting an image in the actual inspection area;
acquiring an actual pixel value of each pixel point in the image;
calculating the ratio of the pixel points of which the actual pixel values are lower than the preset pixel values;
judging whether the ratio is lower than a preset threshold value or not;
If yes, judging that the darkness of the image in the actual inspection area is lower than a preset value;
If not, judging that the darkness of the image in the actual inspection area is higher than a preset value.
Preferably, the step of obtaining the actual pixel value of each pixel point in the image includes the steps of:
Traversing each pixel point in the image line by line in turn;
comparing each pixel point with a standard color card;
finding out the position of each pixel point in the standard color card;
and reading the pixel value corresponding to the position of each pixel point.
Preferably, the calculating the ratio of the pixel points with the actual pixel value lower than the preset pixel value includes the steps of:
acquiring the total number M of all pixel points in the image;
acquiring the number N of all pixel points of which the actual pixel values are lower than a preset pixel value in the image;
and (5) calculating a numerical value.
Preferably, the step of increasing the brightness of the image in the actual inspection area includes the steps of:
Acquiring an image in the actual inspection area;
Graying the image;
constructing a matrix of each pixel point and adjacent pixel points in the image;
acquiring a gray value of each pixel point in the matrix;
calculating the average value of gray values of the matrix;
judging whether the gray value of each pixel point in the matrix is larger than or equal to the gray value average value;
if yes, maintaining the current gray value of the pixel point;
if not, replacing the current gray value of the pixel point by using the gray value average value.
Preferably, the graying of the image comprises the steps of:
acquiring RGB values of each pixel point in the image;
assigning preset weights to R, G and B in the RGB values;
and calculating the RGB value after graying according to the preset weight.
Preferably, the calculation formula of the RGB numerical value after graying is:
RGB Rear part (S) ＝αR Front part +βG Front part +γB Front part
Wherein RGB Rear part (S) represents an RGB value after graying, α, β and γ are preset weights corresponding to R, G and B, respectively, R Front part 、G Front part and B Front part represent R, G and B values before graying, α+β+γ=1.
The inspection image recognition method for the intelligent substation inspection robot provided by the application can automatically judge whether the darkness in the area in front of the inspection robot exceeds a preset value, automatically supplement brightness when the darkness exceeds the preset value, and ensure the quality of the shot pictures or videos.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a flow chart of a method for identifying an inspection image of an intelligent substation inspection robot;
Fig. 2 is a schematic diagram of an intelligent substation inspection robot inspection image recognition method provided by the invention;
fig. 3 is a schematic diagram of an intelligent substation inspection robot inspection image recognition method provided by the invention.
Detailed Description
The objects, technical solutions and advantages of the present invention will become more apparent by the following detailed description of the present invention with reference to the accompanying drawings. It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
As shown in fig. 1, in an embodiment of the present application, the present application provides a method for identifying an inspection image of an intelligent substation inspection robot, where the method includes the steps of:
S101: determining an alternative inspection area in a preset space around the inspection robot;
s102: acquiring the movement direction of the inspection robot;
S103: dividing the alternative inspection area into an actual inspection area and an abandoned inspection area according to the movement direction;
s104: judging whether the darkness of the image in the actual inspection area is lower than a preset value or not;
s105: if yes, the brightness of the image in the actual inspection area is increased;
S106: if not, shooting the image in the actual inspection area.
As shown in fig. 2, in the embodiment of the present application, the determining the candidate inspection area in the preset space around the inspection robot in step S101 includes the steps of:
acquiring the current position coordinates of the inspection robot;
acquiring the action distance of a patrol signal of the patrol robot;
And taking the current position coordinate as a circle center and the action distance of the inspection signal as a radius to form a sphere so as to construct an alternative inspection area of the inspection robot.
In the embodiment of the present application, as shown in fig. 2, an alternative inspection area of the inspection robot may be obtained according to the above steps.
As shown in fig. 3, in the embodiment of the present application, in the step S103, the dividing the candidate inspection area into the actual inspection area and the abandoned inspection area according to the movement direction includes the steps of:
acquiring the alternative inspection area;
Acquiring the movement direction of the inspection robot;
taking the inspection robot as a starting point to make a ray which is parallel to and in the same direction as the movement direction, wherein the ray and the alternative inspection area intersect at an intersection point:
Acquiring the distance between the intersection point and the inspection robot;
Taking the intersection point as a circle center and the distance as a radius as a sphere, wherein the intersection of the sphere and the alternative inspection area is the actual inspection area, and the difference between the alternative inspection area and the sphere is the abandoned inspection area.
In the embodiment of the present application, as shown in fig. 3, the actual inspection area and the abandoned inspection area can be obtained through the above operations.
Specifically, the calculation formulas of the actual inspection area a and the abandoned inspection area B are as follows:
A＝M∩N，B＝M-N；
Wherein M represents the alternative inspection area and N represents the sphere.
In the embodiment of the present application, the step S104 of determining whether the darkness of the image in the actual inspection area is lower than a preset value includes the steps of:
transmitting light rays with preset pixel values into the actual inspection area;
Shooting an image in the actual inspection area;
acquiring an actual pixel value of each pixel point in the image;
calculating the ratio of the pixel points of which the actual pixel values are lower than the preset pixel values;
judging whether the ratio is lower than a preset threshold value or not;
If yes, judging that the darkness of the image in the actual inspection area is lower than a preset value;
If not, judging that the darkness of the image in the actual inspection area is higher than a preset value.
In the embodiment of the application, light with a preset pixel value is emitted into an actual inspection area, such as pure white light (256,256,256), then an image in the actual inspection area is shot, and then the ratio of the pixel points with the actual pixel value lower than the preset pixel value is calculated. Meanwhile, the ratio of the pixel points with the actual pixel values lower than the preset pixel values needs to be calculated, if the ratio exceeds a preset threshold value, the darkness of the image in the actual inspection area is lower than the preset value, otherwise, the darkness of the image in the actual inspection area is higher than the preset value.
In the embodiment of the present application, the step of obtaining the actual pixel value of each pixel point in the image includes the steps of:
Traversing each pixel point in the image line by line in turn;
comparing each pixel point with a standard color card;
finding out the position of each pixel point in the standard color card;
and reading the pixel value corresponding to the position of each pixel point.
In the embodiment of the application, the pixel value of each pixel point can be obtained through the operation.
In the embodiment of the present application, the calculating the ratio of the pixel points where the actual pixel value is lower than the preset pixel value includes the steps of:
acquiring the total number M of all pixel points in the image;
acquiring the number N of all pixel points of which the actual pixel values are lower than a preset pixel value in the image;
and (5) calculating a numerical value.
In the embodiment of the present application, the ratio of the pixel points where the actual pixel value is lower than the preset pixel value may be calculated through the above operation.
In the embodiment of the present application, the step of increasing the brightness of the image in the actual inspection area includes the steps of:
Acquiring an image in the actual inspection area;
Graying the image;
constructing a matrix of each pixel point and adjacent pixel points in the image;
acquiring a gray value of each pixel point in the matrix;
calculating the average value of gray values of the matrix;
judging whether the gray value of each pixel point in the matrix is larger than or equal to the gray value average value;
if yes, maintaining the current gray value of the pixel point;
if not, replacing the current gray value of the pixel point by using the gray value average value.
In the embodiment of the application, the matrix of each pixel point comprises four pixel points above, left, below and right around the pixel point, and the brightness of the image can be increased through the operation.
In an embodiment of the present application, the graying the image includes the steps of:
acquiring RGB values of each pixel point in the image;
assigning preset weights to R, G and B in the RGB values;
and calculating the RGB value after graying according to the preset weight.
In the embodiment of the application, the RGB values of the image after graying can be calculated according to the steps.
In the embodiment of the application, the calculation formula of the RGB numerical value after graying is as follows:
RGB Rear part (S) ＝αR Front part +βG Front part +γB Front part
Wherein RGB Rear part (S) represents an RGB value after graying, α, β and γ are preset weights corresponding to R, G and B, respectively, R Front part 、G Front part and B Front part represent R, G and B values before graying, α+β+γ=1.
The inspection image recognition method for the intelligent substation inspection robot provided by the application can automatically judge whether the darkness in the area in front of the inspection robot exceeds a preset value, automatically supplement brightness when the darkness exceeds the preset value, and ensure the quality of the shot pictures or videos.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explanation of the principles of the present invention and are in no way limiting of the invention. Accordingly, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present invention should be included in the scope of the present invention. Furthermore, the appended claims are intended to cover all such changes and modifications that fall within the scope and boundary of the appended claims, or equivalents of such scope and boundary.
Claims (9)
1. The method for identifying the inspection image of the intelligent substation inspection robot is characterized by comprising the following steps:
determining an alternative inspection area in a preset space around the inspection robot;
Acquiring the movement direction of the inspection robot;
acquiring the alternative inspection area;
taking the inspection robot as a starting point to make a ray which is parallel to and in the same direction as the movement direction, wherein the ray and the alternative inspection area intersect at an intersection point:
Acquiring the distance between the intersection point and the inspection robot;
taking the intersection point as a circle center and the distance as a radius as a sphere, wherein the intersection of the sphere and the alternative inspection area is an actual inspection area, and the difference between the alternative inspection area and the sphere is a abandoned inspection area;
judging whether the darkness of the image in the actual inspection area is lower than a preset value or not;
If yes, the brightness of the image in the actual inspection area is increased;
If not, shooting the image in the actual inspection area.
2. The method for identifying the inspection image of the inspection robot of the intelligent substation according to claim 1, wherein the step of determining the candidate inspection area in the preset space around the inspection robot comprises the steps of:
acquiring the current position coordinates of the inspection robot;
acquiring the action distance of a patrol signal of the patrol robot;
And taking the current position coordinate as a circle center and the action distance of the inspection signal as a radius to form a sphere so as to construct an alternative inspection area of the inspection robot.
3. The intelligent substation inspection robot inspection image recognition method according to claim 1, wherein the calculation formulas of the actual inspection area a and the abandoned inspection area B are as follows:
A=M∩N，B=M-N；
Wherein M represents the alternative inspection area and N represents the sphere.
4. The method for identifying the inspection image of the intelligent substation inspection robot according to claim 1, wherein the step of determining whether the darkness of the image in the actual inspection area is lower than a preset value comprises the steps of:
transmitting light rays with preset pixel values into the actual inspection area;
Shooting an image in the actual inspection area;
acquiring an actual pixel value of each pixel point in the image;
calculating the ratio of the pixel points of which the actual pixel values are lower than the preset pixel values;
judging whether the ratio is lower than a preset threshold value or not;
if yes, judging that the darkness of the image in the actual inspection area is lower than the preset value;
if not, judging that the darkness of the image in the actual inspection area is higher than the preset value.
5. The method for identifying the inspection image of the intelligent substation inspection robot according to claim 4, wherein the step of obtaining the actual pixel value of each pixel point in the image comprises the steps of:
Traversing each pixel point in the image line by line in turn;
comparing each pixel point with a standard color card;
finding out the position of each pixel point in the standard color card;
and reading the pixel value corresponding to the position of each pixel point.
6. The method for recognizing an inspection image of an intelligent substation inspection robot according to claim 4, wherein the calculating the ratio of the pixel points where the actual pixel value is lower than the preset pixel value includes the steps of:
acquiring the total number M of all pixel points in the image;
And acquiring the number N of all pixel points of which the actual pixel values are lower than the preset pixel values in the image.
7. The method for identifying the inspection image of the intelligent substation inspection robot according to claim 1, wherein the step of increasing the brightness of the image in the actual inspection area comprises the steps of:
Acquiring an image in the actual inspection area;
Graying the image;
constructing a matrix of each pixel point and adjacent pixel points in the image;
acquiring a gray value of each pixel point in the matrix;
calculating the average value of gray values of the matrix;
judging whether the gray value of each pixel point in the matrix is larger than or equal to the gray value average value;
if yes, maintaining the current gray value of the pixel point;
if not, replacing the current gray value of the pixel point by using the gray value average value.
8. The method for identifying the inspection image of the intelligent substation inspection robot according to claim 7, wherein the step of graying the image comprises the steps of:
acquiring RGB values of each pixel point in the image;
Assigning preset weights to R, G and B in the RGB values;
and calculating the RGB value after graying according to the preset weight.
9. The intelligent substation inspection robot inspection image recognition method according to claim 8, wherein the grayscale RGB numerical calculation formula is:
；
Wherein, Representing the RGB value after graying, wherein alpha, beta and gamma are preset weights corresponding to R, G and B respectively,/>、And/>The R, G and B values before graying, α+β+γ=1.
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