CN112102206A - Inspection image identification method for inspection robot of intelligent substation - Google Patents
Inspection image identification method for inspection robot of intelligent substation Download PDFInfo
- Publication number
- CN112102206A CN112102206A CN202011136812.0A CN202011136812A CN112102206A CN 112102206 A CN112102206 A CN 112102206A CN 202011136812 A CN202011136812 A CN 202011136812A CN 112102206 A CN112102206 A CN 112102206A
- Authority
- CN
- China
- Prior art keywords
- inspection
- image
- inspection area
- actual
- robot
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000007689 inspection Methods 0.000 title claims abstract description 163
- 238000000034 method Methods 0.000 title claims abstract description 25
- 239000011159 matrix material Substances 0.000 claims description 13
- 238000010586 diagram Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 238000005286 illumination Methods 0.000 description 1
Images
Classifications
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Analysis (AREA)
Abstract
An intelligent substation inspection robot inspection image identification method comprises the following steps: determining a standby inspection area in a preset space around the inspection robot; acquiring the motion direction of the inspection robot; dividing the alternative patrol inspection area into an actual patrol inspection area and a abandoned patrol inspection area according to the movement direction; judging whether the image darkness in the actual inspection area is lower than a preset value or not; if yes, increasing the image brightness in the actual inspection area; and if not, shooting the image in the actual inspection area. The application provides a patrol and examine image recognition method of robot is patrolled and examined to intelligent substation, can judge automatically whether the regional darkness in the front of the robot of patrolling and examining exceeds the default to automatic supplementary luminance when exceeding the default, guarantee the quality of taking picture or video.
Description
Technical Field
The invention belongs to the technical field of inspection robots, and particularly relates to an inspection image identification method of an inspection robot of an intelligent substation.
Background
Patrol and examine the robot and often be set up to patrolling and examining the object along predetermined circuit, and at the operation in-process, because light in the surrounding environment is not enough, probably lead to patrolling and examining the darkness in robot the place ahead and reduce to can't guarantee to shoot picture or video definition, picture or video quality are low.
Disclosure of Invention
In order to solve the problems, the invention provides a method for identifying an inspection image of an inspection robot of an intelligent substation, which comprises the following steps:
determining a standby inspection area in a preset space around the inspection robot;
acquiring the motion direction of the inspection robot;
dividing the alternative patrol inspection area into an actual patrol inspection area and a abandoned patrol inspection area according to the movement direction;
judging whether the image darkness in the actual inspection area is lower than a preset value or not;
if yes, increasing the image brightness in the actual inspection area;
and if not, shooting the image in the actual inspection area.
Preferably, the step of determining the alternative inspection area in the preset space around the inspection robot comprises the following steps:
acquiring the current position coordinate of the inspection robot;
acquiring the routing inspection signal action distance of the routing inspection robot;
and taking the current position coordinate as a circle center and the routing inspection signal action distance as a radius to form a sphere so as to construct a standby routing inspection area of the routing inspection robot.
Preferably, the dividing the alternative patrol inspection area into an actual patrol inspection area and a discard patrol inspection area according to the movement direction includes the steps of:
acquiring the alternative inspection area;
acquiring the motion direction of the inspection robot;
using the inspection robot as a starting point to make rays parallel and in the same direction as the moving direction, wherein the rays and the alternative inspection area are intersected at an intersection point:
acquiring the distance between the intersection point and the inspection robot;
the intersection of the sphere and the alternative patrol inspection area is the actual patrol inspection area, and the difference between the alternative patrol inspection area and the sphere is the abandonment patrol inspection area.
Preferably, the calculation formulas of the actual inspection area a and the abandoning inspection area B are as follows:
A＝M∩N，B＝M-N；
wherein M represents the alternative patrol area, and N represents the sphere.
Preferably, the step of judging whether the image darkness in the actual inspection area is lower than a preset value comprises the steps of:
emitting 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 image darkness in the actual inspection area is lower than a preset value;
if not, the image darkness in the actual inspection area is judged to be higher than a preset value.
Preferably, the acquiring an actual pixel value of each pixel point in the image includes:
sequentially traversing each pixel point in the image line by line;
comparing each pixel point with a standard color card;
finding out the position of each pixel point in the standard color card;
and reading a pixel value corresponding to the position of each pixel point.
Preferably, the calculating the ratio of the pixel points of which the actual pixel values are lower than the preset pixel values includes:
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 the preset pixel values in the image;
the calculated value.
Preferably, 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 thereof in the image;
acquiring the gray value of each pixel point in the matrix;
calculating the gray value average value of the matrix;
judging whether the gray value of each pixel point in the matrix is greater than or equal to the average gray value;
if so, keeping the current gray value of the pixel point;
and if not, replacing the current gray value of the pixel point by using the average gray value.
Preferably, the graying the image comprises the steps of:
acquiring the RGB value of each pixel point in the image;
allocating preset weights to R, G and B in the RGB values;
and calculating the grayed RGB numerical value according to the preset weight.
Preferably, the grayed RGB numerical calculation formula is:
RGBrear end＝αRFront side+βGFront side+γBFront side
Wherein, RGBRear endRepresenting grayed RGB values, alpha, beta and gamma being preset weights corresponding to R, G and B, respectively, RFront side、GFront sideAnd BFront sideThe values of R, G and B before graying are shown, and α + β + γ is 1.
The application provides a patrol and examine image recognition method of robot is patrolled and examined to intelligent substation, can judge automatically whether the regional darkness in the front of the robot of patrolling and examining exceeds the default to automatic supplementary luminance when exceeding the default, guarantee the quality of taking picture or video.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flow chart of a method for identifying inspection images of an inspection robot of an intelligent substation, which is provided by the invention;
fig. 2 is a schematic diagram of an inspection image identification method of an intelligent substation inspection robot provided by the invention;
fig. 3 is a schematic diagram of an inspection image identification method of an intelligent substation inspection robot provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
As shown in fig. 1, in the embodiment of the present application, the present application provides a method for identifying an inspection image of an inspection robot of an intelligent substation, the method includes the steps of:
s101: determining a standby inspection area in a preset space around the inspection robot;
s102: acquiring the motion direction of the inspection robot;
s103: dividing the alternative patrol inspection area into an actual patrol inspection area and a abandoned patrol inspection area according to the movement direction;
s104: judging whether the image darkness in the actual inspection area is lower than a preset value or not;
s105: if yes, increasing the image brightness in the actual inspection area;
s106: and 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 alternative inspection area in the preset space around the inspection robot in step S101 includes:
acquiring the current position coordinate of the inspection robot;
acquiring the routing inspection signal action distance of the routing inspection robot;
and taking the current position coordinate as a circle center and the routing inspection signal action distance as a radius to form a sphere so as to construct a standby routing inspection area of the routing inspection robot.
Referring to fig. 2, in the embodiment of the present application, a candidate 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, the dividing the alternative patrol area into the actual patrol area and the discard patrol area according to the moving direction in step S103 includes:
acquiring the alternative inspection area;
acquiring the motion direction of the inspection robot;
using the inspection robot as a starting point to make rays parallel and in the same direction as the moving direction, wherein the rays and the alternative inspection area are intersected at an intersection point:
acquiring the distance between the intersection point and the inspection robot;
the intersection of the sphere and the alternative patrol inspection area is the actual patrol inspection area, and the difference between the alternative patrol inspection area and the sphere is the abandonment patrol inspection area.
As shown in fig. 3, in the embodiment of the present application, the actual patrol area and the discard patrol area can be obtained through the above operations.
Specifically, the calculation formulas of the actual inspection area a and the abandon inspection area B are as follows:
A＝M∩N，B＝M-N；
wherein M represents the alternative patrol area, and N represents the sphere.
In this embodiment of the application, the step of determining whether the darkness of the image in the actual inspection area is lower than the preset value in step S104 includes the steps of:
emitting 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 image darkness in the actual inspection area is lower than a preset value;
if not, the image darkness in the actual inspection area is judged to be higher than a preset value.
In this application embodiment, at first to the light of predetermineeing the pixel value of actually patrolling and examining the region, for example launch pure white light (256,256,256), then shoot and actually patrol and examine the image in the region, then calculate actual pixel value and be less than predetermine the pixel value the ratio of pixel, because the light in actually patrolling and examining the region may be insufficient, the pixel value that the pixel in the picture of shooting this moment has partial pixel becomes low. 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 illumination actual inspection area is lower than a preset value, and otherwise, the darkness of the image in the actual inspection area is higher than the preset value.
In this embodiment of the present application, the obtaining an actual pixel value of each pixel point in the image includes:
sequentially traversing each pixel point in the image line by line;
comparing each pixel point with a standard color card;
finding out the position of each pixel point in the standard color card;
and reading a pixel value corresponding to the position of each pixel point.
In the embodiment of the present application, the pixel value of each pixel point can be obtained through the above operations.
In this embodiment of the present application, the calculating a ratio of the pixel points whose actual pixel values are lower than the preset pixel values includes:
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 the preset pixel values in the image;
the calculated value.
In this embodiment of the present application, the ratio of the pixel point whose actual pixel value is lower than the preset pixel value may be calculated through the above operations.
In this embodiment of the application, the increasing the brightness of the image in the actual inspection area includes:
acquiring an image in the actual inspection area;
graying the image;
constructing a matrix of each pixel point and adjacent pixel points thereof in the image;
acquiring the gray value of each pixel point in the matrix;
calculating the gray value average value of the matrix;
judging whether the gray value of each pixel point in the matrix is greater than or equal to the average gray value;
if so, keeping the current gray value of the pixel point;
and if not, replacing the current gray value of the pixel point by using the average gray value.
In the embodiment of the application, the matrix of each pixel point comprises the pixel point and four pixel points around the pixel point, namely, the upper pixel point, the left pixel point, the lower pixel point and the right 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:
acquiring the RGB value of each pixel point in the image;
allocating preset weights to R, G and B in the RGB values;
and calculating the grayed RGB numerical value according to the preset weight.
In the embodiment of the present application, the RGB values after the image graying can be calculated according to the above steps.
In the embodiment of the present application, the formula for calculating the grayed RGB values is as follows:
RGBrear end＝αRFront side+βGFront side+γBFront side
Wherein, RGBRear endRepresenting grayed RGB values, alpha, beta and gamma being preset weights corresponding to R, G and B, respectively, RFront side、GFront sideAnd BFront sideThe values of R, G and B before graying are shown, and α + β + γ is 1.
The application provides a patrol and examine image recognition method of robot is patrolled and examined to intelligent substation, can judge automatically whether the regional darkness in the front of the robot of patrolling and examining exceeds the default to automatic supplementary luminance when exceeding the default, guarantee the quality of taking picture or video.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (10)
1. An intelligent substation inspection robot inspection image identification method is characterized by comprising the following steps:
determining a standby inspection area in a preset space around the inspection robot;
acquiring the motion direction of the inspection robot;
dividing the alternative patrol inspection area into an actual patrol inspection area and a abandoned patrol inspection area according to the movement direction;
judging whether the image darkness in the actual inspection area is lower than a preset value or not;
if yes, increasing the image brightness in the actual inspection area;
and if not, shooting the image in the actual inspection area.
2. The intelligent substation inspection robot inspection image identification method according to claim 1, wherein the step of determining the alternative inspection area in the preset space around the inspection robot comprises the steps of:
acquiring the current position coordinate of the inspection robot;
acquiring the routing inspection signal action distance of the routing inspection robot;
and taking the current position coordinate as a circle center and the routing inspection signal action distance as a radius to form a sphere so as to construct a standby routing inspection area of the routing inspection robot.
3. The intelligent substation inspection robot inspection image identification method according to claim 1, wherein the dividing of the alternative inspection area into an actual inspection area and a discard inspection area according to the movement direction comprises the steps of:
acquiring the alternative inspection area;
acquiring the motion direction of the inspection robot;
using the inspection robot as a starting point to make rays parallel and in the same direction as the moving direction, wherein the rays and the alternative inspection area are intersected at an intersection point:
acquiring the distance between the intersection point and the inspection robot;
the intersection of the sphere and the alternative patrol inspection area is the actual patrol inspection area, and the difference between the alternative patrol inspection area and the sphere is the abandonment patrol inspection area.
4. The intelligent substation inspection robot inspection image identification method according to claim 3, wherein the calculation formulas of the actual inspection area A and the abandoning inspection area B are as follows:
A＝M∩N，B＝M-N；
wherein M represents the alternative patrol area, and N represents the sphere.
5. The intelligent substation inspection robot inspection image identification method according to claim 1, wherein the step of judging whether the darkness of the image in the actual inspection area is lower than a preset value comprises the steps of:
emitting 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 image darkness in the actual inspection area is lower than a preset value;
if not, the image darkness in the actual inspection area is judged to be higher than a preset value.
6. The intelligent substation inspection robot inspection image identification method according to claim 5, wherein the obtaining of the actual pixel value of each pixel point in the image comprises the steps of:
sequentially traversing each pixel point in the image line by line;
comparing each pixel point with a standard color card;
finding out the position of each pixel point in the standard color card;
and reading a pixel value corresponding to the position of each pixel point.
7. The intelligent substation inspection robot inspection image identification method according to claim 5, wherein the calculating the ratio of the pixel points with the actual pixel values lower than the preset pixel values comprises 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 the preset pixel values in the image;
the calculated value.
8. The intelligent substation inspection robot inspection image identification method 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 thereof in the image;
acquiring the gray value of each pixel point in the matrix;
calculating the gray value average value of the matrix;
judging whether the gray value of each pixel point in the matrix is greater than or equal to the average gray value;
if so, keeping the current gray value of the pixel point;
and if not, replacing the current gray value of the pixel point by using the average gray value.
9. The intelligent substation inspection robot inspection image identification method according to claim 1, wherein graying the image comprises the steps of:
acquiring the RGB value of each pixel point in the image;
allocating preset weights to R, G and B in the RGB values;
and calculating the grayed RGB numerical value according to the preset weight.
10. The inspection image recognition method for the inspection robot of the intelligent substation according to claim 9, wherein the grayed RGB numerical calculation formula is as follows:
RGBrear end＝αRFront side+βGFront side+γBFront side
Wherein, RGBRear endRepresenting grayed RGB values, alpha, beta and gamma being preset weights corresponding to R, G and B, respectively, RFront side、GFront sideAnd BFront sideThe values of R, G and B before graying are shown, and α + β + γ is 1.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010665379 | 2020-07-11 | ||
| CN2020106653793 | 2020-07-11 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112102206A true CN112102206A (en) | 2020-12-18 |
| CN112102206B CN112102206B (en) | 2024-05-28 |
Family
ID=73785949
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011136812.0A Active CN112102206B (en) | 2020-07-11 | 2020-10-22 | Inspection image recognition method for intelligent substation inspection robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112102206B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106506953A (en) * | 2016-10-28 | 2017-03-15 | 山东鲁能智能技术有限公司 | The substation equipment image acquisition method of servo is focused on and is exposed based on designated area |
| CN206598277U (en) * | 2016-08-31 | 2017-10-31 | 杭州申昊科技股份有限公司 | A kind of crusing robot |
| CN107992857A (en) * | 2017-12-25 | 2018-05-04 | 深圳钰湖电力有限公司 | A kind of high-temperature steam leakage automatic detecting recognition methods and identifying system |
| CN108803627A (en) * | 2018-08-20 | 2018-11-13 | 国网福建省电力有限公司 | A kind of crusing robot paths planning method suitable for substation's cubicle switch room |
| CN110593957A (en) * | 2019-10-08 | 2019-12-20 | 上海市东方海事工程技术有限公司 | Tunnel inspection method |
| CN111027422A (en) * | 2019-11-27 | 2020-04-17 | 国网山东省电力公司电力科学研究院 | Emergency unmanned aerial vehicle inspection method and system applied to power transmission line corridor |
| CN111292439A (en) * | 2020-01-22 | 2020-06-16 | 上海杰狮信息技术有限公司 | Unmanned aerial vehicle inspection method and inspection system for urban pipe network |
-
2020
- 2020-10-22 CN CN202011136812.0A patent/CN112102206B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN206598277U (en) * | 2016-08-31 | 2017-10-31 | 杭州申昊科技股份有限公司 | A kind of crusing robot |
| CN106506953A (en) * | 2016-10-28 | 2017-03-15 | 山东鲁能智能技术有限公司 | The substation equipment image acquisition method of servo is focused on and is exposed based on designated area |
| CN107992857A (en) * | 2017-12-25 | 2018-05-04 | 深圳钰湖电力有限公司 | A kind of high-temperature steam leakage automatic detecting recognition methods and identifying system |
| CN108803627A (en) * | 2018-08-20 | 2018-11-13 | 国网福建省电力有限公司 | A kind of crusing robot paths planning method suitable for substation's cubicle switch room |
| CN110593957A (en) * | 2019-10-08 | 2019-12-20 | 上海市东方海事工程技术有限公司 | Tunnel inspection method |
| CN111027422A (en) * | 2019-11-27 | 2020-04-17 | 国网山东省电力公司电力科学研究院 | Emergency unmanned aerial vehicle inspection method and system applied to power transmission line corridor |
| CN111292439A (en) * | 2020-01-22 | 2020-06-16 | 上海杰狮信息技术有限公司 | Unmanned aerial vehicle inspection method and inspection system for urban pipe network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112102206B (en) | 2024-05-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11790504B2 (en) | Monitoring method and apparatus | |
| US8542873B2 (en) | Motion object detection method using adaptive background model and computer-readable storage medium | |
| US7940955B2 (en) | Vision-based method of determining cargo status by boundary detection | |
| CN111246051B (en) | Method, device, equipment and storage medium for automatically detecting stripes and inhibiting stripes | |
| CN109741307B (en) | Stray light detection method, stray light detection device and stray light detection system of camera module | |
| US20110033086A1 (en) | Image processing apparatus and image processing method | |
| CN108259770B (en) | Image processing method, image processing device, storage medium and electronic equipment | |
| US8134614B2 (en) | Image processing apparatus for detecting an image using object recognition | |
| CN106778534B (en) | Method for identifying ambient light during vehicle running | |
| CN107404647A (en) | Camera lens condition detection method and device | |
| JP5279653B2 (en) | Image tracking device, image tracking method, and computer program | |
| CN105989583B (en) | A kind of image defogging method | |
| CN110720224B (en) | Image processing method and device | |
| CN111723805B (en) | Method and related device for identifying foreground region of signal lamp | |
| US20050030415A1 (en) | Exposure adjustment in an imaging apparatus | |
| CN113126252B (en) | Low-light-level imaging system | |
| CN112102206A (en) | Inspection image identification method for inspection robot of intelligent substation | |
| JPH1021408A (en) | Device and method for extracting image | |
| CN115953726B (en) | Machine vision container face damage detection method and system | |
| CN105184758A (en) | Defogging and enhancing method for image | |
| JP2005049979A (en) | Face detection device and interphone system | |
| CN113259595B (en) | Image acquisition method, image acquisition device and storage medium | |
| WO2020215865A1 (en) | Target tracking system and method | |
| CN113808117A (en) | Lamp detection method, device, equipment and storage medium | |
| CN112770021B (en) | Camera and filter switching method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |