CN113390386A - Artificial intelligence-based antenna azimuth angle measurement method, device and system - Google Patents
Artificial intelligence-based antenna azimuth angle measurement method, device and system Download PDFInfo
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Abstract
The application discloses antenna azimuth angle measurement method, device and system based on artificial intelligence, wherein, antenna azimuth angle measurement method based on artificial intelligence includes: acquiring an aerial overlook image shot by a camera; identifying an antenna overlook image according to a preset image identification model to obtain an antenna profile; wherein the antenna profile comprises a profile of a top plane of the antenna; identifying the outline of the top plane of the antenna to obtain the normal direction of the antenna; acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotating direction; acquiring the absolute angle of the orientation of the camera according to the rotation direction and a first preset direction; and acquiring the azimuth angle of the antenna according to the first relative angle and the absolute angle. The antenna azimuth angle measuring method based on artificial intelligence can quickly measure the antenna azimuth angle, is high in measuring efficiency, does not need to be assisted by more tools, and is high in accuracy.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to an antenna azimuth angle measurement method, apparatus, and system based on artificial intelligence.
Background
In the related art, in the field of communications, a public mobile communication base station (hereinafter, referred to as a base station) is a form of a radio station, which is a radio transceiver station that performs information transfer with a mobile phone terminal through a mobile communication switching center in a certain radio coverage area. An antenna, which is the core of a base station, is a device for transmitting and receiving radio signals. Since the radiating plane of the antenna is oriented in a fixed manner, in order to achieve the best coverage effect of the antenna, the antenna is usually designed to be installed in a specific orientation (i.e. a preset azimuth angle) when the base station is built, which puts forward a need for measuring the installation azimuth angle of the antenna in the field of base station antennas.
The antenna is used as a main carrier of signal transmission, effective measurement needs to be carried out on parameters of the antenna, the optimal value of the antenna signal transmission is analyzed and judged through the measured parameters, manual measurement is mostly adopted in traditional antenna attitude parameter measurement, complex calculation is needed, the parameter accuracy of the manual measurement cannot be guaranteed, different tools are needed to implement assistance in multi-parameter measurement, and the efficiency is very low.
Disclosure of Invention
The present application is directed to solving at least one of the problems in the prior art. Therefore, the application provides an antenna azimuth angle measuring method, device and system based on artificial intelligence and a computer readable storage medium, which can rapidly measure the antenna azimuth angle through an unmanned aerial vehicle, have high measuring efficiency, do not need to adopt more tools for assistance, and have high accuracy.
The artificial intelligence-based antenna azimuth angle measurement method according to the first aspect of the application comprises the following steps:
acquiring an aerial overlook image shot by a camera; the position of the camera is higher than the highest height of the top end of the antenna to be detected, the camera is arranged on the flying device, and the aerial overlook image comprises an image of the top plane of the antenna;
identifying the antenna overlook image according to a preset image identification model to obtain an antenna profile; wherein the antenna profile comprises a profile of an antenna top plane;
identifying the outline of the top plane of the antenna to obtain the normal direction of the antenna;
acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotation direction; the first relative angle is a horizontal included angle between a camera facing line and a normal direction line according to the rotation direction;
acquiring the absolute angle of the orientation of the camera according to the rotation direction and a first preset direction; the absolute angle is a horizontal included angle between a first preset direction line and a camera orientation line according to the rotating direction;
and acquiring the azimuth angle of the antenna according to the first relative angle and the absolute angle.
According to the antenna azimuth angle measuring method based on artificial intelligence recognition, the method at least has the following beneficial effects: firstly, obtaining an antenna overlook image shot by a camera, and then identifying the antenna overlook image according to a preset image identification model to obtain an antenna profile, wherein the antenna profile comprises a profile of a top plane of the antenna, and the image identification model can accurately identify the profile of the top plane of the antenna and provide a favorable basis for subsequently determining a finding direction; then, identifying the outline of the top plane of the antenna to obtain the normal direction of the antenna; thereafter, acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotating direction, and acquiring an absolute angle of the orientation of the camera according to a first preset direction and the rotating direction; and finally, accurately acquiring the azimuth angle of the antenna according to the first relative angle and the absolute angle. Therefore, the antenna azimuth angle measurement method based on artificial intelligence recognition can quickly measure the antenna azimuth angle, is high in measurement efficiency, does not need to be assisted by more tools, and is high in accuracy.
According to some embodiments of the application, the acquiring of the aerial overhead view image shot by the camera comprises:
setting the image center in the camera in the back area of the antenna;
and acquiring an aerial overlook image shot according to the camera of the flying device according to the back area.
According to some embodiments of the application, the identifying the profile of the antenna top plane to obtain the normal direction of the antenna comprises:
identifying the outline of the antenna top plane according to a preset antenna normal direction algorithm, and fitting a rectangle of the antenna top plane;
obtaining two longest sides according to the rectangle;
obtaining the normal directions of the two longest sides according to the middle points of the two longest sides; and the normal directions of the two longest sides are directions which are far away from the rectangle by taking the middle point as a starting point.
According to some embodiments of the present application, the identifying the profile of the antenna top plane to obtain the normal direction of the antenna further includes:
establishing a rectangular coordinate system by taking the image center as an origin;
obtaining quadrants to which the normal directions of the two longest sides belong;
and obtaining the normal direction of the antenna according to the quadrant to which the normal directions of the two longest sides belong.
According to some embodiments of the application, the obtaining a first relative angle between the camera orientation and the normal direction according to a direction of rotation comprises:
according to the rotation direction and a second preset direction, a second opposite angle of the orientation of the camera and a third opposite angle of the normal direction phase are obtained; the second opposite angle is a horizontal included angle between a second preset direction line and the camera facing line according to the rotating direction, and the third opposite angle is a horizontal included angle between the second preset direction line and the normal direction line according to the rotating direction;
and acquiring the first relative angle according to the second relative angle and the third relative angle.
According to some embodiments of the application, the first predetermined direction is a north direction;
the obtaining the absolute angle of the orientation of the camera according to the rotation direction and the first preset direction comprises:
detecting to obtain the true north direction according to the gyroscope of the flight device and a preset geographic coordinate system;
acquiring the absolute angle of the orientation of the camera according to the north; and the absolute angle is a horizontal included angle between a north direction line and the camera orientation line according to the rotation direction.
According to some embodiments of the application, the obtaining the antenna azimuth angle according to the first relative angle and the absolute angle comprises:
summing the first relative angle and the absolute angle to obtain a sum value;
performing remainder calculation processing on the summation value and 360 degrees;
and acquiring the azimuth angle of the antenna according to the remainder.
An artificial intelligence-based antenna azimuth angle measurement apparatus according to an embodiment of a second aspect of the present application includes:
the image acquisition module is used for acquiring an aerial overlook image shot by the camera; the position of the camera is higher than the highest height of the top end of the antenna to be detected, the camera is arranged on the flying device, and the aerial overlook image comprises an image of the top plane of the antenna;
the image recognition module is used for recognizing the antenna overlook image according to a preset image recognition model to obtain an antenna outline; wherein the antenna profile comprises a profile of an antenna top plane;
the outline identification module is used for identifying the outline of the plane at the top of the antenna to obtain the normal direction of the antenna;
the relative angle acquisition module is used for acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotation direction; the first relative angle is a horizontal included angle between a camera facing line and a normal direction line according to the rotation direction;
the absolute angle acquisition module is used for acquiring the absolute angle of the orientation of the camera according to the rotation direction and a first preset direction; the absolute angle is a horizontal included angle between a first preset direction line and a camera orientation line according to the rotating direction;
and the azimuth angle acquisition module is used for acquiring the antenna azimuth angle according to the first relative angle and the absolute angle.
According to this application embodiment's antenna azimuth angle measuring device based on artificial intelligence, have following beneficial effect at least: the antenna azimuth angle measuring device based on artificial intelligence can execute the antenna azimuth angle measuring method based on artificial intelligence in the first aspect, firstly, an antenna overlook image shot by a camera is obtained, then the antenna overlook image is identified according to a preset image identification model, an antenna profile is obtained, the antenna profile comprises a profile of a top plane of an antenna, the image identification model can accurately identify the profile of the top plane of the antenna, and a favorable basis is provided for subsequent determination of a finding direction; then, identifying the outline of the top plane of the antenna to obtain the normal direction of the antenna; thereafter, acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotating direction, and acquiring an absolute angle of the orientation of the camera according to a first preset direction and the rotating direction; and finally, accurately acquiring the azimuth angle of the antenna according to the first relative angle and the absolute angle. Therefore, the artificial intelligence-based antenna azimuth angle measurement device in the embodiment of the application is used for executing the artificial intelligence-based antenna azimuth angle measurement method in the embodiment of the first aspect, can quickly measure the antenna direction angle, has high measurement efficiency, does not need to adopt more tools for assistance, and has high accuracy.
An artificial intelligence based antenna azimuth angle measurement system according to an embodiment of a third aspect of the present application includes:
at least one memory;
at least one processor;
at least one program;
the programs are stored in the memory, and the processor executes at least one of the programs to implement the artificial intelligence based antenna azimuth angle measurement method as an embodiment of the first aspect of the present application.
According to a fourth aspect of the present application, there is provided a computer-readable storage medium storing computer-executable instructions for causing a computer to perform an artificial intelligence-based antenna azimuth angle measurement method as defined in the first aspect of the present application.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The present application is further described with reference to the following figures and examples, in which:
FIG. 1 is a schematic flow chart of an artificial intelligence based antenna azimuth measurement method according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating recognition of an image recognition model according to an embodiment of the present application;
FIG. 3 is a schematic diagram of the top planar profile of an antenna provided by one embodiment of the present application;
FIG. 4 is a schematic view of the normal direction identification provided by an embodiment of the present application;
fig. 5 is a schematic diagram illustrating an antenna azimuth acquisition according to an embodiment of the present application;
fig. 6 is a schematic diagram of obtaining an azimuth angle of an antenna according to another embodiment of the present application;
fig. 7 is a schematic diagram of obtaining an azimuth angle of an antenna according to another embodiment of the present application;
FIG. 8 is a schematic illustration of obtaining an overhead image and identifying an antenna profile as provided by one embodiment of the present application;
FIG. 9 is a schematic connection diagram of an artificial intelligence based antenna azimuth angle measurement apparatus according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of an artificial intelligence based antenna azimuth angle measurement system according to an embodiment of the present application.
Reference numerals:
the antenna comprises an antenna 100, a coincidence region 200, an outline 300 of a top plane of the antenna, an image acquisition module 400, an image recognition module 410, an outline recognition module 420, a relative angle acquisition module 430, an absolute angle acquisition module 440, an azimuth angle acquisition module 450, a memory 500, and a processor 600.
Detailed Description
Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
It should be noted that although functional block divisions are provided in the system drawings and logical orders are shown in the flowcharts, in some cases, the steps shown and described may be performed in different orders than the block divisions in the systems or in the flowcharts. The terms etc. in the description and claims and the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In the description of the present application, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present number, and the above, below, within, etc. are understood as including the present number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present application, unless otherwise expressly limited, terms such as set, mounted, connected and the like should be construed broadly, and those skilled in the art can reasonably determine the specific meaning of the terms in the present application by combining the detailed contents of the technical solutions.
In the description of the present application, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The azimuth angle of the antenna can be understood as the angle between the antenna facing and the preset direction. For example, the antenna azimuth angle is the angle between the antenna orientation and the true north direction, and the angle is the angle that the true north direction experiences when rotated clockwise to and coincident with the antenna orientation.
Convolutional Neural Networks (CNN) are a class of feed forward Neural Networks (fed forward Neural Networks) that contain convolution computations and have a deep structure, and are one of the representative algorithms for deep learning (deep learning). Convolutional Neural Networks have a feature learning (rendering) capability, and can perform Shift-Invariant classification (Shift-Invariant classification) on input information according to a hierarchical structure thereof, and are therefore also called "Shift-Invariant Artificial Neural Networks (SIANN)".
In the related art, one method for measuring the azimuth angle of the antenna adopts a visual antenna orientation mode, and the north arrow is used for measuring according to the visual antenna orientation, so that the measuring mode is rough and the precision is poor. Another method for measuring the azimuth angle of the antenna needs to manually climb onto the antenna, attach the sensor to the plane of the top of the antenna, enable the detection surface of the sensor and the plane of the top of the antenna to be in the same plane, and then measure the azimuth angle of the antenna. The operation is inconvenient, and certain potential safety hazards exist.
An artificial intelligence-based antenna azimuth angle measurement method according to an embodiment of the present application is described below with reference to fig. 1.
As shown in fig. 1, the artificial intelligence based antenna azimuth angle measurement method includes:
step S100, acquiring an aerial overlook image shot by a camera; the position of the camera is higher than the highest height of the top end of the antenna 100 to be measured, the camera is arranged on the flying device, and an antenna overlook image comprises an image of the top plane of the antenna;
step S110, identifying an antenna overlook image according to a preset image identification model to obtain an antenna outline; the antenna outline 300 comprises an outline 300 of an antenna top plane, and a model image recognition model is constructed on the basis of a convolutional neural network;
step S120, identifying the outline 300 of the top plane of the antenna to obtain the normal direction of the antenna 100;
step S130, acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotation direction; the first relative angle is a horizontal included angle between the camera facing line and the normal direction line according to the rotating direction;
step S140, acquiring the absolute angle of the orientation of the camera according to the rotating direction and a first preset direction; the absolute angle is a horizontal included angle between a first preset direction line and a camera orientation line according to the rotation direction;
and S150, acquiring an antenna azimuth angle according to the first relative angle and the absolute angle.
Firstly, an aerial overlook image shot by a camera is obtained, then the aerial overlook image is identified according to a preset image identification model, so that the outline 300 of the aerial top plane is obtained, the outline 300 of the aerial top plane can be accurately identified by the model image identification model, and a favorable basis is provided for the subsequent determination of the finding direction; then, recognizing the outline 300 of the top plane of the antenna to obtain the normal direction of the antenna 100, and adopting an antenna normal direction algorithm to quickly and accurately calculate and obtain the normal direction of the antenna 100; thereafter, acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotating direction, and acquiring an absolute angle of the orientation of the camera according to a first preset direction and the rotating direction; and finally, accurately acquiring the azimuth angle of the antenna according to the first relative angle and the absolute angle. Therefore, the antenna azimuth angle measurement method based on artificial intelligence recognition can quickly measure the antenna azimuth angle, is high in measurement efficiency, does not need to be assisted by more tools, and is high in accuracy.
It can be understood that, as shown in fig. 2, acquiring an aerial overhead view image taken by a camera includes:
the center of the image in the camera is set in the back area of the antenna 100;
and acquiring an aerial overlook image shot by a camera of the flying device according to the back area.
It can be understood that the center of the image is the center of the cross, and the position of the cross of the camera is determined by the set rule, so that a basis is provided for subsequent identification and calculation of the normal direction of the antenna 100. Specifically, as shown in fig. 2, the cross in the camera may be a cross, where the center of the cross is the center of the image, and the cross is used to place the center of the image in the back area of the antenna 100, and further obtain an antenna overhead image of the center of the image in the back area of the antenna 100. Further, the cross in the camera may also be a cross line, the center of the cross line is the center of the image, the cross line is used to place the center of the image in the back area of the antenna 100, and an antenna top view image with the center of the image in the back area of the antenna 100 may also be acquired.
It will be appreciated that if the antenna is viewed from above as a single antenna 100, as shown in fig. 2, the cross in the camera need only be placed in the back area of the antenna 100.
It is understood that, as shown in fig. 2, if the antenna top view image includes a plurality of antennas 100, an overlapping area 200 is formed in the radiation area of the back surfaces of the plurality of antennas 100; the overlapping area 200 is a regular hexagon. The cross in the center of the image in the camera may be anywhere in the overlap area 200, e.g., the cross is located as shown in fig. 2. The selection of the overlap region 200 provides a basis for subsequent acquisition of the normal direction of the antenna 100.
It will be appreciated that the profile 300 of the antenna top plane is several acquisition points, as shown in fig. 3. The outline 300 of the antenna top plane is obtained by recognizing the antenna top plane image according to a preset image recognition model, wherein the image recognition model is a convolutional neural network model (CNN), and the outline 300 of the antenna top plane can be recognized accurately by training the type, length, width, height and color of the antenna 100 and the change of the antenna 100 under different illumination conditions. Specifically, the outline 300 of the top plane of the antenna is a quasi-rectangle surrounded by a plurality of acquisition points; the acquisition points are calculated in an array mode. The image recognition model obtains a quasi-rectangle surrounded by a plurality of acquisition points by recognizing an aerial overlook image, and the acquisition points can be adjusted through setting, so that the method is convenient and flexible.
It will be appreciated that identifying the profile 300 of the top plane of the antenna, as shown in fig. 3 and 4, results in the normal direction of the antenna 100, including:
fitting a rectangle according to a preset antenna normal direction algorithm and the outline 300 of the antenna top plane;
obtaining two longest sides according to the rectangle;
obtaining the normal directions of the two longest sides according to the middle points of the two longest sides; wherein, the normal directions of the two longest sides respectively use the respective middle points as starting points to be far away from the rectangle.
And fitting a rectangle meeting the requirements, and further obtaining the normal directions of the two longest sides according to the rectangle, so as to provide a basis for subsequently confirming the normal directions of the antenna.
It can be understood that, as shown in fig. 3 and 4, identifying the profile 300 of the antenna top plane, obtaining the normal direction of the antenna 100, further includes:
establishing a rectangular coordinate system by taking the center of the image as an origin;
obtaining quadrants to which the normal directions of the two longest sides belong;
the normal direction of the antenna 100 is obtained from the quadrants to which the normal directions of the two longest sides belong.
By the method, the normal direction of the antenna 100 can be accurately identified, and the interference formed by the longest edge on the back side of the antenna is effectively eliminated.
It will be appreciated that the antenna normal direction algorithm is a mathematical algorithm that fits a straight line, e.g., y kx + b, to the acquisition points on one side of the profile 300 of the antenna top plane, which is the straight line that passes the most through the acquisition points.
It can be understood that, as shown in fig. 3 and 4, first, a rectangle is fitted according to the obtained outline 300 of the antenna top plane by using an antenna normal direction algorithm, wherein the antenna normal direction algorithm is used for fitting the rectangle according to the outline 300 of the antenna top plane; then, according to the rectangle, obtaining two longest sides of the rectangle, namely the length in the length and the width; then, identifying the middle point of the longest side according to the two longest sides, and respectively drawing two normal directions in opposite directions by taking the middle point as a starting point; then, a rectangular coordinate system is established with the cross center as the origin of coordinates according to the defined image center, i.e., the cross center, of the previously photographed antenna top view image, and the quadrant to which the normal directions of the two line segments belong is identified, and at this time, the correct normal direction of the antenna 100 can be determined according to the quadrant to which the normal directions of the two line segments belong.
It will be appreciated that acquiring a first relative angle between the camera orientation and the normal direction, as shown in fig. 5, based on the rotational direction, includes:
according to the rotation direction and a second preset direction, a second opposite angle towards which the camera faces and a third opposite angle towards the normal direction are obtained; the second opposite angle is a horizontal included angle between a second preset direction line and a camera facing line according to the rotating direction, and the third opposite angle is a horizontal included angle between the second preset direction line and a normal direction line according to the rotating direction;
and acquiring a first relative angle according to the second relative angle and the third relative angle.
It is understood that the direction of rotation is clockwise; the second preset direction line is a second opposite angle according to a horizontal included angle between the clockwise direction and the camera facing line, and the second preset direction line is a third opposite angle according to an angle between the clockwise direction and the normal direction line. The first relative angle can be read through the second preset direction, and the scheme is simple and easy to realize. Specifically, as shown in fig. 5, the vertical direction in the overhead image of the antenna is set to be a second preset direction and also to be a 0-degree direction, a horizontal angle between a 0-degree direction line from the clockwise direction to the facing line of the camera is a second opposite angle, a horizontal angle between the 0-degree direction line from the clockwise direction to the normal direction line is a third opposite angle, and the second opposite angle and the third opposite angle are angles relative to the 0-degree direction.
It can be appreciated that as shown in fig. 5, when the aerial top view image is taken, we already know the camera orientation, so that from the 0 degree direction line to the camera orientation line, a second relative angle of 90 degrees can be obtained; in addition, the third diagonal is also read according to the same method.
It is understood that the first predetermined direction is a north direction;
according to direction of rotation and first preset direction, obtain the absolute angle of camera orientation, include:
detecting to obtain the true north direction according to a gyroscope of the flight device and a preset geographic coordinate system;
acquiring the absolute angle of the orientation of the camera according to the north; the absolute angle is a horizontal included angle between a positive north direction line and a camera orientation line according to the rotation direction.
The setting of the due north direction is a first preset direction, the due north direction can be effectively detected through the gyroscope of the unmanned aerial vehicle and a preset geographic coordinate system, and then the horizontal included angle between the due north direction line and the camera orientation line, namely the absolute angle, is quickly read.
It is understood that acquiring the antenna azimuth angle from the first relative angle and the absolute angle includes:
summing the first relative angle and the absolute angle to obtain a sum value;
performing remainder calculation processing on the summation value and 360 degrees;
and acquiring the azimuth angle of the antenna according to the remainder.
The first relative angle and the absolute angle are simply calculated to obtain the azimuth angle of the antenna, the scheme is simple, the implementation is convenient, and meanwhile, the influence caused by the angle larger than 360 degrees is removed through the calculation method.
It will be appreciated that the antenna is viewed from above as shown in figure 5. Firstly, according to the reading rule of the angle, that is, the horizontal included angle between the 0-degree direction line and the corresponding direction line in the clockwise direction is known, and according to the obtained normal direction, as shown in fig. 5, the normal direction is defined as 30 degrees relative to the 0-degree direction, the orientation of the camera of the unmanned aerial vehicle is 90 degrees relative to the 0-degree direction, and the reading is performed according to the horizontal included angle between the 0-degree direction line and the corresponding direction line in the clockwise direction, that is, the orientation of the camera of the unmanned aerial vehicle rotates 300 degrees to coincide with the normal direction, so that the relative angle between the orientation of the camera and the normal direction is 300 degrees; then, detecting the actual due north direction according to the gyroscope of the flight device and a preset geographic coordinate system, as shown in fig. 5, if the detected due north direction is the same as the 0-degree direction, then the horizontal included angle between the due north direction and the orientation of the camera, that is, the absolute angle is 90 degrees; finally, by calculation, the 300 degrees is added to the 90 degrees to obtain 390 degrees, and remainder processing is performed on the 390 degrees and the 360 degrees to obtain 30 degrees, so that the azimuth angle of the antenna shown in fig. 5 is 30 degrees.
It will be appreciated that the antenna is viewed from above as shown in figure 6. First, as shown in fig. 6, the normal direction is defined as 30 degrees (i.e. the horizontal included angle between the 0-degree direction line and the normal direction line is 30 degrees), the camera orientation of the unmanned aerial vehicle is 90 degrees (i.e. the horizontal included angle between the 0-degree direction line and the camera orientation line is 90 degrees), and at this time, the relative angle between the camera orientation and the normal direction is 300 degrees (i.e. the horizontal included angle between the camera orientation line and the normal direction line is 300 degrees); then, detecting an actual due north direction according to a gyroscope of the unmanned aerial vehicle and a preset geographic coordinate system, as shown in fig. 6, if the detected due north direction is the same as the normal direction, a horizontal included angle between the due north direction and the orientation of the camera, that is, an absolute angle is 60 degrees; finally, by calculation, the 300 degrees is added with 60 degrees to obtain 360 degrees, and remainder calculation processing is performed on 360 degrees and 360 degrees to obtain 0 degree, so that the azimuth angle of the antenna shown in fig. 6 is 0 degree.
It will be appreciated that the antenna is viewed from above as shown in figure 7. First, as shown in fig. 7, the normal direction is defined as 135 degrees (i.e., a horizontal included angle between a 0-degree direction line and a camera orientation line is 135 degrees), the camera orientation of the unmanned aerial vehicle is 90 degrees (i.e., a horizontal included angle between a 0-degree direction line and a camera orientation line is 90 degrees), and then a relative angle between the camera orientation and the normal direction is 45 degrees (i.e., a horizontal included angle between a clockwise direction and a normal direction line is 45 degrees); then, detecting an actual due north direction according to a gyroscope of the unmanned aerial vehicle and a preset geographic coordinate system, as shown in fig. 7, if the detected due north direction is the same as the 0-degree direction, a horizontal included angle between the due north direction and the orientation of the camera, that is, an absolute angle is 90 degrees; finally, by calculation, the 45 degrees is added with the 90 degrees to obtain 135 degrees, and the remainder processing is performed on the 135 degrees and the 360 degrees to obtain 135 degrees, so that the azimuth angle of the antenna shown in fig. 7 is 135 degrees.
An artificial intelligence-based antenna azimuth angle measurement apparatus according to an embodiment of the present application is described below with reference to fig. 9.
As shown in fig. 9, the antenna azimuth angle measuring apparatus based on artificial intelligence includes:
an image obtaining module 400, configured to obtain an antenna overhead image captured by a camera; the position of the camera is higher than the highest height of the top end of the antenna 100 to be measured, the camera is arranged on the flying device, and an antenna overlook image comprises an image of the top plane of the antenna;
the image recognition module 410 is used for recognizing an antenna overlook image according to a preset image recognition model to obtain an antenna profile; the antenna outline 300 comprises an outline 300 of an antenna top plane, and a model image recognition model is constructed on the basis of a convolutional neural network;
the profile identification module 420 is used for identifying the profile 300 of the antenna top plane to obtain the normal direction of the antenna 100;
a relative angle obtaining module 430, configured to obtain a first relative angle between the camera orientation and a normal direction according to the rotation direction; the first relative angle is a horizontal included angle between the camera facing line and the normal direction line according to the rotating direction;
an absolute angle obtaining module 440, configured to obtain an absolute angle towards which the camera faces according to the rotation direction and a first preset direction; the absolute angle is a horizontal included angle between a first preset direction line and a camera orientation line according to the rotation direction;
an azimuth obtaining module 450, configured to obtain an antenna azimuth according to the first relative angle and the absolute angle.
The antenna azimuth angle measuring device based on artificial intelligence can execute the antenna azimuth angle measuring method based on artificial intelligence, firstly, an antenna overlook image shot by a camera is obtained, then the antenna overlook image is identified according to a preset image identification model, the outline 300 of the antenna top plane is obtained, the model image identification model can accurately identify the outline 300 of the antenna top plane, and a favorable basis is provided for subsequent determination of finding direction; then, recognizing the outline 300 of the top plane of the antenna to obtain the normal direction of the antenna 100, and adopting an antenna normal direction algorithm to quickly and accurately calculate and obtain the normal direction of the antenna 100; thereafter, acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotating direction, and acquiring an absolute angle of the orientation of the camera according to a first preset direction and the rotating direction; and finally, accurately acquiring the azimuth angle of the antenna according to the first relative angle and the absolute angle. Therefore, the antenna azimuth angle measuring device based on artificial intelligence in the embodiment of the application is used for executing the antenna azimuth angle measuring method based on artificial intelligence in the embodiment, the antenna azimuth angle can be rapidly measured, the measuring efficiency is high, more tools are not needed for assistance, and the accuracy is high.
It will be appreciated that a drone may be provided above the antenna 100 for taking aerial overhead images from the camera. Specifically, during measurement, the position of the camera for shooting and framing requires the highest height higher than the top end of the antenna 100 to be measured, and more accurate images of the top plane of the antenna can be acquired.
It can be understood that the method and apparatus for measuring the azimuth angle of an Antenna based on artificial intelligence in the embodiments of the present application can be applied to the measurement of 2G, 3G, and 4G base station antennas, and can also be applied to the measurement of 5G base station AAU (Active Antenna Unit).
It can be understood that, as shown in fig. 8, when the drone is located on the top of the antenna, the height of the camera of the drone is higher than the height of the plane of the top of the antenna, and at this time, the overlook image is shot, so that the outline of the antenna 100 can be identified more effectively.
An artificial intelligence based antenna azimuth angle measurement system according to an embodiment of the present application is described below with reference to fig. 10.
As shown in fig. 10, the artificial intelligence based antenna azimuth angle measurement system according to the embodiment of the present application may be any type of intelligent terminal, such as a mobile phone, a tablet computer, a personal computer, and the like.
It can be understood that the antenna azimuth angle measuring method based on artificial intelligence can shoot the antenna overlooking image by operating the unmanned aerial vehicle through the mobile phone, the unmanned aerial vehicle transmits the antenna overlooking image back to the mobile phone, and the mobile phone uploads the antenna overlooking image to the cloud for data processing.
Specifically, an artificial intelligence based antenna azimuth angle measurement system includes:
at least one memory 500;
at least one processor 600;
at least one program;
a program is stored in the memory 500 and the processor 600 executes at least one program to implement the artificial intelligence based antenna azimuth angle measurement method described above. Fig. 10 illustrates an example of a processor 600.
The processor 600 and the memory 500 may be connected by a bus or other means, and fig. 10 illustrates a connection by a bus as an example.
The memory 500, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and signals, such as program instructions/signals corresponding to the artificial intelligence based antenna azimuth angle measurement system in the embodiments of the present application. The processor 600 executes various functional applications and data processing, i.e., implementing the artificial intelligence based antenna azimuth angle measurement method of the above-described method embodiments, by executing non-transitory software programs, instructions and signals stored in the memory 500.
The memory 500 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area can store the related data of the artificial intelligence-based antenna azimuth angle measurement method and the like. Further, the memory 500 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 500 optionally includes memory located remotely from processor 600, which may be connected to the artificial intelligence based antenna azimuth angle measurement system via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more signals are stored in the memory 500 and, when executed by the one or more processors 600, perform the artificial intelligence based antenna azimuth angle measurement method in any of the method embodiments described above. For example, the above-described method steps S100 to S150 in fig. 1 are performed.
A computer-readable storage medium according to an embodiment of the present application is described below with reference to fig. 10.
As shown in fig. 10, a computer-readable storage medium stores computer-executable instructions that, when executed by one or more processors 600, e.g., by one of processors 600 in fig. 3, may cause the one or more processors 600 to perform the artificial intelligence based antenna azimuth measurement method in the method embodiments described above. For example, the above-described method steps S100 to S150 in fig. 1 are performed.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
From the above description of embodiments, those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media and communication media. The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. In addition, communication media typically embodies computer readable signals, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The embodiments of the present application have been described in detail with reference to the drawings, but the present application is not limited to the embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present application. Furthermore, the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
Claims (10)
1. An antenna azimuth angle measurement method based on artificial intelligence is characterized by comprising the following steps:
acquiring an aerial overlook image shot by a camera; the position of the camera is higher than the highest height of the top end of the antenna to be detected, the camera is arranged on the flying device, and the aerial overlook image comprises an image of the top plane of the antenna;
identifying the antenna overlook image according to a preset image identification model to obtain an antenna profile; wherein the antenna profile comprises a profile of an antenna top plane;
identifying the outline of the top plane of the antenna to obtain the normal direction of the antenna;
acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotation direction; the first relative angle is a horizontal included angle between a camera facing line and a normal direction line according to the rotation direction;
acquiring the absolute angle of the orientation of the camera according to the rotation direction and a first preset direction; the absolute angle is a horizontal included angle between a first preset direction line and a camera orientation line according to the rotating direction;
and acquiring the azimuth angle of the antenna according to the first relative angle and the absolute angle.
2. The artificial intelligence based antenna azimuth angle measurement method according to claim 1, wherein the acquiring of the aerial top view image taken by the camera comprises:
setting the image center in the camera in the back area of the antenna;
and acquiring an aerial overlook image shot according to the camera of the flying device according to the back area.
3. The artificial intelligence based antenna azimuth angle measurement method of claim 2, wherein the identifying the profile of the antenna top plane, obtaining the normal direction of the antenna, comprises:
identifying the outline of the antenna top plane according to a preset antenna normal direction algorithm, and fitting a rectangle of the antenna top plane;
obtaining two longest sides according to the rectangle;
obtaining the normal directions of the two longest sides according to the middle points of the two longest sides; and the normal directions of the two longest sides are directions which are far away from the rectangle by taking the middle point as a starting point.
4. The artificial intelligence based antenna azimuth angle measurement method of claim 3, wherein the identifying the profile of the antenna top plane, resulting in a normal direction of the antenna, further comprises:
establishing a rectangular coordinate system by taking the image center as an origin;
obtaining quadrants to which the normal directions of the two longest sides belong;
and obtaining the normal direction of the antenna according to the quadrant to which the normal directions of the two longest sides belong.
5. The artificial intelligence based antenna azimuth angle measurement method of claim 1, wherein the acquiring a first relative angle between the camera orientation and the normal direction according to a rotation direction comprises:
according to the rotation direction and a second preset direction, a second opposite angle of the orientation of the camera and a third opposite angle of the normal direction phase are obtained; the second opposite angle is a horizontal included angle between a second preset direction line and the camera facing line according to the rotating direction, and the third opposite angle is a horizontal included angle between the second preset direction line and the normal direction line according to the rotating direction;
and acquiring the first relative angle according to the second relative angle and the third relative angle.
6. The artificial intelligence based antenna azimuth angle measurement method of claim 2, wherein the first preset direction is a true north direction;
the obtaining the absolute angle of the orientation of the camera according to the rotation direction and the first preset direction comprises:
detecting to obtain the true north direction according to the gyroscope of the flight device and a preset geographic coordinate system;
acquiring the absolute angle of the orientation of the camera according to the north; and the absolute angle is a horizontal included angle between a north direction line and the camera orientation line according to the rotation direction.
7. The artificial intelligence based antenna azimuth angle measurement method of claim 1, wherein the acquiring the antenna azimuth angle from the first relative angle and the absolute angle comprises:
summing the first relative angle and the absolute angle to obtain a sum value;
performing remainder calculation processing on the summation value and 360 degrees;
and acquiring the azimuth angle of the antenna according to the remainder.
8. Antenna azimuth angle measuring device based on artificial intelligence, its characterized in that includes:
the image acquisition module is used for acquiring an aerial overlook image shot by the camera; the position of the camera is higher than the highest height of the top end of the antenna to be detected, the camera is arranged on the flying device, and the aerial overlook image comprises an image of the top plane of the antenna;
the image recognition module is used for recognizing the antenna overlook image according to a preset image recognition model to obtain an antenna outline; wherein the antenna profile comprises a profile of an antenna top plane;
the outline identification module is used for identifying the outline of the plane at the top of the antenna to obtain the normal direction of the antenna;
the relative angle acquisition module is used for acquiring a first relative angle between the orientation of the camera and the normal direction according to the rotation direction; the first relative angle is a horizontal included angle between a camera facing line and a normal direction line according to the rotation direction;
the absolute angle acquisition module is used for acquiring the absolute angle of the orientation of the camera according to the rotation direction and a first preset direction; the absolute angle is a horizontal included angle between a first preset direction line and a camera orientation line according to the rotating direction;
and the azimuth angle acquisition module is used for acquiring the antenna azimuth angle according to the first relative angle and the absolute angle.
9. Antenna azimuth angle measurement system based on artificial intelligence, its characterized in that includes:
at least one memory;
at least one processor;
at least one program;
stored in the memory, the processor executes at least one of the programs to implement the artificial intelligence based antenna azimuth angle measurement method of any one of claims 1 to 7.
10. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the artificial intelligence based antenna azimuth angle measurement method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116052003A (en) * | 2023-02-07 | 2023-05-02 | 中科星图数字地球合肥有限公司 | Method and device for measuring antenna angle information and related equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106705931A (en) * | 2015-11-16 | 2017-05-24 | 中国移动通信集团公司 | Method, device and system for automatically obtaining azimuth angle of base station antenna |
| CN107830846A (en) * | 2017-09-30 | 2018-03-23 | 杭州艾航科技有限公司 | One kind utilizes unmanned plane and convolutional neural networks measurement communication tower aerial angle method |
| CN105556742B (en) * | 2014-05-27 | 2018-05-11 | 华为技术有限公司 | The acquisition methods and equipment and system of antenna works parameter |
| CN110896331A (en) * | 2018-09-13 | 2020-03-20 | 中兴通讯股份有限公司 | Method and device for measuring antenna engineering parameters |
| CN111461014A (en) * | 2020-04-01 | 2020-07-28 | 西安电子科技大学 | Antenna attitude parameter detection method and device based on deep learning and storage medium |
| US20210049782A1 (en) * | 2019-08-13 | 2021-02-18 | Wuyi University | Method and system of antenna measurement for mobile communication base station |
-
2021
- 2021-05-28 CN CN202110592340.8A patent/CN113390386A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105556742B (en) * | 2014-05-27 | 2018-05-11 | 华为技术有限公司 | The acquisition methods and equipment and system of antenna works parameter |
| CN106705931A (en) * | 2015-11-16 | 2017-05-24 | 中国移动通信集团公司 | Method, device and system for automatically obtaining azimuth angle of base station antenna |
| CN107830846A (en) * | 2017-09-30 | 2018-03-23 | 杭州艾航科技有限公司 | One kind utilizes unmanned plane and convolutional neural networks measurement communication tower aerial angle method |
| CN110896331A (en) * | 2018-09-13 | 2020-03-20 | 中兴通讯股份有限公司 | Method and device for measuring antenna engineering parameters |
| US20210049782A1 (en) * | 2019-08-13 | 2021-02-18 | Wuyi University | Method and system of antenna measurement for mobile communication base station |
| CN111461014A (en) * | 2020-04-01 | 2020-07-28 | 西安电子科技大学 | Antenna attitude parameter detection method and device based on deep learning and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 周密: "基于无人机航拍图像的天线姿态测量", 《中国优秀硕士学位论文全文数据库（电子期刊）》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116052003A (en) * | 2023-02-07 | 2023-05-02 | 中科星图数字地球合肥有限公司 | Method and device for measuring antenna angle information and related equipment |
| CN116052003B (en) * | 2023-02-07 | 2024-05-14 | 中科星图数字地球合肥有限公司 | Method and device for measuring antenna angle information and related equipment |
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