KR102262382B1 - large area space information system for image acquistion base Unmanned Aerial Vehicle - Google Patents

Abstract

The present invention relates to a large area space information building system for image acquisition based on an unmanned aerial vehicle which comprises: an unmanned aerial vehicle that flies in the sky and photographs an image of a crop growing region; an image processing computer that processes the image through processing of the image photographed by the unmanned aerial vehicle; and a management server for integrally managing all data related to the image processed from the image processing computer.

Description

Large area space information system for image acquisition base Unmanned Aerial Vehicle}

The present invention relates to a system for constructing wide-area spatial information for image acquisition based on an unmanned aerial vehicle, and more particularly, it is possible to process and process aerial image data for a large-scale crop cultivation target area for integrated operation management and to improve the usefulness and versatility of data It is about a wide area spatial information construction system of UAV-based image acquisition.

In general, crops are grown in a vast, large-scale area, and since the area for cultivation of these crops is so wide and vast, all aspects related to crop cultivation, such as the type of crop grown in the cultivation target, growth development status, and degree of disease and pest infection The data has been conducted as a full-scale survey in which investigators visit and investigate the field cultivation sites on a daily basis.

However, this total survey method takes considerable time and effort to complete the survey period as the area of the target area for crop cultivation is wider, and as a result, labor costs increase, and the crop cultivation target area may be changed or replaced with other crops. The inconvenience of having to conduct a full investigation again has always been pointed out.

As a way to solve this problem, we started to collect and secure various data related to crop cultivation based on aerial images of crops using drones. There is a limit to management, and above all, there is a problem in that the system necessary for the establishment of the space required for the management and operation of all data is not prepared, so there is a problem of being managed and operated in a constant fashion.

Moreover, since the data collected and secured from drones related to crop cultivation show a big difference from the data of the total survey surveyed from the actual field crop cultivation site, the overall data collected from these drones cannot be used as useful data. .

The present invention for solving the above-mentioned problems effectively integrates and operates data that can be collected from large-scale crop cultivation sites, improves the usability of data, and enables the processing and processing of data collected from unmanned aerial vehicles to be used for actual field crop cultivation. The purpose of this study is to provide a wide area spatial information construction system for image acquisition based on an unmanned aerial vehicle that can be matched with the data of the total survey surveyed from the target site.

The present invention for achieving the above objects is an unmanned aerial vehicle that shoots an image of a crop cultivation target while flying in the sky, an image processing computer that processes the image through processing of the image photographed from the unmanned aerial vehicle, and the image processing computer There is one feature in a wide area spatial information construction system for image acquisition based on an unmanned aerial vehicle including a management server that integrates and manages all data related to images processed from .

UAV-based image acquisition wide area space further comprising a control controller for wireless flight control of the unmanned aerial vehicle, and a portable terminal that receives a sensing signal transmitted from the unmanned aerial vehicle to achieve a flight parallel to the ground slope on which the unmanned aerial vehicle flies An information building system has one characteristic.

Drone operation software for operating the setting information necessary for the flight plan of the unmanned aerial vehicle is installed in the management server, and the image processing computer calculates and generates general reading results of crops that can be compared with the field cultivation target of crops. There is one feature in the wide area spatial information construction system for image acquisition based on an unmanned aerial vehicle in which an intelligence-equipped program is installed.

The image processing computer uses a combination of cvtColor() function, threshold() function, morphologyEx() function, and GaussianBlur() function for image preprocessing. The spatial information construction system has one characteristic.

The imaging camera or 3D scanner mounted on the UAV has a feature in the UAV-based image acquisition wide area spatial information construction system in which a reach is further connected for positioning necessary for center meter-accurate PPK mapping.

The drone further includes an altitude change response device that allows it to fly while maintaining an altitude difference parallel to the slope of the ground, and a weather observation device that can observe and predict the weather in the sky. The system has one feature.

The detection signal of the detection sensor that detects the return time difference information of the laser reflected and returned from the light receiving unit of the altitude change response device is transmitted to the portable terminal to achieve the flight of the unmanned aerial vehicle parallel to the slope of the ground. Light of image acquisition based on an unmanned aerial vehicle There is one feature in the local geospatial information construction system.

The meteorological observation device is characterized in a wide area spatial information construction system of image acquisition based on an unmanned aerial vehicle, which is an aerial weather radar composed of a combination of an ultra-high frequency transmitter, an ultra-high frequency transmitter, and a solid-state exciter.

There is a feature in the wide area spatial information construction system for image acquisition based on an unmanned aerial vehicle in which a digital servo motor is further installed between the lower end of the drone and the altitude change response device for the operation of the altitude change response device.

As described above, according to the present invention, it is possible to construct spatial information about crop cultivation farmland for a large area, and the constructed data data can be integrated and managed in one management server system, so that It is easy to grasp information on crop cultivation area, and it can prevent fluctuations in the price of crops, and it can be shared and used as useful data in various sales fields such as industry, forestry, and urban planning.

In addition, as it is possible to computerize and urbanize the registration of agricultural businesses through the efficient operation of on-site drones in large areas, the efficiency of business processing for measurement of cultivated area and identification of illegal temporary buildings based on spatial information construction data and increase accuracy.

Moreover, as it is managed integratedly in one management server system, it can be easy to find out illegally registered farmhouses according to the easy grasp of all information on the crop cultivation area nationwide, and it is possible to make an accurate judgment on the target of direct payment and transparency in budget execution. can also be obtained.

1 is a view showing, as an example, the processing process of settings necessary for flight plan establishment through the drone operation software mounted on the management server in the present invention;
2 is a view showing an example of a process for shooting a drone in the present invention;
3 is a view showing an image processing process through an image processing computer in the present invention as an example;
4 is a view showing an image processing process through an image processing computer in the present invention as another example;
5 is a view for showing the sample image results taken from the drone in the present invention as an example;
Figure 6 is a view for showing as an example the primary and secondary orthographic images of the target site taken from the drone in the present invention,
7 is a diagram for an example of comparing overlapping photos between adjacent paths of a target site taken from a drone in the present invention;
8 is a view for showing the overlap of the primary and secondary shooting subject diagrams of the target site photographed from the drone in the present invention as an example;
9 is a view for showing an example of an automated processing process for crop reading and area calculation through an AI-equipped program in the present invention;
10 is a diagram illustrating an example of a crop reading result through an AI-equipped program in the present invention by outputting a crop number, a zone number, a boundary line, and the like;
11 is another example of a crop reading result through an AI-equipped program in the present invention, which is a diagram illustrating outputting information such as numbering, crop name, area, etc. according to the type of crop for a specific area of the crop target area;
12 is another example of the results of crop reading through the artificial intelligence-equipped program in the present invention, and is a diagram illustrating the output of overlapping information of orthographic images, crop reading, and total survey results;
13 is a view showing, as an example, a table comparing the results of crop reading based on the results of the crop reading through the artificial intelligence-equipped program in the present invention and the results of the total investigation;
14 is a view showing an elevation and enlarged image according to spatial resolution of an image of a crop target taken from an unmanned aerial vehicle in the present invention as an example;
15 is a view for conceptually illustrating the problem of the flight that is not parallel to the altitude of the unmanned aerial vehicle with the ground slope in the present invention, that is, a straight flight;
16 is a view conceptually illustrating a meteorological phenomenon that may occur in the process of flying the UAV in the present invention on the ground and in the sky;
17 is a view showing the configuration of the drone operation software mounted on the management server in the present invention as an example on the monitor screen;
18 is a diagram illustrating an image processing process of the drone operating software mounted on the management server in the present invention as an example.

The present invention should be interpreted in a way that includes the scope of rights that reach the technical idea through various modified embodiments, only these embodiments make the publication of the present invention complete, and common knowledge in the technical field to which the present invention belongs It is provided to fully inform the possessor of the scope of the invention, and the present invention is only defined by the scope of the claims.

Moreover, the drawings attached to the present invention are attached as a way to help the understanding of the description of the present invention to the last, and the technical spirit of the present invention should not be interpreted as being limited by the attached drawings.

The system for constructing wide area spatial information for image acquisition based on an unmanned aerial vehicle according to the present invention may be provided as a system consisting of an unmanned aerial vehicle, an image processing computer, and a management server, and further equipped with a control controller and a portable terminal for wireless flight control of the unmanned aerial vehicle The image data of the crop cultivation site transmitted from the unmanned aerial vehicle and the signal data necessary for the operation and control of the unmanned aerial vehicle may be transmitted to the image processing computer and the portable terminal, respectively, through the GPS. In particular, the image processing computer can use a workstation-class or higher-level computer capable of efficiently processing vast image data of crop cultivation sites photographed from an unmanned aerial vehicle.

In addition, the image processing computer can be installed and operated with an artificial intelligence-equipped program that can smartly process image data processing, and images taken from the unmanned aerial vehicle and image processing and verification of the field crop cultivation site. As a result, it can be managed in an integrated format in the management server by making a database of the results related to orthographic images, spatial information construction, and thematic map overlapping. Such an AI-equipped program will be described in more detail in a later process.

The unmanned aerial vehicle may use, for example, a drone, and may fly based on a flight plan to shoot the target while flying the crop cultivation target, that is, the crop shooting target, and in the case of a specific procedure for the flight plan, it is managed as shown in FIG. 1 Setting the shooting area through the server monitor -> Configuring a grid with a certain area standard (for example, based on the time available for one flight) -> Satellite photos such as Google Earth or Naver maps and Daum maps, field conditions and take-off and landing locations through aerial photos Confirmation (Examine the take-off and landing sites through the open map) -> Since the spatial resolution and update cycle of each open map are different, use the most useful video to establish a shooting plan -> Save the boundary and grid of the shooting site in kml format - > Load kml format file from drone operation software (eg eMotion3), execute flight simulation, and save as mission -> It can be done through the process of immediately executing flight by calling mission from the field.

Such a drone may be equipped with a photographing camera for obtaining image information on the target area of crops, and in some cases, a 3D scanner may be mounted and utilized, and the altitude change for maintaining parallel flight with the ground slope of the drone A corresponding device and a weather observation device for observing the weather in the sky may be further installed, and drone operation software may be installed in the management server.

In the case of drone photography as an unmanned aerial vehicle for crop cultivation sites, for example, as shown in Fig. 2, it can be done through the process of filming and application for flight permission -> drone photography -> image pre-processing -> image processing. And, of course, it can be performed between the application for a flight permit and drone photography.

The image pre-processing and image processing may be processed through an artificial intelligence-equipped program mounted on an image processing computer and stored in a useful data format, at this time, the artificial intelligence-equipped program interlocks with the drone operation software installed in the management server. can be Filming and flight permit applications can be submitted to the website server operated by the flight permitting agency through the form stored in the image processing computer.

In the image processing, the image processing may be performed in the order of Initial Processing -> point cloud and mesh -> DSM, Orthomosaic steps, for example, as shown in FIGS. 3 to 4, provided by the artificial intelligence-equipped program mounted on the image processing computer.

The higher the resolution, the lower the altitude, closer to the adjacent flight path, the longer the flight time can be, and the lower the resolution, the higher the altitude, and the farther away from the adjacent flight path, the higher the resolution is. , the flight time may be shortened. As the altitude increases, the wind strength becomes stronger, the ascent and descent time is excessive, and extreme battery consumption can occur. For this, reference may be made to FIG. 5 .

As a result of drone shooting for a target area of crops, for example, as shown in FIG. 6 , it may be obtained as a result of a first orthographic image and a DSM image, and a secondary shot orthographic image and a DSM image, and furthermore, a drone shooting result for the target area of a crop As an analysis, as shown in FIG. 7 , it can be analyzed through the comparison of overlapping photos between adjacent paths comparing the overlap of the primary photographing (excessive) and the second photographing (appropriate) with each other.

In other words, due to excessive redundancy setting during the 1st shooting, the shooting time increases and the number of photos is large. During the 2nd shooting, the overlap can be adjusted within the limit that does not interfere with the shooting result. 6,138 pictures taken 1st shot, 3,766 pictures taken 2nd shot Efficient shooting is possible according to the difference of about 2,400 shots per shot, and the accumulation of drone shooting experience.

On the other hand, as a method of superimposing an image on the subject map for on-site inspection and understanding of the current situation, for example, as shown in FIG. 8, the overlapping of the primary shooting subject and the overlapping of the secondary shooting subject may be referred to. It can be divided -> full on-site survey -> survey area method, and in division of the entire addressee area, large-scale satellite map printing can be checked with the naked eye, divided into survey function areas for 1 day, location tag with map number is attached can be

In the total crop survey on the target area of crops, for example, 21 crops can be investigated as the result of the total survey according to the first (summer) survey result schematically, and the total number of parcels is 1,090 parcels, and the lot with the largest number of parcels is the largest. is a rice field, the number of lots is 56.6%, the area ratio is 50.8%, and the place with the second largest number of lots may be red pepper (8.2%), and the place with the second largest area may be radish (10.7%).

As a result of the second (winter) full-scale survey, for example, 10 crops can be surveyed as a result of a full-scale survey according to the schematic, and there are many uncultivated land that has finished harvesting in season, with 76% of uncultivated land, and the second largest number of plots. Radishes (8.9%) were found in many places, while onions and garlic were in the early growth stage at the time of the survey, and cabbage and radishes were harvested in many places at the time of the total survey.

In other words, as a result of the first (summer) census, half of the paddy land area, the top 5 of the cultivated land number could be rice paddy, red pepper, sesame, ginseng, and watermelon in the order, and the top 5 of the cultivated area ratio were paddy, radish, watermelon, ginseng, It may be an onion order. And, as a result of the second (winter) census, the number of uncultivated land is large due to the harvest of paddy land, the top 5 of the cultivated land number are uncultivated land, radish, ginseng, bokbunja, and onion, and the top 5 of the cultivated land area ratio is uncultivated land, It can be radish, ginseng, onion, or Chinese cabbage.

On the other hand, the artificial intelligence-equipped program mounted on the image processing computer can perform the processes of artificial neural networks, deep learning, and machine learning, but in the case of an artificial neural network, information transfer of neurons and synapses constituting the human brain and It is a mathematical technique that models the processing process and gives it a learning function to have problem-solving ability. In the case of deep learning, it is a process of updating weights to find features by inputting vast amounts of data into a deep artificial neural network. In the case of machine learning, it is a process of inputting a large amount of data into an artificial neural network and updating the weights suitable for the purpose (image classification, etc.).

In particular, the artificial neural network technique is a configuration that can be used in a specific field, and the results may vary depending on the selection of the artificial neural network. In the present invention, the artificial neural network can be configured as a neural network suitable for the optimal crop reading technique.

As an example of an artificial neural network suitable for the optimal crop reading technique, Feed Forward Neural Network (FFNN), Convolutional Neural Network (CNN), Convolutional Neural Network_2 (CNN2), Auto Encoder, Boltzmann Machine & Others, Boltzmann Machine & Others_2, etc. There is this.

The AI-equipped program can recognize the image pattern input in advance and calculate the crop and boundary of a new image by using an algorithm, and the main functions of the program include boundary detection, recognition of crop types, and calculation of cultivated area for each crop. can be processed In case of boundary line detection, it is possible to automatically detect the boundary line between roads, farm roads, and different crops, and in case of crop type recognition, crops of collected species such as broccoli, orange, and garlic can be automatically recognized. By automatically calculating the cultivated area for each crop, it is possible to create an area aggregate table.

This AI-equipped program is an operational process for automating crop reading and area calculation, for example, after detecting a boundary line for an aerial photographed image as shown in FIG. 9, and then constructing a database of crop labeling, after learning the crop pattern, It is possible to recognize local crops, identify crops within the boundary line, and calculate and aggregate crop areas.

In this AI-equipped program, the optimization of the first algorithm and machine learning may be in progress, and it is possible to read crops for 10 square kilometers of the shooting area where the full investigation has been made, and both the first and second shooting results are available. Reading can be performed, for example, processing results are displayed for each input zone as shown in FIG. 10, an image and a list of cultivated areas can be created, and the number of the reading zone can be set to 'Cont' and the crop number to 'Label'. .

The result of crop reading read from the program may be output as, for example, area 1 and area 2 of high-cheong area 1 as shown in FIG. 11, and may be output in a way that the type and area of the corresponding crop are calculated according to the label number.

In addition, the result of crop reading can be grasped as the result of crop reading and total survey through orthographic images, for example, as shown in FIG. 12. The total survey and the current status diagram can be drawn with a blue line, and the crop reading can be drawn with a green line. It can also be compared by selecting only the regions where crop readings have been taken. In this way, the accuracy of the crop reading result can be grasped as well as the crop reading and the total survey are compared.

However, the results of the existing crop reading show a large difference in accuracy compared to the census survey. In particular, the measurement results of crop area show a big difference from the results of the census survey. This difference may be due to the quality of the video image, the lack of input data, the insufficient time for artificial intelligence machine learning, and the video error. The results of this conventional crop reading show a significant difference in each item, for example, as shown in FIG. 13 when compared with the census survey.

In other words, the main reason that the results of crop area measurement differ from the results of the full investigation is due to the quality and error of the video image. This is because the detection and identification of the horizontal and tilted flight of the drone in the aerial photography process of the existing drone is difficult. Since there is no relationship, the image quality and errors occur in the images captured in the tilted flight of the drone.

It is very important to take an image at the time of horizontal flight of the drone. If the image taken while the drone is flying in an inclined state rather than flying horizontally is significantly different from the area of the actual field crop site in the process of reading the area of the crop. looks like That is, since the photographed image of the crop site is not photographed in a horizontal state, but is photographed at an angle, it represents a large difference from the area of the actual field crop site.

In addition to this, the program requires an increase in image sets for machine learning of various shapes of classified crops, processing of input for boundary line changes based on existing current status lines or cadastral maps, and improvement of spatial resolution of photographed images It is also necessary to improve the image specifications of the imaging camera equipment for this purpose, and it will be necessary to establish a database for the various growing seasons of crops by season, from the initial period of crop growth to the time of harvest.

For safe drone operation in mountainous areas, it is difficult to recognize the difference in altitude in the case of 2D images, but for example, through the drone operation software of eBee Plus, it will be possible to establish a flight plan considering the altitude difference in the 3D image, and correct the route through the drone operation software. will also be possible

In addition, since the detection sensor is further configured in the light receiving unit that receives the laser reflected and returned from the altitude change response device, which will be described later, the drone flies parallel to the ground slope in a way that detects time difference information according to the return time of the laser. can be

If the drone is not flying parallel to the ground slope, that is, the drone is flying only on a straight path, regardless of the ground slope, an error may occur in the image registration process due to the difference in altitude from the ground, which in turn results in the overall reading of crops. The data does not match the ground elevation information of the actual site. That is, the detection sensor transmits the detection signal to the manager's terminal via satellite so that the drone can fly parallel to the slope of the ground according to the laser's return time difference. it can be done

In other words, it should be flown so that the slope of the ground and the path of the drone are parallel, which is directly related to the change in spatial resolution. The change of spatial resolution may refer to FIG. 14 .

Therefore, the detection sensor connected to the light receiving unit configured in the altitude change response device can actively take countermeasures so that the flight path of the drone is parallel to the slope of the ground. In addition, when setting a route over a mountain, it is possible to prevent an accident that may hit a mountain while moving when crossing the mountain in a straight line from the landing site to the mission area. If the drone insists on flying in a straight line instead of flying parallel to the slope of the ground, a collision accident may occur in the end as shown in FIG. 15 . As such, the detection sensor connected to the light receiving unit configured in the altitude change response device performs a function of making the flight path of the drone parallel to the slope of the ground.

Although the drawings for the installation structure of the altitude change response device and the weather observation device installed on the drone as an unmanned aerial vehicle are not shown, it will not be applicable to the reason for the lack of description due to the lack of illustration because it is fully understood as described below.

For safe drone operation in mountainous areas, it is difficult to recognize the difference in terrain in the case of 2D images, but it will be possible to establish a flight plan considering the altitude difference in 3D images through the drone operation software of eBee Plus, for example, in the MCU installed in the drone. It will also be possible to modify the route through drone operation software. Of course, the drone operation software installed in the management server may be compatible with the MCU.

In addition, the drone may be equipped with not only an altitude change response device but also a weather observation device, and the altitude change response device may include a light receiving unit and a detection sensor for receiving a reflected and returned laser. Therefore, the detection sensor can transmit a detection signal to the portable terminal so that the drone can fly parallel to the ground slope in a way that detects time difference information according to the laser return time of the light receiving unit that receives the reflected laser, and the pilot, that is, the user With reference to this, it is possible to control the drone flight while maintaining the altitude parallel to the slope of the ground.

If the drone is not flying parallel to the ground slope, that is, the drone is flying only on a straight path, regardless of the ground slope, an error may occur in the image registration process due to the difference in altitude from the ground, which in turn results in the overall reading of crops. The data does not match the ground elevation information of the actual site. That is, the detection sensor transmits a detection signal to the user's terminal through a satellite so that the drone can fly parallel to the slope of the ground according to the laser's return time difference. it can be done

In other words, it should be flown so that the slope of the ground and the path of the drone are parallel, which is directly related to the change in spatial resolution. Therefore, the detection sensor connected to the light receiving unit configured in the altitude change response device can actively take countermeasures so that the flight path of the drone is parallel to the slope of the ground. In addition, when setting a route over a mountain, it is possible to prevent an accident that may hit a mountain while moving when crossing the mountain in a straight line from the landing site to the mission area. As such, the detection sensor connected to the light receiving unit configured in the altitude change response device performs a function of making the flight path of the drone parallel to the slope of the ground.

In particular, the altitude change coping device is installed at the bottom of the drone, and a fine adjustment motor is further installed between the lower end of the drone and the altitude change coping device for the operation of the altitude change coping device. The installation structure of such a fine adjustment motor is not shown, which can be understood as a sufficient description.

The fine adjustment motor is installed between the bottom of the drone and the top of the altitude change response device, so that the altitude change response device can be operated in a horizontal state even when the drone is in an inclined flight.

Such a fine adjustment motor is preferable to use a digital servomotor, and a microprocessor is built in the motor to determine whether the light receiving unit receives the laser, and when the light receiving unit does not receive the laser, the altitude change response device is operated in a horizontal state. It instructs the operation of the fine-tuning motor.

These fine-tuning motors are programmed in the microprocessor to maximize the motor's performance, and because the position can be read faster by adjusting the setting value and using a high frequency, the position control for the altitude change response device is fast and accurate. Therefore, it is possible to implement fine adjustments that cannot be implemented with analog servo motors.

In this way, the fine adjustment motor is a power means for implementing fine adjustment that can be essential for quick and accurate position control of the altitude change response device in the present invention. This fine-tuning motor is a digital servo motor and has a feedback circuit added, so accurate position control is possible. By applying this feedback circuit, more precise control is possible compared to a stepping motor, and malfunctions can be corrected and the speed It is much faster than a stepper motor.

A general DC motor is a motor that operates simply by applying electricity. In such a motor, the shaft rotation can be stopped when electricity is cut off, but it is difficult to specify an exact stop position due to inertia. In the case of a stepping motor, the motor rotates and stops at a certain angle. Although it may be possible to solve the disadvantages of the DC motor due to the causal relationship, there is a disadvantage in that it has its own split angle and operates by moving according to the split angle. That is, if the division angle is 1 degree, there is a problem that 10 commands have to be given to rotate 10 degrees, and precise operation such as 7.3 degrees for example is impossible due to the predetermined angle, and the analog servomotor uses the position control current Since is transmitted in block units, there is a disadvantage that it does not work in fine adjustments.

For these reasons, the fine adjustment motor can be used as a power means necessary for position control of the altitude change response device because it is possible to operate precisely and quickly and accurately.

The fine tuning motor may include, for example, a library for a basic thermomotor in Arduino. In the case of functions applied to the library, for example, functions such as attach, write, read, attached, detach, etc. may be used, and the design code Depending on the application of the digital servomotor can be connected to Arduino and operated. Of course, in the design code, for example, code 1 may be a code that moves the motor to be positioned at set angles, and for example, code 2 is the time to finish rotation by putting a delay in the middle to finish rotation in the rotation speed process of the thermomotor. It may be a code that can provide

For example, Code 1 is a code that moves the angle of the servo motor and moves the servo motor to position it at 0 degrees, 90 degrees, and 180 degrees, and an example of the code is as follows.

code 1

#include<Servo.h>

// Naming the servo motor (setting instance variables)

Servo myServo;

int servoPin = 9;

void setup() {

// Servo motor setup (connection)

myServo.attach(servoPin);

}

void loop() {

// move 0 degrees

myServo.write(0);

delay(5000);

// move 90 degrees

myServo.write(90);

delay(5000);

// move 180 degrees

myServo.write(180);

delay(5000);

}

If you operate code 1, you can see that the servomotor is initially positioned at 0 degrees, then at 90 degrees, then 180 degrees, and then returns to 0 degrees again.

If there is a sound when the servo motor moves around 0 degrees or 180 degrees, you can set 'myServo.attach(servoPin)' in the void setup.

The 'attach' function has two more values as 'attach(pin, min_value, max_value)' behind the original. These two values are set to 544 and 2400 by default, but these two values can be adjusted if the servo motor makes a sound.

code 2

#include<Servo.h>

Servo myServo;

int servoPin = 9;

void setup() {

myServo.attach(servoPin);

}

void loop() {

int angle;

for(angle = 0; angle < 180; angle++){

myServo.write(angle);

delay(20);

}

for(angle = 180; angle > 0; angle--){

myServo.write(angle);

delay(20);

}

}

When I upload code 2, the servomotor slowly rotates to 180 degrees and then back to 0 degrees. This code 2 features 'delay'. In other words, the servomotor generally has a moving speed. If the used SG90 turns 60 degrees in 0.12 seconds, a delay should be added in the middle to give the motor time to finish.

Moreover, as the drone has to predict the occurrence of a gust that can unexpectedly and unexpectedly blow in the sky rather than the ground as shown in FIG. 16, for this purpose, the drone may be further equipped with, for example, an airborne weather radar as a weather observation device, such as The aerial weather radar can evaluate the position and strength of clouds, the movement direction and speed of clouds, and the motion characteristics of fine particles inside clouds due to the Doppler effect, as well as observation of special meteorological phenomena including, for example, the dragon's sphere. is also predictable.

This air-to-air radar is composed of a combination of an ultra-high frequency transmitter, an ultra-high-frequency transmitter, and a solid-state exciter. Compared to the existing weather radar whose transmission system is used as an amplification system by a solid-state output device, the measurement accuracy is high and the power consumption is very low, so the high voltage There is no need to use a stage and the manufacturing cost is low.

Since such an aerial weather radar is mounted on the drone, it is possible to predict unexpected gusts in the sky in advance, and it is possible to prevent collision accidents of drones caused by gusts.

On the other hand, in the image pre-processing, the image processing computer does not immediately recognize the image image like a human being, so if the image received by the user is reflected in light or is too dark, the speed of processing the image rapidly decreases. Therefore, it is often used by appropriately modifying the aspect image to suit the user's purpose.

In the main functions required for image preprocessing, for example, cvtColor() function, threshold() function, morphologyEx() function, and GaussianBlur() function may be combined, and image preprocessing may be processed through these combination functions.

When the cvtColor() function takes an image with the camera mounted on the drone and transmits it to the image processing computer, the image processing computer converts the image image into RGB (Red, Green, Blue) values and recognizes it. Since it is difficult to use the image with a , it is possible to change the image of the camera to a different color. For example, a color image can be converted into a black-and-white image.

The threshold() function is a technique frequently used to detect a desired part or object by segmenting an image called segmentation. Threshold is the simplest segmentation method. The binarization uses a black-and-white image. After converting the original image into a black-and-white image with the cvtColor() function, the background and the object can be separated using a threshold value. The threshold value is a specific value and can be arbitrarily set by the user. The picture is an image output after setting the threshold to 80 in a black and white image.

Pixel values of black and white images have values between 0 and 255. (The closer to 0, the closer to black and white, and the closer to 255, the closer to white.) Here, specifying the threshold as 80 means that each pixel value is less than 80. It means that preprocessing will be performed to change it to 0 (change it to black and white) and change it to 255 (change it to white) if it is greater than or equal to Through this pre-processing, only the white keys of the piano will be segmented.

The morphologyEX() function is a function that applies Erosion and Dilation together. If you look closely at the left part of the white key in the picture above, the unwanted white part remains after the threshold is converted. To do this, the morphologyEX() function is used.

Erosion, also called erosion operation, converts the boundary part of the image into a background image. That is, when applied to a photograph, for example, the white portion may be reduced as a whole and the black portion may be enlarged.

Dilation, also called dilation operation, expands the boundary portion of the image, and is an operation corresponding to the opposite of the erosion operation.

When Erosion or Dilation operation is applied to a photo, it is likely to change to a different image from the original image on the left, so it can be processed by removing only noise with Erosion or Dilation and then applying Dilation or Erosion again. That is, they are called Opening (Erosion application -> Dilation application) and Closing (Dilation application -> Erosion application) operations. As such, the morphologyEX() function is a function that applies the Opening and Closing operations.

The GaussianBlur() function is a function that processes the Blur of the image and makes the noise disappear. For example, in the left image, it seems that the white border part is continuously drawn, but the actual pixel values may be in a state where the black part slightly invades the white part. In other words, although it is not easily recognized by the human eye, the computer can recognize the black part (noise) as in the picture because it receives the pixel values as numbers.

GaussianBlur() function can be used to remove such noise. This GaussianBlur() function compares each pixel of the image with surrounding pixels and changes the pixel value to a similar value through a series of calculations. As a result, the pixel values of the image have continuous values similar to the surrounding pixel values. That is, the noise disappears. If you look closely at the photo, you can see that the image on the right has blur processing on the white border.

A Gaussian Blur() function can be used. This GaussianBlur() function compares each pixel of the image with neighboring pixels and changes the pixel value to a similar value through a series of calculations. As a result, the pixel values of the image pass through successive values similar to the surrounding pixel values.

PPK (Post Processed Kinematics) is an alternative to RTK (Real-Time Kinematic). Using PPK workflows, precise positioning may not occur in real time. Both ground and rover (usually UAV-based) can be processed to record raw GNSS logs and then receive accurate positioning tracks.

Post Processed Kinematics (PPK) is primarily used for UAV mapping, but can also be used as a backup for the RTK for any survey task, PPK provides a more flexible workflow that can be run multiple times in the process of using and processing different settings. In addition, equipment setup is simpler as no crystal link is required between the base and the rover.

There are several advantages to using PPK for mapping with drones: PPK eliminates the need to deploy ground control points (GCPs) that can inspect larger areas. This is especially useful when you need to map large territories or places with difficult terrain.

For PPK mapping, it is recommended that the site has few GCPs for data inspection (checkpointing). The most important part of PPK for UAV mapping is the synchronization between the drone's mounted camera and Reach M+ for the following reasons.

There is always a delay between the camera trigger and the actual picture taking time, and when the drone is flying at high speed, the autopilot receives a position measurement every few meters, for example, an accuracy of 2 meters is not enough for a survey.

Reach (M+) solves the problem by connecting it to the camera shutter via a hot shoe, where each photo is timed with sub-microsecond resolution, and during PPK you receive the exact coordinates of the moment in each photo taken.

The autopilot triggers the camera and records the coordinates it currently has, as camera synchronization is important for centermeter-accurate PPK mapping (using less GCP). If the drone is flying at for example 20 m/s and the GPS is operating at 5 Hz, the autopilot will have a position reading every 4 m, which is not suitable for accurate georeferencing. Also, there is always a delay between the trigger and the actual time the picture is taken.

Thus, reach (M+) solves the positioning problem by connecting directly to the camera hot shoe port synchronized with the shutter, where the time and coordinates of each picture are recorded with sub-microsecond resolution. This way you can use Google Cloud to check the accuracy.

The reach can be connected to the hot shoe port of the capture camera, where a pulse is generated from the flash hot shoe connector every time a picture is taken so that it can be synchronized with the shutter opening.

It records pictures of drone flight and arrival events, where reach can capture flash sync pulses with sub-microsecond resolution and store them in the raw data Rinex log in internal memory.

On the other hand, the interface for the drone operation software is, for example, composed of an app area and a sidebar as shown in FIG. 17, a status panel is displayed in the app area, and a flight parameter tab, a camera tab, a mission waypoint tab, and a mission at the top of the sidebar. Element items such as a planning tab, a setting step tab, and a flight observation tab are configured, and may be configured as a control bar such as a toolbar at the top of the app area.

The real-time location and image according to the UAV mounted on the drone and the real-time sensor (location + image) installation can be transmitted to the integrated control room where the management server is operated, and accordingly, the UAV Mapping Solution (Live Drone Map) is used in the integrated control room. As a result, a map is created in real time and transmitted to the integrated control room in other regions, making it easier to share and utilize images. An image processing process in the drone operating program mounted on the management server may refer to FIG. 18 .

Claims (9)

delete delete delete delete delete delete delete delete an unmanned aerial vehicle that flies in the sky and shoots images of crops; an image processing computer for processing an image through processing of the image taken from the unmanned aerial vehicle; and a management server for integrated management of all data related to the image processed by the image processing computer. including,
a control controller for wireless flight control of the unmanned aerial vehicle; a mobile terminal receiving a sensing signal transmitted from the unmanned aerial vehicle to achieve a flight parallel to the ground slope on which the unmanned aerial vehicle is flying; further comprising,
The unmanned aerial vehicle includes an altitude change response device that maintains an altitude difference parallel to the slope of the ground and enables flight; a weather observation device for predicting by observing the weather in the sky; further comprising,
Drone operation software for operating the setting information necessary for the flight plan of the unmanned aerial vehicle is installed in the management server; The image processing computer is installed with an artificial intelligence-equipped program capable of calculating and generating general reading results of crops that can be compared with the field cultivation target of crops;
The image processing computer performs image pre-processing of the crops to be photographed and grown through a combination of cvtColor() function, threshold() function, morphologyEx() function, and GaussianBlur() function for image pre-processing;
A reach is further connected to the imaging camera or 3D scanner mounted on the unmanned aerial vehicle for positioning necessary for center-meter-accurate PPK mapping;
The detection signal of the detection sensor for detecting the return time difference information of the laser reflected and returned from the light receiving unit of the altitude change response device is transmitted to the portable terminal to achieve the flight of the unmanned aerial vehicle parallel to the slope of the ground;
A digital servo motor is further installed between the lower end of the drone and the altitude change response device for the operation of the altitude change response device, and a feedback circuit is applied to the servomotor. Local geospatial information construction system.

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