TWI777223B - Unmanned aerial vehicle traffic survey system and method thereof - Google Patents

Abstract

An unmanned aerial vehicle (UAV) traffic survey system is provided, which includes a first UAV, a second UAV and a calculation module. The first UAV records the image of a road section during a first time period to obtain a first image data. The second UAV records the image of the road section during a second time period to obtain a second image data. The calculation module executes a projective transformation method to convert the first image data and the second image data into a first converted image data and a second converted image data respectively, and calculates the traffic flow characteristic data of the road section according to the first converted image data and the second converted image data.

Description

UAV traffic survey system and its traffic survey method

The invention relates to an unmanned aerial vehicle traffic investigation system, in particular to an unmanned aerial vehicle traffic investigation system combined with artificial intelligence. The invention also relates to the UAV traffic investigation method of the UAV traffic investigation system.

Traffic survey refers to the collection of traffic characteristics including flow rate, speed, density and other data at the road site. The purpose of the traffic survey is to understand the traffic characteristics such as the road system, which is used to evaluate the service level, analyze the capacity, and develop the traffic improvement plan.

Generally speaking, highways have already deployed detectors, which can collect traffic data for a long time. However, the location of these detectors is fixed, which may not meet the analysis needs of the research site. Therefore, it is often necessary to conduct on-site surveys to further collect traffic flow characteristic data for analysis. Traditional on-site survey methods generally use roadside cameras to shoot, requiring construction to mount cameras on roadside facilities. The cameras are recovered after the survey is completed, and then the videos are manually observed for data registration. There are three shortcomings in the on-site investigation: (1) the location of the cameras is limited by the existing roadside facilities on the site, which may not fully meet the requirements; (2) the deployment of the cameras needs to be constructed on site, which affects the operation of traffic flow; (3) Data registration is labor-intensive and prone to survey errors due to observers' visual fatigue.

Unmanned aerial vehicle (UAV) can shoot a large-scale traffic flow image at high altitude fixed point, solve the problem of roadside camera layout, and the orthophoto film shot can be automatically analyzed by using artificial intelligence computer vision algorithm to eliminate the human factors caused by data logging. error. Taking 4K video as an example, the vehicle can still be effectively identified by manual or computer vision within a shooting range of 700 meters. The traffic survey should cover 2-4 hours during peak hours. However, the current commercial drones or long-term drones have an endurance ranging from about 30 minutes to 80 minutes, and a single flight shooting cannot meet the survey needs. If multiple drones are used for continuous shooting, the video images obtained may be slightly different due to the hovering height of the drone, the setting of the shooting angle, or the different models or camera specifications. They are not identical, and the data cannot be effectively integrated for the analysis of traffic flow characteristics.

According to an embodiment of the present invention, the present invention provides a UAV traffic investigation system, which includes a first UAV, a second UAV, and a computing module. The first drone captures a road section within the first time interval to obtain first image data. The second drone captures the road section within the second time interval to obtain second image data. The computing module executes a projection transformation method to convert the first image data and the second image data into the first converted image data and the second converted image data based on the real coordinate system, and then calculates according to the first converted image data and the second converted image data Traffic characteristics data for this road segment.

In one embodiment, the first time interval and the second time interval are continuous or partially overlapping.

In one embodiment, the computing module performs a projective transformation method to convert the image coordinate system of the first image data and the image coordinate system of the second image data into a real coordinate system to generate the first transformed image data and the second transformed image data.

In one embodiment, the computing module selects a plurality of points of interest from the first image data and the second image data respectively from the track image base map including the road section, and records the points of interest in the first image data and The image coordinates and the corresponding latitude and longitude coordinates in the second image data, and then perform a projection transformation method to convert the image coordinate system of the first image data and the image coordinate system of the second image data into a real coordinate system.

In one embodiment, the calculation module calculates a plurality of lane equations of the first converted image data and the second converted image data including the base map of the track image of the road section, and then calculates the road section according to the lane equations. Traffic characteristics data.

In one embodiment, the calculation module selects a plurality of image coordinate points from a plurality of lane lines of the first converted image data and the second converted image data including the track image base map of the road section, and performs polynomial curve simulation. Combined evaluation to obtain lane equations for each lane line.

In one embodiment, the traffic characteristic data includes one or more of flow rate data, velocity data, density data, and lane change data.

According to another embodiment of the present invention, the present invention provides a UAV traffic investigation method, which includes the following steps: photographing a road section within a first time interval by a first UAV to obtain first image data; The drone captures the road section in the second time interval to obtain the second image data; the projection transformation method is performed by the computing module to convert the first image data and the second image data into the first transformed image data based on the real coordinate system and the second converted image data; and calculating the traffic flow characteristic data of the road section according to the first converted image data and the second converted image data through the computing module.

In one embodiment, the first time interval and the second time interval are continuous or partially overlapping.

In one embodiment, the step of converting the first image data and the second image data into the first converted image data and the second converted image data based on the real coordinate system by executing the projective transformation method by the computing module further includes the following steps: The computing module executes a projective transformation method to convert the image coordinate system of the first image data and the image coordinate system of the second image data into a real coordinate system to generate the first transformed image data and the second transformed image data.

In one embodiment, the projection transformation method is performed by the computing module to transform the image coordinate system of the first image data and the image coordinate system of the second image data into a real coordinate system to generate the first transformed image data and the second transformed image data The step further includes the following steps: selecting a plurality of points of interest from the first image data and the second image data in the track image base map including the road section respectively, and recording these points of interest in the first image The image coordinates and the corresponding latitude and longitude coordinates in the data and the second image data; and the projection transformation method is performed by the computing module to convert the image coordinate system of the first image data and the image coordinate system of the second image data into a real coordinate system.

In one embodiment, the step of calculating the traffic flow characteristic data of the road section according to the first converted image data and the second converted image data by the calculation module further includes the following steps: calculating, by the calculation module, the first converted image data and the second converted image data; 2. Convert a plurality of lane equations of the image data including the base map of the track image of the road section, and then calculate the traffic flow characteristic data of the road section according to the lane equations.

In one embodiment, the step of calculating, by the calculation module, the lane equations of the first converted image data and the second converted image data including the base map of the track image of the road section further includes the following steps: respectively, through the calculation module Select a plurality of image coordinate points from a plurality of lane lines of the first converted image data and the second converted image data including the track image base map of the road section, and perform polynomial curve fitting and calibration to obtain the corresponding lane lines of each lane line. Lane Equation.

In one embodiment, the traffic characteristic data includes one or more of flow rate data, velocity data, density data, and lane change data.

Based on the above, according to the UAV traffic survey system and the traffic survey method thereof of the present invention, it can have one or more of the following advantages:

(1) In one embodiment of the present invention, the UAV traffic investigation system adopts UAV to conduct traffic investigation and integrates artificial intelligence technology, so it can fully meet the needs of traffic investigation, and will not affect the operation of traffic flow, and can greatly reduce the labor cost Demand, effectively improving the shortcomings of on-site investigations.

(2) In one embodiment of the present invention, the UAV traffic investigation system uses multiple UAVs to conduct alternate shooting investigations, which can provide a wide range and long-term shooting requirements, and can ensure that the captured traffic images can be kept continuous and uninterrupted. , so it can indeed meet the needs of traffic surveys.

(3) In one embodiment of the present invention, the UAV traffic survey system uses multiple UAVs to conduct alternate shooting surveys, and integrates photogrammetry methods to convert image coordinates and real coordinates, so that the traffic survey data obtained by these UAVs It can have the same coordinate system to achieve time-space synchronization, effectively improving the accuracy of traffic surveys and the efficiency of data sorting.

(4) In one embodiment of the present invention, the UAV traffic investigation system uses multiple UAVs to conduct alternate shooting investigations, and uses artificial intelligence computer vision algorithms to perform automated analysis, so it can effectively eliminate human errors in data registration , to further improve the accuracy of traffic surveys.

(5) In one embodiment of the present invention, the UAV traffic investigation system is ingeniously designed and effectively integrates multiple technologies, so it can achieve the desired effect without greatly increasing the cost, so it is practical and applicable. more extensive.

Embodiments of the UAV traffic survey system and the traffic survey method according to the present invention will be described below with reference to the relevant drawings. For the sake of clarity and convenience in the description of the drawings, the dimensions and proportions of the components in the drawings may be changed. Presented exaggerated or reduced. For ease of understanding, the same elements in the following embodiments are denoted by the same symbols.

Please refer to FIG. 1 , which is a first schematic diagram of a UAV traffic investigation system according to an embodiment of the present invention. As shown in the figure, the UAV traffic investigation system 1 includes a first UAV 11 , a second UAV 12 and a computing module 13 . In another embodiment, the UAV traffic investigation system 1 may also include more than three UAVs.

The first drone 11 photographs the road section within the first time interval T1 to obtain the first image data, and transmits the first image data to the computing module 13 . When the power of the first drone 11 is lower than the preset power threshold, the second drone 12 starts to lift off to take over the first drone 11; when the second drone 12 lifts off to the same height, it takes over the first drone 11 , the first drone 11 landed on the ground, and the battery was replaced on standby. The computing module 13 can be various computer devices, such as servers, workstations, and notebook computers.

The second drone 12 captures the road section during the second time interval T2 to obtain the second image data, and transmits the second image data to the computing module 13 . Similarly, when the power of the second drone 12 is lower than the preset power threshold, the first drone 11 starts to lift off to take over the second drone 12; when the first drone 11 lifts up to the same height, it takes over the second drone 11 At the time of the drone 12, the second drone 12 descends to the ground, and the battery is replaced on standby, so as to prepare to take over the first drone 11 again in a certain period of time.

The above-mentioned mechanism is repeated in the subsequent third time interval T3, fourth time interval T4 and subsequent time intervals, and the adjacent time intervals are continuous or partially overlapping; for example, the second time interval T2 and the first time interval T2 The time interval T1 is continuous or partially overlapping; the third time interval T3 and the second time interval T2 are continuous or partially overlapping; the fourth time interval T4 and the third time interval T3 are continuous or partially overlapping. The preset power threshold should be sufficient for the first drone 11 and the second drone 12 to wait for replacement and land safely. Through the above-mentioned mechanism, the UAV traffic investigation system 1 can provide large-scale and long-term shooting requirements, and can ensure that the captured traffic images can remain continuous and uninterrupted.

Please refer to FIG. 2 , which is a second schematic diagram of a UAV traffic investigation system according to an embodiment of the present invention. As shown, the road section R includes an acceleration lane L0, a first lane L1, a second lane L2, and a third lane L3. The calculation module 13 selects a plurality of POIs from the first image data including the track image base map of the road section R, and records the image coordinates of these POIs in the first image data and the corresponding POIs The latitude and longitude coordinates of the image coordinates, and then perform the projective transformation method (Projective transformation between planes) to convert the image coordinate system of the first image data to the real coordinate system to obtain the first transformed image data, such as formula (1) and formula (2) shown:

……………(1)

……………(1)

……………(2)

……………(2)

Among them, (x, y) are image coordinates; (X, Y) are real coordinates; e0, e1, e2, f0, f1, f2, g1, g2 are conversion parameters, which can be obtained by calibration.

Similarly, the computing module 13 also obtains the second converted image data through the same method. Through this mechanism, the computing module 13 can convert the first image data and the second image data into the first converted image data and the second converted image data based on the same real coordinate system, so that the first unmanned aerial vehicle 11 and the second unmanned aerial vehicle The images captured by the camera 12 can be synchronized in space. Since the first drone 11 and the second drone 12 are alternately photographed, the videos obtained by the first drone 11 and the second drone 12 may be caused by the hovering height, shooting angle setting of the first drone 11 and the second drone 12 or other factors. The imaging may be slightly different; however, the above-mentioned mechanism can effectively correct the difference, so that the images captured by the first drone 11 and the second drone 12 can be spatially synchronized.

Please refer to FIG. 3 , which is a third schematic diagram of a UAV traffic investigation system according to an embodiment of the present invention. As shown in the figure, next, the calculation module 13 selects a plurality of image coordinate points from each of the plurality of lane lines in the base map of the track image of the road section from the first converted image data, and executes a polynomial curve Curve fitting is performed to obtain the lane equations LF1 to LF6 of the lane lines; in this embodiment, the calculation module 13 uses the first converted image data to include a plurality of base maps of the track image of the road section For each of the lane lines, more than 10 image coordinate points are selected, and polynomial curve fitting and calibration are performed to obtain the lane equations LF1-LF6 of the lane lines. The lane between the lane equation LF1 and the lane equation LF2 is the acceleration lane L0; the lane between the lane equation LF3 and the lane equation LF4 is the first lane L1; the lane between the lane equation LF4 and the lane equation LF5 is the second lane L2; the lane between the lane equation LF5 and the lane equation LF6 is the third lane L3. After obtaining the lane equations LF1 to LF6 of the lane lines, the calculation module 13 can determine the lane position of the vehicle according to the image coordinates of the head center of the vehicle trajectory of each vehicle; in this way, the road segment at the first transition can be obtained. Traffic characteristic data of the time interval of the image data; wherein, the traffic characteristic data may include one or more of flow rate data, velocity data, density data and lane change data.

Similarly, the calculation module 13 can also select a plurality of image coordinate points from each of the plurality of lane lines in the base map of the track image of the road section from the second converted image data in the same way, and execute the polynomial Curve fitting and calibration are performed to obtain the lane equations LF1~LF6 of the lane lines, and then the lane position of the vehicle is determined according to the image coordinates of the front center of the vehicle trajectory of each vehicle, and the second converted image of this road section can be obtained. Traffic characteristics data for the time interval of the data. From the above, it can be seen that the UAV traffic investigation system 1 of this embodiment can conduct a traffic investigation of a road section through artificial intelligence (AI) image analysis, and the first UAV 11 and the second UAV 12 take pictures above the road section. The image, and then use the deep learning algorithm to analyze the traffic flow in the image to detect, track, and classify the vehicle, thereby obtaining the traffic flow characteristic data, and can achieve the effect of time-space synchronization.

Please refer to FIGS. 4A to 4D , which are spatiotemporal diagrams of half-hour traffic trajectories of road sections measured by the UAV traffic survey system according to an embodiment of the present invention. Figures 4A to 4D are the half-hour (07:00~07:30) traffic trajectories of each lane of a road section obtained through the mechanism of this embodiment; Figure 4A shows the half-hour (07:00~07:30) traffic trajectories of the third lane L3. Hourly traffic trajectory spatio-temporal map; Figure 4B represents the half-hour traffic trajectory spatiotemporal map of the second lane L2; Figure 4C represents the half-hour traffic trajectory spatiotemporal map of the first lane L1; Figure 4D represents the half-hour traffic trajectory spatio-temporal map of the acceleration lane picture. The corresponding position of each lane is shown in the diagram on the left side of the picture; the trajectory line in the picture represents each vehicle, the color of the trajectory line represents the speed of the vehicle at that time point, and the value refers to the color bar on the right side of the picture.

Please refer to FIGS. 5A to 5C, which are flow rate-time diagrams, density-time diagrams, and rate-time diagrams of the measured road section of the UAV traffic survey system according to an embodiment of the present invention.

As shown in Figure 5A, the curve F0 represents the flow rate-time change of the acceleration lane; the curve F1 represents the flow rate-time change of the first lane L1; the curve F2 represents the flow rate-time change of the second lane L2; Curve F3 represents the flow rate-time variation of the third lane L3; curve FT represents the sum.

As shown in Fig. 5B, the curve D0 represents the density-time variation of the acceleration lane; the curve D1 represents the density-time variation of the first lane L1; the curve D2 represents the density-time variation of the second lane L2; the curve D3 represents Density-time variation of the third lane L3; curve DA represents the main line average.

As shown in Figure 5C, the curve S0 represents the speed-time change of the acceleration lane; the curve S1 represents the speed-time change of the first lane L1; the curve S2 represents the speed-time change of the second lane L2; the curve S3 represents Velocity-time variation of the third lane L3; curve SA represents the main line average.

It can be seen from the above that the UAV traffic survey system 1 of this embodiment can indeed achieve the effect of time-space synchronization, indeed meet the needs of traffic survey, and effectively improve the accuracy of traffic survey and the operation efficiency of data collation.

It is worth mentioning that due to various technical limitations, the existing on-site survey methods cannot fully meet the needs of traffic surveys, and easily affect the operation of traffic flow, and are prone to human survey errors. On the contrary, according to the embodiment of the present invention, the UAV traffic investigation system adopts UAV to conduct traffic investigation and integrates artificial intelligence technology, so it can fully meet the needs of traffic investigation without affecting the operation of traffic flow, and can greatly reduce the demand for manpower , effectively improving the shortcomings of on-site investigation.

The existing UAV traffic survey methods cannot meet the needs of traffic survey due to the limitation of the endurance of the UAV. On the contrary, according to the embodiment of the present invention, the UAV traffic investigation system uses multiple UAVs to perform alternate shooting investigations, which can provide a wide range and long-term shooting requirements, and can ensure that the captured traffic images can be kept continuous and uninterrupted. Therefore, it can indeed meet the needs of traffic surveys.

The existing UAV traffic investigation methods may not be able to effectively integrate the data for analysis of traffic flow characteristics due to differences in hovering height and shooting angle settings, or due to different models or camera specifications. On the contrary, according to the embodiment of the present invention, the UAV traffic survey system uses multiple UAVs to perform alternate shooting surveys, and integrates the photogrammetry method to convert image coordinates and real coordinates, so that the traffic survey data obtained by these UAVs can be It has the same coordinate system to achieve time-space synchronization, which effectively improves the accuracy of traffic surveys and the efficiency of data sorting.

In addition, according to the embodiment of the present invention, the UAV traffic investigation system uses multiple UAVs to conduct alternate shooting investigations, and uses artificial intelligence computer vision algorithms to perform automatic analysis, so it can effectively eliminate human errors in data registration, and further Improve the accuracy of traffic surveys.

Furthermore, according to the embodiment of the present invention, the UAV traffic investigation system is ingeniously designed and effectively integrates multiple technologies, so that the desired effect can be achieved without greatly increasing the cost, so it is practical and more applicable. for broad. As can be seen from the above, the UAV traffic investigation system and method according to the embodiments of the present invention can indeed achieve unexpected effects.

Please refer to FIG. 6 , which is a flowchart of a method for investigating drone traffic according to an embodiment of the present invention. The UAV traffic investigation method of this embodiment includes the following steps:

Step S61 : photographing a road section within the first time interval by the first drone to obtain the first image data.

Step S62 : photographing the road section within the second time interval by the second drone to obtain the second image data.

Step S63 : Convert the first image data and the second image data into the first converted image data and the second converted image data based on the real coordinate system by performing a projective transformation method through the computing module.

Step S64: Calculate the traffic flow characteristic data of the road section according to the first converted image data and the second converted image data through the calculation module.

To sum up, according to the embodiments of the present invention, the UAV traffic investigation system uses UAVs for traffic investigation and integrates artificial intelligence technology, so it can fully meet the needs of traffic investigation without affecting the operation of traffic flow, and can greatly reduce manpower demand, effectively improving the shortcomings of on-site investigations.

In addition, according to the embodiment of the present invention, the UAV traffic investigation system uses multiple UAVs to perform alternate shooting surveys, which can provide a wide range and long-term shooting requirements, and can ensure that the captured traffic images can be kept continuous and uninterrupted, so It can really meet the needs of traffic surveys.

In addition, according to an embodiment of the present invention, the UAV traffic survey system uses multiple UAVs to perform alternate shooting surveys, and integrates the photogrammetry method to convert image coordinates and real coordinates, so that the traffic survey data obtained by these UAVs can have The same coordinate system can achieve the synchronization of time and space, which can effectively improve the accuracy of traffic survey and the efficiency of data processing.

In addition, according to the embodiment of the present invention, the UAV traffic investigation system uses multiple UAVs to conduct alternate shooting investigations, and uses artificial intelligence computer vision algorithms to perform automatic analysis, so it can effectively eliminate human errors in data registration, and further Improve the accuracy of traffic surveys.

Furthermore, according to the embodiment of the present invention, the UAV traffic investigation system is ingeniously designed and effectively integrates multiple technologies, so that the desired effect can be achieved without greatly increasing the cost, so it is practical and more applicable. for broad.

It can be seen that the present invention has indeed achieved the desired enhancement effect under the breakthrough of the previous technology, and it is not easy for those who are familiar with the technology to think about it. Yuan has filed a patent application in accordance with the law, and I implore your bureau to approve this invention patent application, so as to encourage creation, and to be grateful.

The above description is exemplary only, not limiting. Any other equivalent modifications or changes without departing from the spirit and scope of the present invention should be included in the appended patent application scope.

1: UAV Traffic Survey System 11: The first drone 12: Second Drone 13: Computing Module T1: The first time interval T2: Second time interval T3: The third time interval T4: Fourth time interval R: road segment L0: acceleration lane L1: first lane L2: Second lane L3: Third lane POI: Point of Interest LF1~LF6: Lane Equation F0~F3,FT,D0~D3,DA,S0~S3,SA: Curve S61~S64: Step flow

FIG. 1 is a first schematic diagram of a UAV traffic investigation system according to an embodiment of the present invention.

FIG. 2 is a second schematic diagram of a UAV traffic investigation system according to an embodiment of the present invention.

FIG. 3 is a third schematic diagram of a UAV traffic investigation system according to an embodiment of the present invention.

Figures 4A to 4D are spatiotemporal diagrams of half-hour traffic trajectories of road sections measured by the UAV traffic survey system according to an embodiment of the present invention.

FIG. 5A is a flow rate-time diagram of the measured road section of the UAV traffic survey system according to an embodiment of the present invention.

FIG. 5B is a density-time diagram of the measured road section of the UAV traffic survey system according to an embodiment of the present invention.

FIG. 5C is a velocity-time diagram of the measured road section of the UAV traffic survey system according to an embodiment of the present invention.

FIG. 6 is a flow chart of a method for investigating drone traffic according to an embodiment of the present invention.

1: UAV Traffic Survey System

11: The first drone

12: Second Drone

13: Computing Module

T1: The first time interval

T2: Second time interval

T3: The third time interval

T4: Fourth time interval

Claims (14)

An unmanned aerial vehicle traffic investigation system, comprising: a first unmanned aerial vehicle, photographing a road section in a first time interval to obtain a first image data; a second unmanned aerial vehicle, in a second time interval photographing the road section to obtain a second image data; and a computing module performing a projective transformation method to convert the first image data and the second image data into a first transformation based on a real coordinate system image data and a second converted image data, so that the first converted image data and the second converted image data are time-synchronized, and then according to the first converted image data and the second converted image data a Traffic flow characteristic data; wherein the first drone and the second drone shoot alternately, so as to provide large-scale and long-term shooting requirements, and obtain the traffic flow characteristic data. The UAV traffic investigation system of claim 1, wherein the first time interval and the second time interval are continuous or partially overlapping. The UAV traffic investigation system of claim 1, wherein the computing module performs the projection transformation method to convert the image coordinate system of the first image data and the image coordinate system of the second image data into the real coordinate system to The first converted image data and the second converted image data are generated. The UAV traffic investigation system according to claim 3, wherein the computing module selects a plurality of points of interest from the first image data and the second image data including the trajectory image base map of the road section, respectively, and recording the points of interest in the images in the first image data and the second image data coordinates and the corresponding latitude and longitude coordinates, and then perform the projective transformation method to convert the image coordinate system of the first image data and the image coordinate system of the second image data into the real coordinate system. The UAV traffic investigation system as claimed in claim 1, wherein the calculation module calculates a plurality of lane equations of the first converted image data and the second converted image data including the base map of the trajectory image of the road section, and then The traffic flow characteristic data of the road section is calculated according to the lane equations. The UAV traffic investigation system as claimed in claim 5, wherein the calculation module is selected from a plurality of lane lines of the first converted image data and the second converted image data including the base map of the track image of the road section, respectively A plurality of image coordinate points are obtained, and a polynomial curve fitting calibration is performed to obtain the lane equation of each of the lane lines. The UAV traffic investigation system according to claim 1, wherein the traffic characteristic data includes one or more of flow rate data, speed data, density data and lane change data. A UAV traffic investigation method, comprising: photographing a road section by a first UAV in a first time interval to obtain a first image data; photographing by a second UAV in a second time interval The road section obtains a second image data; a projection transformation method is performed through a computing module to convert the first image data and the second image data into a first converted image data and a the second converted image data, so that the first converted image data and the second converted image data are time-synchronized; and The calculation module calculates a traffic flow characteristic data of the road section according to the first converted image data and the second converted image data, whereby the first drone and the second drone take pictures alternately, so as to provide a large Scope and long-term shooting requirements, and obtain the traffic characteristics data. The UAV traffic investigation method as claimed in claim 8, wherein the first time interval and the second time interval are continuous or partially overlapping. The UAV traffic investigation method as claimed in claim 8, wherein the projection transformation method is executed by the computing module to transform the first image data and the second image data into the first transformed image data based on the real coordinate system And the step of converting the second image data further comprises: converting the image coordinate system of the first image data and the image coordinate system of the second image data into the real coordinate system by executing the projective transformation method through the computing module to generate The first converted image data and the second converted image data. The UAV traffic survey method as claimed in claim 10, wherein the projection transformation method is executed by the computing module to convert the image coordinate system of the first image data and the image coordinate system of the second image data into the real coordinate system The step of generating the first converted image data and the second converted image data further includes: through the computing module, respectively, from the first image data and the second image data in the track image base map including the road section Selecting a plurality of points of interest, and recording the image coordinates and corresponding latitude and longitude coordinates of the points of interest in the first image data and the second image data; and executing the projection transformation method on the first image data through the computing module. The image coordinate system of the image data and the image coordinate system of the second image data are converted into the real coordinate system. The UAV traffic investigation method as claimed in claim 8, wherein the step of calculating the traffic flow characteristic data of the road section according to the first converted image data and the second converted image data by the calculation module further comprises: The calculation module calculates a plurality of lane equations of the first converted image data and the second converted image data including the base map of the track image of the road section, and then calculates the traffic flow of the road section according to the lane equations characteristic data. The UAV traffic survey method as claimed in claim 12, wherein the calculation module calculates the first converted image data and the second converted image data of the lane equations including the trajectory image base map of the road section The step further includes: selecting a plurality of image coordinate points from the first converted image data and the plurality of lane lines of the second converted image data including the track image base map of the road section through the calculation module, and executing a A polynomial curve fit calibration is performed to obtain the lane equation for each of the lane lines. The UAV traffic investigation method according to claim 8, wherein the traffic characteristic data comprises one or more of flow rate data, speed data, density data and lane change data.

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