A method, system, device and medium for detecting the quality of unmanned aerial vehicle aerial photography data
Patent Information
- Application Number
- CN202411655198.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2026-10-27
Smart Images
Figure CN122990974A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of drone aerial photography technology, and in particular to a method, system, equipment and medium for quality inspection of drone aerial photography data. Background Technology
[0002] With the development of UAV technology, UAVs have been widely used in surveying, geographic information acquisition, and environmental monitoring. High-resolution UAV imagery provides rich geospatial information, offering crucial support for various geographic information systems and accurate ground modeling. However, due to the influence of weather, environment, and flight attitude during UAV flight, the quality of the captured image data cannot be fully guaranteed, placing high demands on image stitching and coverage integrity. Image data quality inspection directly affects the efficiency and accuracy of data processing. Therefore, to ensure the quality of UAV aerial images, comprehensive quality inspection is necessary after capture to check key parameters such as overlap, tilt angle, and flight altitude, ensuring the integrity and reliability of the image data.
[0003] Current UAV image quality inspection methods primarily rely on post-processing inspection. These methods typically require significant time and human resources and are susceptible to human error, especially with high-density image data. Their efficiency and accuracy cannot meet the growing demands. Furthermore, traditional image quality inspection often requires actual image data, increasing data processing complexity and time costs, making it unsuitable for rapid, real-time inspection needs. Summary of the Invention
[0004] To improve the efficiency of quality inspection of UAV images, this application provides a method, system, equipment, and medium for quality inspection of UAV aerial photography data.
[0005] Firstly, this application provides a method for quality inspection of drone aerial photography data, employing the following technical solution: A method for quality inspection of drone aerial photography data, the quality inspection method comprising: Acquire camera parameters, drone attitude data, and shooting position data of each shooting point along the flight path when the drone is flying on the flight path; The shooting location data of each shooting point on the flight route is converted into corresponding planar coordinates, and the terrain height data corresponding to each shooting point is determined based on the planar coordinates. The rotation matrix is calculated based on the UAV attitude data; Based on the terrain height data, rotation matrix, and camera parameters, the ground shooting coverage of each shooting point is calculated; based on the ground shooting coverage of each shooting point on the flight path, the overlap data of each shooting point is calculated; wherein, the overlap data includes the directional overlap between adjacent shooting points on the flight path and the lateral overlap between the flight path and adjacent flight paths. The overlap data of each shooting point is compared with the preset overlap limit range to obtain the overlap comparison result; Based on the overlap comparison results, the ground shooting coverage of each shooting point is color-rendered to obtain the rendered quality inspection graphic. A first image quality inspection report is generated based on the rendered quality inspection graphic and the overlap comparison result.
[0006] By adopting the above technical solution, based on efficient quality inspection of UAV imagery data, it is possible to quickly and accurately calculate key indicators such as ground coverage and overlap, and provide intuitive quality feedback through graphical rendering and quality reports. This technical solution can significantly improve the quality inspection efficiency of UAV imagery, ensure the reliability and accuracy of imagery data, and is suitable for various terrain data mapping and geographic information collection scenarios.
[0007] Optionally, the rotation matrix R is calculated based on the UAV attitude data. xyz The calculation formulas include: In the above formula, R is the roll angle (Roll), Y is the yaw angle (Yaw), and P is the pitch angle (Pitch); R xyz This is a rotation matrix used for spatial coordinate transformation based on the UAV's attitude data.
[0008] Optionally, based on the terrain height data, rotation matrix, and camera parameters, the ground shooting coverage area of each shooting point is calculated: Obtain the terrain height data, camera parameters, and rotation matrix R of the shooting point. xyz The camera parameters include the camera sensor size and the camera focal length f. Set the shooting location data of the shooting point as (X) s Y s Z s The terrain height data of the shooting point is set as Z. a Based on the camera sensor size, the image-side coordinates (x, y) of each corner point in the camera sensor are calculated. The image coordinates (x, y) and the rotation matrix R are used to... xyz Terrain height data Z aCamera focal length f and shooting position data (X s Y s Z s Substituting these values into formulas (2), (3), and (4) below, the ground position coordinates (X, Y, F, Z) of each corner point are calculated. a Y a ); where each of the corner points includes the top left corner, top right corner, bottom left corner, bottom right corner, and center point; X a =X s +γ(a1*x+a2*y-a3*f) (3) X a =X s +γ(b1*x+b2*y-b3*f) (4) Based on the ground position coordinates (X) of each corner point a Y a Based on a three-dimensional terrain database, the actual terrain height at each corner point is determined. The final ground terrain height Z corresponding to the shooting point is obtained by averaging the actual terrain heights of each corner point. A ; The final ground terrain height Z A Substitute the values into formulas (5) and (6) below to calculate the actual ground coordinates (X) of each corner point. A Y A ), thus obtaining the ground shooting coverage area of the shooting point; In the above formula, γ is the scaling factor, (x,y) are the image-side coordinates of the shooting point, f is the camera focal length, and X... S Y S Z S X is the location data of the shooting point. A Y A Z A a represents the actual coordinates of the shooting location; i b i c i Corresponding to rotation matrix R xyz The element in the i-th row.
[0009] By adopting the above technical solution, not only are the changes in terrain elevation considered, ensuring more accurate ground coordinates for each point, but the impact of measurement errors at individual points is also reduced through multi-point averaging calculations, thus improving the reliability and representativeness of the data. In applications such as aerial photography, remote sensing, and geographic information systems, this technical solution can support more effective mission planning and data analysis, ensure the accuracy of the boundaries and center points of the shooting range, adapt to various complex terrain environments, and improve the overall reliability and efficiency of the mission.
[0010] Optionally, each of the corner points includes the upper left corner, the upper right corner, the lower left corner, the lower right corner and the center point; based on the camera size, the calculation formula for the image coordinates (x, y) of each corner point in the camera size includes: the image coordinates of the upper left corner are: x = -w / 2, y = -h / 2 (7); The image coordinates of the upper right corner are: x = w / 2, y = -h / 2 (8); The image coordinates of the lower left corner are: x = -w / 2, y = h / 2 (9); The image coordinates of the lower right corner are: x = w / 2, y = h / 2 (10); The image-side coordinates of the center point are: x = 0, y = 0 (11); In the above formula, h is the height of the camera sensor size, and w is the width of the camera sensor size.
[0011] Optionally, the step of calculating the overlap data of each shooting point based on the ground shooting coverage of each shooting point along the flight route includes: The heading overlap of each shooting point is calculated based on the intersection area of the ground projection coverage of each adjacent shooting point on the flight path. The ground shooting coverage areas of each shooting point along the flight route are merged to obtain the aerial shooting area of the flight route. The lateral overlap of adjacent shooting points between different routes is calculated based on the intersection area of the aerial photography area of the stated route and the aerial photography area of adjacent routes.
[0012] By adopting the above technical solution and conducting detailed overlap analysis on the UAV aerial photography data, not only is the continuity and consistency of imagery within the same flight path ensured, but the connectivity between images along different flight paths is also effectively assessed. This precise overlap calculation method, combined with efficient ground-based coverage merging technology, can significantly improve the quality control level of aerial photography data, providing a reliable foundation for subsequent data processing and applications.
[0013] Optionally, the quality inspection method further includes: Acquire image quality data for each shooting point along the flight path; the image data includes image yaw angle, image tilt angle, and image altitude hold. The image quality data of each shooting point is compared with the preset image tolerance range to obtain the image quality comparison results; Based on the image quality comparison results, the ground shooting coverage of each shooting point is color-rendered to obtain the rendered image quality detection graphic. A second image quality inspection report is generated based on the rendered image quality inspection graphic and the image quality comparison results.
[0014] By employing the aforementioned technical solutions, comprehensive quality inspection of aerial photography data was conducted, ensuring that the image quality at each shooting point met the standards. By acquiring key parameters such as image yaw angle, image tilt angle, and image altitude hold, and comparing them with preset tolerance ranges, the image quality at each shooting point can be accurately assessed. Furthermore, color rendering was used to visually display the quality assessment results, helping users quickly identify potential problems. The final quality inspection report not only provides detailed data support but also offers important references for the overall quality control of the project.
[0015] Secondly, this application provides a quality inspection system for drone aerial photography data, employing the following technical solution: A quality inspection system for drone aerial photography data, the quality inspection system comprising: The data acquisition module is used to acquire camera parameters, drone attitude data, and shooting position data of each shooting point on the flight path when the drone is flying along the flight path. The terrain height data generation module is used to convert the shooting position data of each shooting point on the flight route into corresponding planar coordinates, and determine the terrain height data corresponding to each shooting point based on the planar coordinates. The rotation matrix calculation module is used to calculate the rotation matrix based on the UAV attitude data. The coverage calculation module is used to calculate the ground shooting coverage of each shooting point based on the terrain height data, rotation matrix and camera parameters; The overlap calculation module is used to calculate the overlap data of each shooting point based on the ground shooting coverage of each shooting point on the flight path; wherein, the overlap data includes the directional overlap between adjacent shooting points on the flight path and the lateral overlap between the flight path and adjacent flight paths. The overlap comparison module is used to compare the overlap data of each shooting point with the preset overlap limit range to obtain the overlap comparison result; The first rendering module is used to perform color rendering on the ground shooting coverage of each shooting point according to the overlap comparison result, so as to obtain the rendered quality inspection graphic. The quality inspection report generation module is used to generate a first image quality inspection report based on the rendered quality inspection graphic and the overlap comparison result.
[0016] Optionally, the quality inspection system further includes: The image data module is used to acquire image quality data of each shooting point along the flight path; the image data includes image yaw angle, image tilt angle, and image altitude hold. The image quality comparison module is used to compare the image quality data of each shooting point with the preset image tolerance range to obtain the image quality comparison result. The second rendering module is used to perform color rendering on the ground shooting coverage of each shooting point according to the image quality comparison results, so as to obtain the rendered image quality detection graphic. The quality inspection report generation module is also used to generate a second image quality inspection report based on the rendered image quality inspection graphic and the image quality comparison result.
[0017] Thirdly, this application provides a computer device, which adopts the following technical solution: A computer device includes a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to perform the steps of the method as described in the first aspect.
[0018] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as in any of the methods in the first aspect.
[0019] In summary, this application offers at least one of the following beneficial technical effects: By comprehensively considering multiple factors such as camera parameters, rotation matrix, and terrain height, this application ensures the comprehensiveness and accuracy of the calculations. This technical solution improves data reliability by reducing the impact of measurement errors at individual points, adapts to changes in complex terrain, and provides strong technical support for applications in aerial photography, remote sensing, and geographic information systems, significantly enhancing the accuracy and efficiency of mission planning and data analysis. Attached Figure Description
[0020] Figure 1 This is a schematic diagram of the first process of a method for quality inspection of drone aerial photography data according to one embodiment of this application.
[0021] Figure 2This is a schematic diagram illustrating the determination of terrain height based on planar coordinates according to one embodiment of this application.
[0022] Figure 3 This is a schematic diagram of the second process of a method for quality inspection of drone aerial photography data according to one embodiment of this application.
[0023] Figure 4 This is a schematic diagram of the third process of a method for quality inspection of drone aerial photography data according to one embodiment of this application.
[0024] Figure 5 This is a schematic diagram of the fourth process of a method for quality inspection of drone aerial photography data according to one embodiment of this application. Detailed Implementation
[0025] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-5 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.
[0026] Currently, in the practice of UAV image data quality inspection, the following technical problems often exist: insufficient overlap between images leads to the loss of ground information; non-compliance with heading and lateral overlap affects the image stitching effect; and changes in UAV attitude lead to increased image tilt deviation. These problems not only reduce the practical value of the images but may also reduce the efficiency and reliability of the task in subsequent image processing and applications.
[0027] Based on this, this application discloses a method for quality inspection of drone aerial photography data.
[0028] Reference Figure 1 A method for quality inspection of drone aerial photography data, the quality inspection method including: Step S101: Obtain camera parameters, drone attitude data, and shooting position data of each shooting point on the flight path when the drone is flying on the flight path; Specifically, when drones are conducting aerial photography missions, camera parameters (such as focal length, pixel size, and camera sensor size) and attitude data (Yaw, Pitch, Roll) are key factors that determine the shooting quality and image coverage. Recording the location information of each shooting point facilitates subsequent calculation of ground coverage.
[0029] Yaw, Pitch, and Roll are three rotation angles used to describe the attitude of a drone, representing its direction and attitude in space. Their specific meanings are as follows: Yaw describes the drone's rotation about its vertical axis, representing its rotation in the horizontal plane, and is typically used to indicate changes in the drone's left or right direction; for example, yaw to the left or right changes the drone's orientation. Pitch describes the drone's rotation about its lateral axis, representing its tilt in the forward or backward direction; tilting up or down raises or lowers the drone's "nose," affecting its forward angle. Roll describes the drone's rotation about its longitudinal axis, representing its tilt in the left or right direction; for example, rolling to the left or right slightly lowers or raises the drone's left or right side, and is typically used for lateral balance.
[0030] For example, suppose the drone has three shooting points P1, P2 and P3 on flight path A, with a camera focal length of 35mm, a pixel size of 0.02mm and a frame size of 36x24mm; the attitude angle data are 30°, 30°, 5°, and 0° respectively.
[0031] Step S102: Convert the shooting position data of each shooting point on the flight path into corresponding planar coordinates, and determine the terrain height data corresponding to each shooting point based on the planar coordinates; In order to accurately locate the shooting location on the map, the geographic coordinates (latitude and longitude) need to be converted into planar coordinates; the acquisition of terrain height data can be based on a terrain database to ensure that the image coverage area matches the terrain.
[0032] For example, latitude and longitude can be converted into planar coordinates (X,Y) through Gaussian forward calculation or other geographic projection algorithms, and then a three-dimensional terrain database can be accessed to find the corresponding terrain height information (Z) based on the planar coordinates (X,Y).
[0033] Reference Figure 2 As shown, this is a schematic diagram of the terrain height determined by the plane coordinates. The geographic coordinates of P1 are (40.0°N, 116.0°E), which are converted to plane coordinates (X1, Y1). By searching the corresponding terrain database, the terrain height of P1 is found to be 300 meters.
[0034] Step S103: Calculate the rotation matrix based on the UAV attitude data; The attitude angles (Yaw, Pitch, Roll) describe the rotational state of the UAV during flight. Using the attitude angles, a rotation matrix (R) can be constructed, which transforms the camera coordinate system into the ground coordinate system, making the calculation of image coverage more accurate and able to accurately reflect the actual ground conditions.
[0035] Step S104: Based on terrain height data, rotation matrix and camera parameters, calculate the ground shooting coverage of each shooting point; After obtaining the terrain height, camera parameters, and rotation matrix, the actual projection range of the UAV shooting point on the ground can be derived through the collinearity condition equation. This range determines the coverage area of the image on the ground, which can provide a basis for subsequent overlap analysis and ensure that the image achieves complete coverage of the target area on the ground.
[0036] Step S105: Calculate the overlap data of each shooting point based on the ground shooting coverage of each shooting point on the flight path; wherein, the overlap data includes the directional overlap between adjacent shooting points on the flight path and the lateral overlap between the flight path and adjacent flight paths. The calculation of overlap data helps determine the coverage between adjacent images. By calculating the forward and lateral overlap, precise control over image overlap can be achieved to prevent insufficient coverage and ensure that the captured images meet the coverage requirements.
[0037] Step S106: Compare the overlap data of each shooting point with the preset overlap limit range to obtain the overlap comparison result; By comparing the actual overlap with the overlap tolerance range, it can be determined whether the image meets the preset quality standards. The preset overlap tolerance range ensures the coverage accuracy and stitching effect of the image. For example, within the preset overlap tolerance range, the forward overlap requirement is set to be greater than 70%, and the lateral overlap requirement is set to be greater than 65%. If the coverage areas of P1 and P2 overlap by 80% in the forward direction and 70% in the lateral direction, then the overlap of P1 and P2 meets the conditions; otherwise, it does not meet the requirements.
[0038] Understandably, identifying shooting points that do not meet quality standards provides a basis for image quality control.
[0039] Step S107: Based on the overlap comparison results, perform color rendering on the ground shooting coverage of each shooting point to obtain the rendered quality inspection graphic. The system uses color rendering to visually display the overlap status of different shooting points. Colors are assigned to different overlap areas based on the comparison results for graphical analysis. For example, areas meeting the overlap requirements are marked in green, while those not meeting the requirements are marked in red. This visually displays image quality and provides a clear and concise feedback by allowing inspectors to quickly assess shooting quality.
[0040] Step S108: Generate a first image quality inspection report based on the rendered quality inspection graphic and the overlap comparison results.
[0041] Specifically, the first image quality inspection report summarizes the overlap data, tolerance comparison, and graphical rendering results of all shooting points. This report facilitates a comprehensive quality assessment after the mission. For example, the first image quality inspection report might show that the overlap between P1 and P2 meets the requirements, but the overlap between P2 and P3 is insufficient, suggesting a reshoot. By providing a detailed quality assessment, the report helps drone operators make subsequent adjustments or reshoots, providing data support for efficiently completing the mission.
[0042] In the above embodiments, based on efficient quality inspection of UAV imagery data, key indicators such as ground coverage and overlap can be calculated quickly and accurately. Furthermore, intuitive quality feedback can be provided through graphical rendering and quality reports. This technical solution significantly improves the efficiency of UAV imagery quality inspection, ensuring the reliability and accuracy of imagery data, and is suitable for various terrain data mapping and geographic information acquisition scenarios.
[0043] As one implementation of step S103, the rotation matrix R is calculated based on the UAV attitude data. xyz The calculation formulas include: In the above formula, R is the roll angle (Roll), Y is the yaw angle (Yaw), and P is the pitch angle (Pitch); R xyz This is a rotation matrix used for spatial coordinate transformation based on the UAV's attitude data.
[0044] Specifically, the rotation matrix is the result of multiplying three basic rotation matrices, which correspond to rotations around the X, Y, and Z axes, respectively. Each term is a combination of sine or cosine functions of the corresponding angle, reflecting how rotational components in different directions interact to determine the final position and orientation.
[0045] Reference Figure 3 As one implementation of step S104, the ground shooting coverage area of each shooting point is calculated based on terrain height data, rotation matrix, and camera parameters: Step S201: Obtain the terrain height data, camera parameters, and rotation matrix R of the shooting point. xyz ; The camera parameters include the camera sensor size and the camera focal length f. Specifically, the camera sensor size includes the width w and the height h.
[0046] Step S202, set the shooting position data of the shooting point to (X s Y s Z s Set the terrain height data of the shooting point to Z. a ; Among them, the shooting location data (X) s Y s Z s () represents the coordinates of the camera exposure point in object space, and the terrain height data Z. a This indicates the ground elevation of the shooting location.
[0047] Step S203: Based on the camera sensor size, calculate the image coordinates (x, y) of each corner point in the camera sensor; where the image coordinates (x, y) represent the pixel coordinates of each corner point in the camera sensor, providing input for subsequent coordinate transformation.
[0048] Step S204, set the image coordinates (x, y) and rotation matrix R. xyz Terrain height data Z a Camera focal length f and shooting position data (X s Y s Z s Substituting these values into formulas (2), (3), and (4) below, the ground position coordinates (X, Y, F, Z) of each corner point are calculated. a Y a ); where each corner point includes the top left corner, top right corner, bottom left corner, bottom right corner, and center point; X a =X s +γ(a1*x+a2*y-a3*f) (3) X a =X s +γ(b1*x+b2*y-b3*f) (4) Step S205, based on the ground position coordinates (X) of each corner point a Y a Based on a three-dimensional terrain database, the actual terrain height at each corner point is determined. The three-dimensional terrain database stores the actual terrain height data for various locations on the ground. For example, the actual terrain height of the upper left corner is extracted as Z. A左上 =105 meters, the actual terrain height in the upper right corner is Z A右上 = 110 meters, the actual terrain height in the lower left corner is Z. A左下 =108 meters, the actual terrain height in the lower right corner is Z. A右下 = 112 meters, the actual terrain height of the center point is Z A中心 = 107 meters.
[0049] Step S206: Calculate the average of the actual terrain heights at each corner point to obtain the final ground terrain height Z corresponding to the shooting point. A ; Specifically, the final ground elevation Z at the shooting point is calculated using an average value. A This provides parameters for subsequent calculations of the actual ground location coordinates.
[0050] Step S207, the final ground terrain height Z A Substitute the values into formulas (5) and (6) below to calculate the actual ground coordinates (X) of each corner point. A Y A This allows us to obtain the ground coverage area of the shooting point. In the above formula, γ is the scaling factor, (x,y) are the image-side coordinates of the shooting point, f is the camera focal length, and X... S Y S Z S X is the location data of the shooting point. A Y A Z A a represents the actual coordinates of the shooting location; i b i c i Corresponding to rotation matrix R xyz The elements in the i-th row. These elements are nine direction cosines composed of the image's exterior azimuth elements (i.e., roll (R), yaw (Y), and pitch (P)).
[0051] The above implementation not only considers changes in terrain elevation to ensure more accurate ground coordinates for each point, but also reduces the impact of individual point measurement errors through multi-point averaging calculations, thereby improving data reliability and representativeness. In applications such as aerial photography, remote sensing, and geographic information systems, this technical solution can support more effective mission planning and data analysis, ensure the accuracy of the boundaries and center points of the shooting range, adapt to various complex terrain environments, and improve the overall reliability and efficiency of the mission.
[0052] As one implementation of step S203, the formula for calculating the image-side coordinates (x, y) of each corner point in the camera frame based on the camera frame size includes: The image coordinates of the upper left corner are: x = -w / 2, y = -h / 2(7); The image coordinates of the upper right corner are: x = w / 2, y = -h / 2 (8); The image coordinates of the lower left corner are: x = -w / 2, y = h / 2 (9); The image coordinates of the lower right corner are: x = w / 2, y = h / 2 (10); The image-side coordinates of the center point are: x = 0, y = 0 (11); In the above formula, h is the height of the camera sensor size, and w is the width of the camera sensor size.
[0053] Specifically, these coordinates represent the image coordinates of the four corners and the center point of the camera frame. The actual positions of these points in the object space can be calculated using the formula described above.
[0054] Reference Figure 4 As one implementation of step S105, the step of calculating the overlap data of each shooting point based on the ground shooting coverage of each shooting point on the flight path includes: Step S301: Calculate the heading overlap of each shooting point based on the intersection area of the ground projection coverage of each adjacent shooting point on the flight path. Forward overlap refers to the degree of image overlap between adjacent shooting points along the same flight path. The main purpose of calculating forward overlap is to ensure sufficient overlap between adjacent shooting points so that image stitching and 3D reconstruction can be performed smoothly in post-processing. Specifically, the calculation method is to determine the intersection area of the ground projection coverage of adjacent shooting points, and then divide this intersection area by the ground projection area of a single shooting point.
[0055] For example, suppose there are two adjacent shooting points A and B within a certain flight path, and their ground projection coverage areas are rectangles A1 and B1, respectively. If the intersection area of these two rectangles is S... 交 If the ground projection area of a single shooting point is S, then the formula for calculating the forward overlap is: Forward overlap = S 交 / S.
[0056] Step S302: Merge the ground shooting coverage areas of each shooting point on the flight path to obtain the aerial shooting area of the flight path; Understandably, in order to assess the coverage of the entire flight path, it is necessary to merge the ground-based coverage of all shooting points within the same flight path into a large aerial shooting area. This step is crucial for subsequent calculations of lateral overlap, as it provides the actual coverage of the entire flight path.
[0057] For example, the ground projection coverage areas (A1, B1, C1, etc.) of all shooting points (such as A, B, C, etc.) within the flight path are merged into a larger polygon P, which represents the aerial photography area of the entire flight path.
[0058] Step S303: Calculate the lateral overlap of adjacent shooting points between different routes based on the intersection area of the aerial photography area of the flight route and the aerial photography area of adjacent flight routes.
[0059] Lateral overlap refers to the degree of image overlap between adjacent shooting points on different flight paths. Its calculation method is similar to that of directional overlap, but it focuses on the overlap between different flight paths. By calculating the intersection area of the ground shooting coverage areas between adjacent flight paths, lateral overlap can be evaluated, thereby ensuring smooth connection between different flight paths.
[0060] For example, suppose there are two adjacent flight paths L1 and L2, whose combined ground imaging coverage areas are polygons P1 and P2, respectively. If the intersection area of these two polygons is S... 旁交 The formula for calculating lateral overlap is: Lateral overlap = S 旁交 / S; where S is the ground projection area of a single shooting point.
[0061] In the above embodiments, detailed overlap analysis of UAV aerial photography data not only ensures the continuity and consistency of images within the same flight path but also effectively assesses the connectivity between images from different flight paths. This precise overlap calculation method, combined with efficient ground-based coverage merging technology, significantly improves the quality control level of aerial photography data, providing a reliable foundation for subsequent data processing and applications.
[0062] Reference Figure 5 As a further implementation of the quality inspection method, the quality inspection method also includes: Step S401: Acquire image quality data for each shooting point along the flight path; wherein, the image data includes image yaw angle, image tilt angle, and image altitude hold. Image quality data includes image yaw angle, image tilt angle, and image altitude hold, all of which directly affect the quality of the final aerial data. Image yaw angle refers to the rotation angle of the image relative to the horizontal plane; image tilt angle refers to the angle between the image plane and the horizontal plane; and image altitude hold refers to the drone's ability to maintain a constant altitude during flight. Acquiring this data is to evaluate whether the image quality at each shooting point meets the standards.
[0063] Step S402: Compare the image quality data of each shooting point with the preset image tolerance range to obtain the image quality comparison result; The preset image tolerance ranges are image quality standards set according to industry standards or project requirements. By comparing the image quality data of each shooting point with these tolerance ranges, it can be determined whether the image quality of each shooting point meets the standards. Specifically, the preset image tolerance ranges include image yaw angle tolerance range, image tilt angle tolerance range, and image altitude hold tolerance range.
[0064] Step S403: Based on the image quality comparison results, perform color rendering on the ground shooting coverage of each shooting point to obtain the rendered image quality detection graphic. Specifically, based on the image quality comparison results, the ground coverage area of each shooting point is rendered in color, with different colors representing different quality levels. For example, green indicates acceptable quality, and red indicates unacceptable quality. This visualization method helps users intuitively understand the image quality status of each shooting point.
[0065] Step S404: Generate a second image quality inspection report based on the rendered image quality inspection graphic and the image quality comparison results.
[0066] The second image quality inspection report summarizes the image quality data (image yaw angle, image tilt angle, and image altitude, etc.) of all shooting points and their tolerance comparison results, including a color rendering map of the ground shooting coverage area for each shooting point. The purpose of generating this report is to provide a detailed quality assessment document for project management and decision-making.
[0067] In the above implementation method, a comprehensive quality inspection of the aerial photography data is conducted to ensure that the image quality at each shooting point meets the standards. By acquiring key parameters such as image yaw angle, image tilt angle, and image altitude hold, and comparing them with preset tolerance ranges, the image quality at each shooting point can be accurately evaluated. Furthermore, the quality assessment results are visually displayed using color rendering, helping users quickly identify potential problems. The final generated quality inspection report not only provides detailed data support but also offers important references for the overall quality control of the project.
[0068] In practical applications, this application enables rapid quality checks on the image position and attitude data recorded in real time during UAV flight. No additional data such as actual images are required. Simply import the UAV's flight area, image position, and attitude data after flight to check data such as image overlap, image yaw angle, image tilt angle, and flight altitude hold. It also provides an intuitive graphical interface analysis and quality check report.
[0069] This application also discloses a quality inspection system for drone aerial photography data.
[0070] A quality inspection system for drone aerial photography data, the quality inspection system comprising: The data acquisition module is used to acquire camera parameters, drone attitude data, and shooting location data of various shooting points along the flight path when the drone is flying on the flight path. The terrain height data generation module is used to convert the shooting position data of each shooting point on the flight path into corresponding planar coordinates, and determine the terrain height data corresponding to each shooting point based on the planar coordinates; The rotation matrix calculation module is used to calculate the rotation matrix based on the UAV attitude data. The coverage calculation module is used to calculate the ground shooting coverage of each shooting point based on terrain height data, rotation matrix and camera parameters; The overlap calculation module is used to calculate the overlap data of each shooting point based on the ground shooting coverage of each shooting point on the flight path; the overlap data includes the directional overlap between adjacent shooting points on the flight path and the lateral overlap between the flight path and adjacent flight paths. The overlap comparison module is used to compare the overlap data of each shooting point with the preset overlap limit range to obtain the overlap comparison result; The first rendering module is used to perform color rendering on the ground shooting coverage of each shooting point according to the overlap comparison results, and obtain the rendered quality inspection graphic. The quality inspection report generation module is used to generate a first image quality inspection report based on the rendered quality inspection graphics and the overlap comparison results.
[0071] In the above embodiments, by automatically, accurately and efficiently detecting the quality of UAV aerial photography data, the problems of inaccurate overlap calculation, insufficient consideration of terrain height influence, low data processing efficiency and lack of automation and intelligence in traditional methods can be effectively solved, thus significantly improving the quality and application effect of UAV aerial photography data.
[0072] As a further implementation of the quality inspection system, the quality inspection system also includes: The image data module is used to acquire image quality data from various shooting points along the flight path; the image data includes image yaw angle, image tilt angle, and image altitude hold. The image quality comparison module is used to compare the image quality data of each shooting point with the preset image tolerance range to obtain the image quality comparison result. The second rendering module is used to perform color rendering on the ground shooting coverage of each shooting point according to the image quality comparison results, and obtain the rendered image quality detection graphic. The quality inspection report generation module is also used to generate a second image quality inspection report based on the rendered image quality inspection graphics and image quality comparison results.
[0073] In the above implementation, by acquiring key parameters such as image yaw angle, image tilt angle, and image altitude hold, and comparing them with preset tolerance ranges, the image quality of each shooting point can be accurately evaluated. Furthermore, by using color rendering, the quality evaluation results are displayed intuitively, helping users quickly identify potential problems. The final generated quality inspection report not only provides detailed data support but also offers important reference for the overall quality control of the project.
[0074] The quality inspection system for UAV aerial photography data according to an embodiment of this application can implement any of the above-mentioned quality inspection methods, and the specific working process of each module in the quality inspection system can refer to the corresponding process in the above-mentioned method embodiments.
[0075] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a certain module is merely a logical functional division, and in actual implementation there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.
[0076] This application also discloses a computer device.
[0077] A computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for quality inspection of drone aerial photography data as described above.
[0078] This application also discloses a computer-readable storage medium.
[0079] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described above in any of the methods for quality inspection of drone aerial photography data.
[0080] The computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device; the program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.
[0081] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.
[0082] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.
Claims
1. A method for quality inspection of drone aerial photography data, characterized in that, The quality testing method includes: Acquire camera parameters, drone attitude data, and shooting position data of each shooting point along the flight path when the drone is flying on the flight path; The shooting location data of each shooting point on the flight route is converted into corresponding planar coordinates, and the terrain height data corresponding to each shooting point is determined based on the planar coordinates. The rotation matrix is calculated based on the UAV attitude data; Based on the terrain height data, rotation matrix, and camera parameters, the ground shooting coverage of each shooting point is calculated; based on the ground shooting coverage of each shooting point on the flight path, the overlap data of each shooting point is calculated; wherein, the overlap data includes the directional overlap between adjacent shooting points on the flight path and the lateral overlap between the flight path and adjacent flight paths. The overlap data of each shooting point is compared with the preset overlap limit range to obtain the overlap comparison result; Based on the overlap comparison results, the ground shooting coverage of each shooting point is color-rendered to obtain the rendered quality inspection graphic. A first image quality inspection report is generated based on the rendered quality inspection graphic and the overlap comparison result.
2. The method for quality inspection of UAV aerial photography data according to claim 1, characterized in that, Based on the UAV attitude data, the rotation matrix R is calculated. xyz The calculation formulas include: In the above formula, R is the roll angle (Roll), Y is the yaw angle (Yaw), and P is the pitch angle (Pitch); R xyz This is a rotation matrix used for spatial coordinate transformation based on the UAV's attitude data.
3. The method for quality inspection of UAV aerial photography data according to claim 2, characterized in that, Based on the terrain height data, rotation matrix, and camera parameters, the ground coverage area of each shooting point is calculated: Obtain the terrain height data, camera parameters, and rotation matrix R of the shooting point. xyz The camera parameters include the camera sensor size and the camera focal length f. Set the shooting location data of the shooting point as (X) s Y s Z s The terrain height data of the shooting point is set as Z. a ; Based on the camera sensor size, the image-side coordinates (x, y) of each corner point in the camera sensor are calculated. The image coordinates (x, y) and the rotation matrix R are used to... xyz Terrain height data Z a Camera focal length f and shooting position data (X s Y s Z s Substituting these values into formulas (2), (3), and (4), the ground coordinates (X, Y, X) of each corner point are calculated. a Y a ); where each of the corner points includes the top left corner, top right corner, bottom left corner, bottom right corner, and center point; X a =X s +γ(a1*x+a2*y-a3*f) (3) X a =X s +γ(b1*x+b2*y-b3*f) (4) Based on the ground position coordinates (X) of each corner point a Y a Based on a three-dimensional terrain database, the actual terrain height at each corner point is determined. The final ground terrain height Z corresponding to the shooting point is obtained by averaging the actual terrain heights of each corner point. A ; The final ground terrain height Z A Substitute the values into formulas (5) and (6) below to calculate the actual ground coordinates (X) of each corner point. A Y A ), thus obtaining the ground shooting coverage area of the shooting point; In the above formula, γ is the scaling factor, (x,y) are the image-side coordinates of the shooting point, f is the camera focal length, and X... S Y S Z S X is the location data of the shooting point. A Y A Z A a represents the actual coordinates of the shooting location; i b i c i Corresponding to rotation matrix R xyz The element in the i-th row.
4. The method for quality inspection of UAV aerial photography data according to claim 3, characterized in that, Each of the aforementioned corner points includes the top left corner, top right corner, bottom left corner, bottom right corner, and center point; Based on the camera sensor size, the formulas for calculating the image-side coordinates (x, y) of each corner point in the camera sensor include: The image coordinates of the upper left corner are: x = -w / 2, y = -h / 2 (7); The image coordinates of the upper right corner are: x = w / 2, y = -h / 2 (8); The image coordinates of the lower left corner are: x = -w / 2, y = h / 2 (9); The image coordinates of the lower right corner are: x = w / 2, y = h / 2 (10); The image-side coordinates of the center point are: x = 0, y = 0 (11); In the above formula, h is the height of the camera sensor size, and w is the width of the camera sensor size.
5. The method for quality inspection of UAV aerial photography data according to claim 1, characterized in that, The steps for calculating the overlap data of each shooting point based on the ground shooting coverage of each shooting point along the flight route include: The heading overlap of each shooting point is calculated based on the intersection area of the ground projection coverage of each adjacent shooting point on the flight path. The ground shooting coverage areas of each shooting point along the flight route are merged to obtain the aerial shooting area of the flight route. The lateral overlap of adjacent shooting points between different routes is calculated based on the intersection area of the aerial photography area of the stated route and the aerial photography area of adjacent routes.
6. A method for quality inspection of UAV aerial photography data according to any one of claims 1 to 5, characterized in that, The quality testing method further includes: Acquire image quality data for each shooting point along the flight path; the image data includes image yaw angle, image tilt angle, and image altitude hold. The image quality data of each shooting point is compared with the preset image tolerance range to obtain the image quality comparison results; Based on the image quality comparison results, the ground shooting coverage of each shooting point is color-rendered to obtain the rendered image quality detection graphic. A second image quality inspection report is generated based on the rendered image quality inspection graphic and the image quality comparison results.
7. A quality inspection system for drone aerial photography data, characterized in that, The quality inspection system includes: The data acquisition module is used to acquire camera parameters, drone attitude data, and shooting position data of each shooting point on the flight path when the drone is flying along the flight path. The terrain height data generation module is used to convert the shooting position data of each shooting point on the flight route into corresponding planar coordinates, and determine the terrain height data corresponding to each shooting point based on the planar coordinates. The rotation matrix calculation module is used to calculate the rotation matrix based on the UAV attitude data. The coverage calculation module is used to calculate the ground shooting coverage of each shooting point based on the terrain height data, rotation matrix and camera parameters; The overlap calculation module is used to calculate the overlap data of each shooting point based on the ground shooting coverage of each shooting point on the flight path; wherein, the overlap data includes the directional overlap between adjacent shooting points on the flight path and the lateral overlap between the flight path and adjacent flight paths. The overlap comparison module is used to compare the overlap data of each shooting point with the preset overlap limit range to obtain the overlap comparison result; The first rendering module is used to perform color rendering on the ground shooting coverage of each shooting point according to the overlap comparison result, so as to obtain the rendered quality inspection graphic. The quality inspection report generation module is used to generate a first image quality inspection report based on the rendered quality inspection graphic and the overlap comparison result.
8. The quality inspection system for UAV aerial photography data according to claim 7, characterized in that, The quality inspection system also includes: The image data module is used to acquire image quality data of each shooting point along the flight path; the image data includes image yaw angle, image tilt angle, and image altitude hold. The image quality comparison module is used to compare the image quality data of each shooting point with the preset image tolerance range to obtain the image quality comparison result. The second rendering module is used to perform color rendering on the ground shooting coverage of each shooting point according to the image quality comparison results, so as to obtain the rendered image quality detection graphic. The quality inspection report generation module is also used to generate a second image quality inspection report based on the rendered image quality inspection graphic and the image quality comparison result.
9. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as claimed in any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that: The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 6.