A method for calculating the scale of concealed ground fissures based on unmanned aerial vehicle remote sensing technology

Abstract

The present application relates to the technical field of surface crack survey, and particularly relates to a surface hidden crack scale calculation method based on unmanned aerial vehicle remote sensing technology, which comprises: basic terrain acquisition, obtaining point cloud data of the ground by using an unmanned aerial vehicle carrying a radar sensor, and processing the point cloud data to generate DEM; crack data acquisition, flying and shooting in the measured area by using an unmanned aerial vehicle carrying a near-infrared sensor to obtain infrared images of cracks, and extracting cracks in the infrared images according to the temperature difference between the cracks and surrounding objects; crack attribute extraction, analyzing the infrared images; and crack scale calculation, bringing the infrared sensor parameters, crack parameters in the images, flight parameters, terrain data and the like into a conversion formula to calculate the actual length of the cracks. The present application obtains infrared data of ground cracks, calculates crack scale parameters, and realizes large-range, long-distance and accurate identification of hidden cracks on the ground.

Description

Technical Field

[0001] This invention relates to the field of surface crack detection technology, and in particular to a method for calculating the scale of hidden surface cracks based on UAV remote sensing technology. Background Technology

[0002] In areas of surface subsidence caused by coal mining, numerous cracks exist due to mining activities. These cracks not only significantly impact the growth of surface vegetation and the safety of surface structures, but also easily allow water-conducting fissures in the goaf roof to extend to the surface, posing hazards to underground ventilation, fire prevention, and waterproofing. Therefore, the investigation and research of these cracks is of great importance. Currently, monitoring methods for ground fissures typically involve direct measurement, primarily including leveling and GPS observation. However, these methods rely on specialized measuring instruments, have limited detection ranges, require manual on-site operation, are inefficient, and are affected by topographical factors. Although satellite remote sensing technology has been extensively studied in the field of ground fissure detection, its resolution and cost limit its widespread application for small and concealed cracks. Summary of the Invention

[0003] This invention proposes a method for calculating the scale of hidden surface cracks based on UAV remote sensing technology, which solves the problems of high difficulty and danger in manually measuring ground cracks in existing technologies.

[0004] A method for calculating the scale of hidden surface cracks based on UAV remote sensing technology includes:

[0005] S1. Basic terrain data acquisition: Use a drone equipped with a radar sensor to acquire point cloud data of the ground, and process the point cloud data to generate a DEM;

[0006] S2. Crack data acquisition: A drone equipped with a near-infrared sensor is used to fly and take pictures in the area to obtain infrared images of cracks. Cracks are extracted from the infrared images based on the temperature difference between the cracks and the surrounding ground features.

[0007] S3. Crack attribute extraction: Analyze the infrared image to obtain the number of pixels n occupied by the crack in the image and the clockwise angle ζ between the crack and the aircraft's heading. The clockwise angle between the aircraft's heading and the north direction is... The angle y between the crack and the north direction is...

[0008] S4. Crack Scale Calculation: The infrared sensor parameters, crack parameters in the image, flight parameters, and terrain data are input into a conversion formula to calculate the actual crack length D. The infrared sensor parameters include focal length f and pixel size a. The crack parameters include the number of pixels n occupied by the crack in the image and the angle γ between the crack and the north direction. The terrain data includes the slope β, aspect i, and elevation H2 at the crack location. The flight parameters include flight altitude H1.

[0009] Preferably, the elevation H2 of each point is obtained in S1, and then the slope and aspect are analyzed on the DEM to obtain the slope and aspect model, thus obtaining the slope and aspect at any location.

[0010] Preferably, in S2, the attributes of the infrared image include the latitude and longitude and flight altitude H1 at the moment of shooting. The latitude and longitude are input into the slope and aspect model to obtain the slope and aspect of a certain point.

[0011] Preferably, if the elevation of the DEM is H2, then the height h of the UAV relative to the ground is: h = H1 - H2.

[0012] Preferably, S4 includes: the horizontal length of the crack's actual length is D′, and its length in the image is d. Let be the direction of the normal to the slope. According to the law of sines, we have:

[0013] The expression for the actual length D of the crack is:

[0014] According to the similarity theorem, we can obtain:

[0015] The expression for the length D′ of the crack in the horizontal plane is:

[0016] Based on the right triangle relationship, α can be expressed as:

[0017] The value of d is obtained from the number of pixels n occupied by the crack: d = na;

[0018] According to the projection relationship, we have:

[0019] The expression for θ is:

[0020] Substituting the expressions for D′, a, d, and h into the expression for D, we obtain the formula for calculating the crack size.

[0021] Compared with existing technologies, the beneficial effects of this invention are: based on the theory of thermal infrared imaging, infrared data of ground fissures are acquired, and the scale parameters of the fissures are calculated, enabling large-scale, long-distance, and accurate identification of hidden fissures on the ground surface; the advantages of infrared sensors in accurately acquiring the attributes of ground fissures and lidar in efficiently collecting basic data are effectively combined, achieving the effect of accurately measuring fissure parameters. The measurement method is simple and efficient, reducing the intensity and cost of manual labor and improving the level of intelligent production management. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the technical process of the present invention.

[0023] Figure 2 This is a schematic diagram of the first angular parameters for imaging the crack in the image;

[0024] Figure 3 A schematic diagram of the second-degree parameters for imaging the crack in the image.

[0025] Figure 4 This is a schematic diagram of the third angle parameters for imaging the crack in the image;

[0026] In the diagram, A indicates the direction the aircraft is facing, B indicates north, and D indicates east. Detailed Implementation

[0027] The specific embodiments of the present invention will be further described below with reference to specific examples:

[0028] A method for calculating the scale of hidden surface cracks based on UAV remote sensing technology, such as Figure 1 ,include:

[0029] S1. Basic terrain data acquisition: Use a drone equipped with a radar sensor to acquire point cloud data of the ground, and process the point cloud data to generate a DEM;

[0030] S2. Crack data acquisition: Using a drone equipped with a near-infrared sensor to fly and take pictures in the measured area to obtain infrared images of cracks. Cracks are extracted from the infrared images based on the temperature difference between the cracks and the surrounding ground objects. Using a drone equipped with a thermal infrared sensor can grasp the distribution pattern of cracks on a macro scale.

[0031] S3. Crack attribute extraction: Analyze the infrared image to obtain the number of pixels n occupied by the crack in the image and the clockwise angle ζ between the crack and the aircraft's heading. The clockwise angle between the aircraft's heading and the north direction is... The angle y between the crack and the north direction is...

[0032] S4. Crack Scale Calculation: Introducing infrared sensor parameters, crack parameters in the imagery, flight parameters, and terrain data into the conversion formula, the actual crack length D is calculated. The specific meanings of the infrared sensor parameters are as follows: Figure 2 , Figure 3 and Figure 4 ,

[0033] The infrared sensor parameters include focal length f and pixel size a; the crack parameters include the number of pixels n occupied by the crack in the image and the angle γ between the crack and the north direction; the terrain data includes the slope β, aspect i, and elevation H2 at the crack; and the flight parameters include flight altitude H1.

[0034] The elevation H2 of each point is obtained in S1. Then, the slope and aspect are analyzed on the DEM to obtain the slope and aspect model. The slope and aspect at any location are obtained. The slope refers to the flatness of the terrain. The larger the value, the steeper the terrain. The aspect is the angle between the projection of the normal of the slope onto the horizontal plane and the north direction. It is measured in a clockwise direction and the range is between 0° and 360°.

[0035] In S2, the attributes of the infrared image include the latitude and longitude and flight altitude H1 at the moment of shooting. The latitude and longitude are input into the slope and aspect model to obtain the slope and aspect of a certain point.

[0036] Let the elevation of the DEM be H2, then the height h of the UAV relative to the ground is: h = H1 - H2.

[0037] S4 includes: the actual horizontal length of the crack is D′, and its length in the image is d. Let be the direction of the normal to the slope. According to the law of sines, we have:

[0038] The expression for the actual length D of the crack is:

[0039] According to the similarity theorem, we can obtain:

[0040] The expression for the length D′ of the crack in the horizontal plane is:

[0041] Based on the right triangle relationship, α can be expressed as:

[0042] The value of d is obtained from the number of pixels n occupied by the crack: d = n·a;

[0043] According to the projection relationship, we have:

[0044] The expression for θ is:

[0045] Substituting the expressions for D′, α, d, and h into the expression for D, we obtain the formula for calculating the crack size.

[0046] In this embodiment, a surface subsidence area in a mining area was selected as the test area. First, radar data was collected to obtain basic data for the test area. Then, an infrared imagery-equipped UAV was used to collect data on randomly selected cracks. By processing the crack images and combining them with the radar data, the following results were obtained: the angle γ between the crack width direction and the north direction was 90.5°, n was 2, the lens focal length f was 19mm, the pixel size a was 17μm, the UAV's flight altitude H1 was 1442.7m, and the surface elevation H2 of the crack was 1434.8m. Therefore, the height relative to the surface h = H1 - H2 = 7.9m, the slope i was 270.3°, and the slope β was 36.5°. These values ​​were then substituted into the formula for calculating the crack scale.

[0047]

[0048] Through on-site measurement of the crack, the actual width of the crack was found to be 11.6 cm. In this example, the result obtained by the algorithm proposed in this invention differs from the actual situation by 6.5 mm, with a relative error of 5.6%, indicating that the result is relatively reliable.

[0049] Based on the imaging principle of thermal infrared, this invention can clearly visualize cracks that are covered by vegetation and difficult to see with the naked eye in infrared images. For long and narrow cracks, the method of this invention allows for a comprehensive understanding of the crack's morphology from the air, avoiding the incomplete observation of long and narrow cracks during manual measurement. Furthermore, leveraging the advantages of drones—unrestricted by terrain and high speed—it can quickly acquire parameters for individual cracks in a crack cluster. Therefore, by using the method of this invention to collect crack data, crack parameters can be obtained rapidly, thereby reducing costs and improving accuracy.

[0050] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for calculating the scale of hidden surface cracks based on UAV remote sensing technology, characterized in that, include: S1. Basic terrain data acquisition: Use a drone equipped with a radar sensor to acquire point cloud data of the ground, and process the point cloud data to generate a DEM; S2. Crack data acquisition: A drone equipped with a near-infrared sensor is used to fly and take pictures in the area to obtain infrared images of cracks. Cracks are extracted from the infrared images based on the temperature difference between the cracks and the surrounding ground features. S3. Crack attribute extraction: Analyze the infrared image to obtain the parameters of the crack in the image, including the number of pixels it occupies. and the clockwise angle between the crack and the direction of the machine head The angle between the nose of the aircraft and the clockwise direction of north is... The angle between the crack and the north direction is... for ; S4. Crack Scale Calculation: By inputting infrared sensor parameters, crack parameters in the image, flight parameters, and terrain data into the conversion formula, the actual length of the crack is calculated. The infrared sensor parameters include focal length. Pixel size The parameters of a crack in an image: the number of pixels the crack occupies in the image. and the angle between the crack and the north direction Topographic data includes the slope at the crack. Slope aspect and elevation Flight parameters include flight altitude , .

2. The method for calculating the scale of hidden surface cracks based on UAV remote sensing technology as described in claim 1, characterized in that, The elevation of each point is obtained in S1. Then, slope and aspect analysis is performed on the DEM to obtain the slope and aspect model, and the slope and aspect at any location are obtained.

3. The method for calculating the scale of hidden surface cracks based on UAV remote sensing technology as described in claim 2, characterized in that, In S2, the attributes of the infrared image include the latitude, longitude, and altitude at the moment of capture. By inputting latitude and longitude into the slope and aspect model, the slope and aspect of a certain point can be obtained.

4. The method for calculating the scale of hidden surface cracks based on UAV remote sensing technology as described in claim 3, characterized in that, Let the elevation of the DEM be... The drone's altitude relative to the ground for: .

5. The method for calculating the scale of hidden surface cracks based on UAV remote sensing technology as described in claim 4, characterized in that, S4 includes: The actual length of the crack is the horizontal length. Its length in the image is , Let be the direction of the normal to the slope. According to the law of sines, we have: ; The actual length of the crack The expression is: ; According to the similarity theorem, we can obtain: ; Crack length in the horizontal plane The expression is: ; Based on the relationship of right triangles Represented as: ; Based on the number of pixels occupied by the crack Seek : ; According to the projection relationship, we have: ; Seek The expression is: ; Will , , , Substitute the expression From the expression, the formula for calculating the crack size is obtained.

Images