A method for measuring the shape of a roadway surrounding rock crack

Abstract

The application discloses a kind of roadway surrounding rock fissure form measurement methods, comprising the following steps: S1, install photographic surveying device in roadway, obtain the continuous digital image of the surrounding rock region of measured roadway;S2, the structural plane of roadway surrounding rock is carried out image acquisition, obtains color original drawing, and color original drawing is converted into gray scale image;S3, the gray scale image is divided into multiple local areas, establishes local area gray scale histogram, equalizes the gray scale histogram of local area, forms enhanced gray scale image;S4, to enhanced gray scale image is adaptively gamma correction;S5, using median filter to enhanced gray scale image is smoothed;S6, using Sobel operator to enhanced gray scale image is carried out fissure contour extraction;S7, the real length, real width of roadway surrounding rock structural plane fissure, fissure dip angle are calculated.The application can realize the safe, direct, efficient, accurate acquisition of coal mine roadway surrounding rock fissure image and the rapid identification extraction of surrounding rock fissure parameter.

Description

Technical Field

[0001] This invention relates to the field of engineering measurement technology for the surrounding rock structure of coal mine roadways, and in particular to a method for measuring the fracture morphology of the surrounding rock in roadways. Background Technology

[0002] Under the combined effects of mining-induced stress and original ground stress, coal mine roadway excavation faces are prone to frequent safety accidents such as roof falls, rock bursts, and rock blasts. The initial manifestation of these safety accidents is the generation and development of structural fissures in the surrounding rock. Therefore, timely detection and prediction of fissure development in the surrounding rock are indispensable cornerstones for achieving safe and efficient production in the coal mining industry, and also important means to prevent underground safety accidents and improve economic efficiency.

[0003] Currently, underground coal mining industries commonly employ measurement methods such as line surveying, three-dimensional laser scanning technology, and precision surveying to accurately identify fractures in the surrounding rock structure of roadways. However, these methods also have the following shortcomings or defects:

[0004] ① The surveying method relies on manual measurement, which results in a low safety factor and low efficiency in actual operation;

[0005] ②The three-dimensional laser scanning technology is too slow to obtain data on the fractures in the surrounding rock of the tunnel, and the data is lengthy and difficult to analyze;

[0006] ③ The precision measurement method requires a lot of manpower and resources, making it difficult to apply to practical operations on the production site, and its practicality is poor.

[0007] Therefore, there is an urgent need to provide a method for measuring the fracture morphology of surrounding rock in coal mine roadways, so as to achieve safe, direct, efficient and accurate acquisition of the fracture morphology of surrounding rock in coal mine roadways, and provide strong support for safe production in coal mines. Summary of the Invention

[0008] To address the problems existing in the prior art, this invention provides a method for measuring the fracture morphology of roadway surrounding rock, the specific technical solution of which is as follows:

[0009] A method for measuring the fracture morphology of surrounding rock in roadways includes the following steps:

[0010] S1, Install a photogrammetric device in the roadway, adjust the spatial parameters of the photogrammetric device in the roadway, and obtain continuous digital images of the surrounding rock area of ​​the roadway to be measured;

[0011] S2, using the photogrammetry device, images of the structural surfaces of the surrounding rock in the tunnel are acquired, a color original image is obtained, and the color original image is converted into a grayscale image;

[0012] S3, divide the grayscale image into multiple local regions, establish local region grayscale histograms, equalize the grayscale histograms of the local regions, and merge the equalized results to form an enhanced grayscale image;

[0013] S4, perform adaptive gamma correction on the enhanced grayscale image;

[0014] S5 uses a median filter to smooth the enhanced grayscale image;

[0015] S6. Use the Sobel operator to extract the crack contour of the enhanced grayscale image;

[0016] S7 calculates the true length, true width, and fracture dip angle of the structural surface cracks in the surrounding rock of the tunnel.

[0017] Furthermore, the spatial parameters of the photogrammetric device within the tunnel include the working distance, photographic height, width of the visible rock surface area, and field of view; the working distance is the vertical distance between the center point of the camera lens and the surrounding rock of the tunnel to be measured; the photographic height is the height between the center point of the camera lens and the tunnel floor; the formula for calculating the width of the visible rock surface area is:

[0018]

[0019] In the formula, W f ... f D represents the height of the visible rock surface area, in mm. w F represents the working distance for photography, in mm; F ​​represents the focal length of the camera lens, in mm.

[0020] The field of view L f The calculation formula is:

[0021] In the formula, L f For field of view, mm 2 S d The size of the camera projection sensor is the product of its length *a* and width *b*, in mm. 2 ;D w F represents the working distance for photography, in mm; F ​​represents the focal length of the camera lens, in mm.

[0022] Furthermore, the steps to convert a color original image to a grayscale image are as follows:

[0023] Use MATLAB to obtain the R, G, and B component values ​​of each pixel in the original color image, convert the R, G, and B component values ​​into individual grayscale values, and use the grayscale values ​​to replace the R, G, and B component values ​​of pixels in the original color image's bitmap image.

[0024] Furthermore, the grayscale conversion formula is as follows:

[0025] I(x,y)=0.299×I r (x,y)+0.587×I g (x,y)+0.114×I b (x,y)

[0026] In the formula, I(x,y) is the gray value corresponding to the pixel in the original image; r (x,y), I g (x,y), I b (x,y) represents the RGB values ​​of the pixels in the original image.

[0027] Furthermore, a cumulative distribution function is calculated for the gray-level histogram of the local region to identify the total frequency of the local region from the lowest gray-level value to the current gray-level value; the formula for calculating the cumulative distribution function is:

[0028]

[0029] In the formula, CDF(i) is the cumulative distribution function of the gray-level histogram; P(j) is the pixel proportion of gray-level value j in the gray-level histogram.

[0030] Furthermore, by transforming each pixel in the local region through the cumulative distribution function, its gray value is remapped to the equalized value, thereby equalizing the gray histogram of the local region. Specifically, the CDF is normalized to the range [0, L-1], where L is the gray level (8-bit image, L = 256), then: NewValue = round(CDF(i) × (L-1)).

[0031] Furthermore, the grayscale image is divided into multiple local regions using MATLAB, and the size of the local regions is preferably set to 5×5; the grayscale values ​​of the pixels in each local region are obtained, and a grayscale histogram corresponding to the local region is established using MATLAB.

[0032] Furthermore, the transformation formula for adaptive gamma correction is:

[0033]

[0034] In the formula, GC(r) k ) represents the adaptive gamma correction normalization transformation; r k r represents the k-th gray level of the input. k maxγ is the maximum gray level of the input; γ is an adjustment parameter: γ<1 maps the input gray level to a higher gray level range, which brightens the image, while γ>1 has the opposite effect, which darkens the image.

[0035] Furthermore, the cumulative distribution function is used for adaptive image correction of the enhanced grayscale image, and the weighted distribution function is used to fine-tune the grayscale histogram of local regions; the expression of the weighted distribution function is as follows:

[0036] γ = 1 - cdf w (r k )

[0037] in,

[0038]

[0039] therefore,

[0040]

[0041] Right now,

[0042]

[0043] In the formula,

[0044] γ is the weight distribution function; r k It is the k-th gray level within a local region; pdf max It is the maximum value of the grayscale probability density function; pdf min It is the minimum value of the gray-level probability density function; α is the adjustment parameter; r j It is the j-th gray level within a local region; pdf w (r j ) is the maximum value of the gray probability density function of the j-th level.

[0045] Furthermore, the Sobel operator includes the following steps:

[0046] First, define the Sobel convolution kernel:

[0047]

[0048] In the formula, G x For horizontal convolution kernels; G y It is a vertical convolution kernel.

[0049] Second, the smoothed image is convolved using a Sobel kernel to obtain the gradients in the x and y directions:

[0050]

[0051] In the formula, G x(x,y) represents the gradient in the x-direction; G y (x,y) represents the gradient in the y-direction; G x (i,j) represents the gradient of the convolution kernel in the x-direction at positions i and j; G y (i,j) represents the y-direction gradient of the convolution kernel at positions i and j; g (x+i,y+j) represents the pixel value at position (x+i,y+j) in the smoothed grayscale image.

[0052] Furthermore, non-maximum suppression is performed on the enhanced grayscale image, including the following steps:

[0053] First, the gradient magnitude G(x,y) and direction θ(x,y) of each pixel in the enhanced grayscale image are calculated using the following formula:

[0054]

[0055] In the formula, G(x,y) is the gradient magnitude of each pixel in the enhanced grayscale image; G x (x,y) represents the gradient in the x-direction; G y (x,y) represents the gradient in the y-direction; θ(x,y) represents the gradient direction of each pixel in the enhanced grayscale image.

[0056] Second, non-maximum suppression is performed on the enhanced grayscale image. Specifically, the indices of two adjacent pixels in the gradient direction are set as p and q. Neighboring pixels in the G(x,y) direction are selected for comparison. If G(x,y) < G(p,q), then G(x,y) is suppressed and made to 0; otherwise, G(x,y) is retained.

[0057] Furthermore, edge extraction is performed on the enhanced grayscale image, with a high threshold T set. h and low threshold T l Specifically: if G(x,y)>T h If G(x,y) < T, then mark it as an edge point E(x,y) = 1; if G(x,y) < T l If T is not an edge point, then it is marked as a non-edge point E(x,y) = 0; l <G(x,y)<T h Then, the connectivity is used to determine the connection: if this pixel is connected to any pixel that has been marked as an edge, then E(x,y) = 1; otherwise, set it to E(x,y) = 0.

[0058] Furthermore, the calculation of the true length, true width, and dip angle of the structural surface fractures in the surrounding rock of the tunnel includes the following steps:

[0059] First, define the actual length of the structural surface crack in the surrounding rock of the tunnel as l, the width as u, the pixel length as l1, and the pixel width as u1. This can be obtained through the following formula:

[0060]

[0061] In the formula, l is the actual length of the structural surface crack in the surrounding rock of the tunnel, mm; u is the actual width of the structural surface crack in the surrounding rock of the tunnel, mm; l1 is the pixel length of the structural surface crack in the surrounding rock of the tunnel, mm; u1 is the pixel width of the structural surface crack in the surrounding rock of the tunnel, mm; P s D represents the distance on the sensor as a unit pixel distance; w F represents the working distance for photography, in mm; F ​​represents the focal length of the camera lens, in mm.

[0062] Second, the pixel connection line of the enhanced grayscale image of the cracks in the surrounding rock structure of the tunnel is approximated as a straight line, and the dip angle of the cracks in the surrounding rock structure of the tunnel is calculated based on the linear regression algorithm.

[0063] The regression formula for the linear regression algorithm is:

[0064] y = cx + d

[0065]

[0066] In the formula, y represents the dependent variable, which is the target variable that needs to be predicted or explained; x represents the independent variable, which is the factor or predictor that affects y. and These are the average coordinates of the pixel skeleton points along the pixel connection line; x i With y i , where are the x and y coordinates of the i-th point, respectively; N is the number of fracture skeleton points; c is the regression coefficient; and d is the intercept of the regression equation.

[0067] The formula for calculating the fracture dip angle of the surrounding rock structural surface in a roadway is:

[0068] δ = arctanc.

[0069] In the formula, δ is the fracture dip angle of the surrounding rock structure of the tunnel; c is the regression coefficient.

[0070] Based on the above technical solution, the present invention has the following beneficial effects:

[0071] 1. Based on high-precision cameras and precise spatial positioning, the system can identify fractures in the entire space of the surrounding rock in the tunnel, and accurately determine the development status of the fractures.

[0072] 2. The grayscale image is divided into regions, and the Sobel operator is used to quickly find the outer contour line of each connected region. The image is then smoothed to improve image accuracy and avoid the redundancy and inefficiency of traditional algorithms.

[0073] 3. Extract the underlying mechanistic information from a large amount of surrounding rock monitoring data to provide a reference for the prevention and control of disasters such as roof collapse and rock bursts in roadways.

[0074] 4. By monitoring and analyzing the deformation of the surrounding rock, potential safety hazards can be identified in a timely manner, providing a strong guarantee for safe production in coal mines. Simultaneously, it can provide a scientific basis for roadway support design and construction scheme optimization, further improving the economic and social benefits of coal mining.

[0075] 5. By combining digital photogrammetry with digital image processing, we can achieve safe, direct, efficient, and accurate acquisition of images of rock fissures in coal mine roadways and rapid identification and extraction of rock fissure parameters. Attached Figure Description

[0076] Figure 1 : A schematic diagram of the technical route of the method described in this invention;

[0077] Figure 2 Schematic diagram of the layout of a photogrammetry device for fractures in the surrounding rock of a coal mine roadway;

[0078] Figure 3 : A schematic diagram illustrating the specific implementation steps of the method described in this invention. Detailed Implementation

[0079] It should be noted that:

[0080] 1. Certain terms are used in the specification and claims to refer to specific components or structures. Those skilled in the art will understand that different terms may be used to refer to the same component or structure. This specification and claims do not distinguish components or structures based on differences in terminology, but rather on differences in their functions.

[0081] 2. Unless otherwise explicitly specified and limited, the terms "installation," "setting," "connection," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0082] 3. Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components.

[0083] The following is in conjunction with the appendix Figure 1 -Appendix Figure 3 The present invention will be described in detail by way of examples.

[0084] This embodiment describes a method for measuring the fracture morphology of surrounding rock in a roadway, which specifically includes the following steps.

[0085] S1. Install a photogrammetric device in the tunnel and acquire or set the spatial parameters of the photogrammetric device in the tunnel, including but not limited to the working distance, height, width of the visible rock surface area, and field of view.

[0086] As attached Figure 2 As shown, the main body of the photogrammetry device consists of a high-precision explosion-proof digital camera 1 and a laser rangefinder 2, and is equipped with lighting equipment 3 such as fill lights and diffused lights, and adjustable tripods 4, etc., to ensure sufficient and uniform light source during photography / videography, avoid shadows and reflections, and adjust the height of the camera and rangefinder relative to the tunnel floor and the distance relative to the surrounding rock of the tunnel according to the actual conditions of the tunnel.

[0087] When installing a photogrammetric device, in order to obtain a clear and complete image of the surrounding rock surface of the tunnel, it is preferable to install the photogrammetric device in a location close to the center of the tunnel.

[0088] To ensure efficient, rapid, and accurate measurement, and to obtain continuous digital images of large areas of the surrounding rock in the tunnel, it is necessary to appropriately set the photographic working distance, photographic height, width of the visible rock surface area, and field of view. The specific steps are as follows:

[0089] S101. Using measuring tools such as rulers or laser rangefinders, the working distance between the measuring object and the photogrammetric device, as well as the photogrammetric height of the photogrammetric device from the roadway floor, are obtained.

[0090] Taking digital cameras as an example, the working distance for photography refers to the vertical distance between the center point of the camera lens and the object being measured, while the photographic height refers to the height distance between the center point of the camera lens and the floor of the tunnel.

[0091] Preferably, the photogrammetric device is positioned along the central axis and waistline of the tunnel. This configuration facilitates the measurement of the structural parameters of the surrounding rock on both sides of the tunnel, allowing for the acquisition of continuous digital images of large areas of the surrounding rock, resulting in a more complete digital image of the tunnel's surrounding rock.

[0092] ①The formula for calculating the working distance for photography is:

[0093]

[0094] In the formula,

[0095] D W Working distance for photography, in mm;

[0096] D h The width of the tunnel is in mm;

[0097] ②The formula for calculating the photographic height is:

[0098]

[0099] In the formula,

[0100] H W Photograph height, mm;

[0101] H h The height of the tunnel is in mm.

[0102] S102, determine the reasonable and applicable shooting range of the surrounding rock of the tunnel to be tested, including but not limited to the width of the visible rock surface area and the field of view of the surrounding rock of the tunnel.

[0103] ①The formula for calculating the width of the visible rock surface area of ​​the surrounding rock in the tunnel is:

[0104]

[0105] In the formula,

[0106] W f Width of the visible rock face area of ​​the tunnel surrounding rock, in mm;

[0107] 'a' represents the length of the camera projection sensor, in mm.

[0108] b is the width of the camera projection sensor, in mm;

[0109] H f Height of the visible rock surface area, in mm;

[0110] The formula for calculating the height of the visible rock surface area is:

[0111]

[0112] In the formula,

[0113] H f is the height of the visible rock surface area, in mm;

[0114] 'a' represents the length of the camera projection sensor, in mm.

[0115] Dw Working distance for photography, in mm;

[0116] F represents the camera lens focal length, in mm.

[0117] ② Field of view L f The calculation formula is:

[0118]

[0119] In the formula,

[0120] L f For field of view, mm 2 ;

[0121] S d The size of the camera projection sensor is the product of its length *a* and width *b*, in mm. 2 ;

[0122] D w Working distance for photography, in mm;

[0123] F represents the camera lens focal length, in mm.

[0124] S2, based on the spatial position of the photogrammetric device determined in step S1, images of the structural surface of the surrounding rock of the tunnel are acquired to obtain a color original image; the acquired original image data is transmitted to the computer in real time through a wireless communication protocol, and the color original image is converted into a grayscale image.

[0125] The specific steps are as follows:

[0126] Use MATLAB to obtain the R, G, and B component values ​​of each pixel in the original color image, convert the R, G, and B component values ​​into individual grayscale values, and use the grayscale values ​​to replace the R, G, and B component values ​​of pixels in the original color image's bitmap image.

[0127] The formula for converting grayscale values ​​is:

[0128] I(x,y)=0.299×I r (x,y)+0.587×I g (x,y)+0.114×I b (x,y)

[0129] In the formula,

[0130] I(x,y) is the gray value corresponding to the pixel in the original image;

[0131] I r (x,y), I g (x,y), I b (x,y) represents the RGB values ​​of the pixels in the original image.

[0132] Converting a color image to grayscale preserves the brightness information of the original image while reducing interference from color information.

[0133] S3 uses local mean filtering to denoise the grayscale image, obtaining an enhanced grayscale image.

[0134] Specifically, the steps include the following:

[0135] S301, use MATLAB to divide the grayscale image into multiple local regions, and preferably set the size of the local regions to 5×5.

[0136] Subdividing grayscale images into regions can prevent the formation of large areas of high or low brightness concentration and suppress uneven brightness distribution.

[0137] S302, obtain the grayscale values ​​of pixels in each local region, and use MATLAB to build the corresponding grayscale histogram of the local region.

[0138] A grayscale histogram can visually display the frequency of each grayscale level within a local area.

[0139] S303, calculate the cumulative distribution function for the gray-level histogram of each local region, thereby representing the total frequency of the local region from the lowest gray-level value to the current gray-level value.

[0140] The formula for calculating the cumulative distribution function is:

[0141]

[0142] In the formula,

[0143] CDF(i) is the cumulative distribution function of the gray-level histogram;

[0144] P(j) is the proportion of pixels with gray value j in the gray-level histogram.

[0145] S304 transforms each pixel in the local region using the cumulative distribution function, remapping its grayscale value to the equalized value, thereby equalizing the grayscale histogram of the local region.

[0146] Normalize the CDF to the range [0, L-1], where L is the number of gray levels (8-bit image, L = 256), then: NewValue = round(CDF(i) × (L-1)).

[0147] S305 merges the results of equalizing all local regions to form the enhanced grayscale image.

[0148] S4 performs adaptive gamma correction on the enhanced grayscale image to improve the image brightness and enhance the difference between the rock surface and fissures of the surrounding rock in the tunnel.

[0149] Specifically, the following steps are included:

[0150] S401, based on traditional gamma correction, performs adaptive gamma correction transformation. The normalized form of its transformation formula is GC(r). k )for:

[0151]

[0152] In the formula,

[0153] GC(r k () is an adaptive gamma correction normalization transformation;

[0154] r k The input is the k-th gray level;

[0155] r k max The maximum grayscale value input;

[0156] γ is an adjustment parameter: γ<1 maps the input grayscale to a higher grayscale range, which brightens the image, while γ>1 has the opposite effect, which darkens the image.

[0157] S402 uses the cumulative distribution function to perform adaptive correction of the enhanced grayscale image, and uses the weighted distribution function to fine-tune the grayscale histogram of the local area to avoid the enhanced grayscale image being too bright or too dark.

[0158] The weight distribution function is expressed as follows:

[0159] γ = 1 - cdf w (r k )

[0160] in,

[0161]

[0162] therefore,

[0163]

[0164] Right now,

[0165]

[0166] In the formula,

[0167] γ is the weight distribution function;

[0168] r jIt is the j-th gray level within a local area;

[0169] pdf max It is the maximum value of the gray-scale probability density function;

[0170] pdf min It is the minimum value of the gray-scale probability density function;

[0171] α is an adjustment parameter;

[0172] r j It is the j-th gray level within a local area;

[0173] pdf w (r j ) is the maximum value of the gray probability density function of the j-th level.

[0174] S5, Select a 3×3 median filter to smooth the enhanced grayscale image. S6, Use the Sobel operator to extract the crack contours from the enhanced grayscale image.

[0175] Specifically, the following steps are included:

[0176] S601, Define the Sobel convolution kernel:

[0177]

[0178] In the formula,

[0179] G x It is a horizontal convolution kernel;

[0180] G y It is a vertical convolution kernel.

[0181] S602, the smoothed grayscale image is convolved using a Sobel kernel to obtain the gradients in the x and y directions:

[0182]

[0183] In the formula,

[0184] G x (x,y) represents the gradient in the x-direction;

[0185] G y (x,y) represents the gradient in the y-direction;

[0186] G x (i,j) represents the gradient of the convolution kernel in the x-direction at positions i and j;

[0187] G y (i,j) represents the gradient of the convolution kernel in the x-direction at positions i and j;

[0188] I g (x+i,y+j) represents the pixel value at position (x+i,y+j) in the smoothed grayscale image.

[0189] S603, calculate the gradient magnitude G(x,y) and direction θ(x,y) of each pixel in the enhanced grayscale image using the following formula:

[0190]

[0191] In the formula,

[0192] G(x,y) is the gradient magnitude of each pixel in the enhanced grayscale image;

[0193] G x (x,y) represents the gradient in the x-direction;

[0194] G y (x,y) represents the gradient in the y-direction;

[0195] θ(x,y) represents the gradient direction of each pixel in the enhanced grayscale image.

[0196] S604 performs non-maximum suppression on the enhanced grayscale image, specifically as follows:

[0197] Let the indices of two adjacent pixels along the gradient direction be p and q. Then, by comparing neighboring pixels along the G(x,y) direction, we have:

[0198] If G(x,y) < G(p,q), then suppress G(x,y) and make it 0;

[0199] Otherwise, retain G(x,y).

[0200] S605, perform edge extraction on the enhanced grayscale image, and set a high threshold T. h and low threshold T l Specifically:

[0201] If G(x,y)>T h If so, then mark it as an edge point E(x,y) = 1;

[0202] If G(x,y)<T l If the value is 0, then it is marked as a non-edge point E(x,y)=0;

[0203] If T l <G(x,y)<T h Then, the determination is based on connectivity:

[0204] If this pixel is connected to any pixel that has been marked as an edge, then E(x,y) = 1; otherwise, set it to E(x,y) = 0.

[0205] S7, Calculate the geometric parameters of the fractures on the structural surface of the surrounding rock of the roadway, including but not limited to the actual length, actual width, and dip angle of the fractures on the structural surface of the surrounding rock of the roadway. Specifically, this includes the following steps:

[0206] S701 defines the actual length of the structural surface crack in the surrounding rock of the tunnel as l, the width as u, the pixel length as l1, and the pixel width as u1, which can be obtained through the following formula:

[0207]

[0208] In the formula,

[0209] l represents the actual length of the structural surface fracture in the surrounding rock of the tunnel, in mm;

[0210] u represents the actual width of the structural surface fracture in the surrounding rock of the tunnel, in mm;

[0211] l1 represents the pixel length of the structural surface crack in the surrounding rock of the tunnel, in mm;

[0212] u1 is the pixel width of the structural surface cracks in the surrounding rock of the tunnel, in mm;

[0213] P s The distance between adjacent pixels, in mm;

[0214] D w Working distance for photography, in mm;

[0215] F represents the camera lens focal length, in mm.

[0216] S702, calculate the dip angle γ of the fracture surface of the surrounding rock in the tunnel.

[0217] The pixel connection line of the enhanced grayscale image of the fracture surface of the surrounding rock in the tunnel is approximated as a straight line, and the fracture dip angle is calculated based on the linear regression algorithm.

[0218] The regression formula is:

[0219] y = cx + d

[0220] In the formula,

[0221]

[0222] In the formula,

[0223] y represents the dependent variable, which is the target variable that needs to be predicted or explained;

[0224] x represents the independent variable, which is the factor or predictor that affects y;

[0225] c is the regression coefficient;

[0226] d is the intercept of the regression equation.

[0227] and These are the average coordinates of the skeleton points of the crack pixels on the pixel connection line;

[0228] x i With y i and are the x and y coordinates of the i-th point, respectively;

[0229] N is the number of fracture skeleton points.

[0230] The fracture dip angle can then be obtained.

[0231] δ = arctanc.

[0232] In the formula,

[0233] δ is the dip angle of the fracture on the structural surface of the surrounding rock of the tunnel;

[0234] c is the regression coefficient.

[0235] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for measuring the fracture morphology of surrounding rock in roadways, characterized in that, Includes the following steps: S1, Install a photogrammetric device in the roadway, adjust the spatial parameters of the photogrammetric device in the roadway, and obtain continuous digital images of the surrounding rock area of ​​the roadway to be measured; S2, using the photogrammetry device, images of the structural surfaces of the surrounding rock in the tunnel are acquired, a color original image is obtained, and the color original image is converted into a grayscale image; S3, divide the grayscale image into multiple local regions, establish local region grayscale histograms, equalize the grayscale histograms of the local regions, and merge the equalized results to form an enhanced grayscale image; S4, perform adaptive gamma correction on the enhanced grayscale image; S5 uses a median filter to smooth the enhanced grayscale image; S6. Use the Sobel operator to extract the crack contour of the enhanced grayscale image; Non-maximum suppression is performed on the enhanced grayscale image, including the following steps: First, calculate the gradient magnitude of each pixel in the enhanced grayscale image using the following formula. G(x,y) and direction θ(x, y) : ， In the formula, G(x,y) To enhance the gradient magnitude of each pixel in the resulting grayscale image; G x (x,y) for x Orientation gradient; G y (x,y) for y Orientation gradient; θ(x,y) To enhance the gradient direction of each pixel in the resulting grayscale image; Second, non-maximum suppression is applied to the enhanced grayscale image, specifically as follows: Set the index of two adjacent pixels along the gradient direction to be... p and q , choose in G(x,y) Comparing neighboring pixels in the direction, we have: If, G(x,y)<G(p,q), Then inhibit G(x,y) Set it to 0; otherwise, retain it. G(x,y) ; Edge extraction is performed on the enhanced grayscale image, and a high threshold is set. T h and low threshold T l Specifically: if G(x,y) > T h Then it is marked as an edge point. E(x,y) =1; if G(x,y) < T l If it is not an edge point, then it is marked as a non-edge point. E(x,y) =0; if T l < G(x,y) < T h Then, connectivity is used to determine: if this pixel is connected to any pixel that has been marked as an edge, then... E(x,y) =1; otherwise, set to E(x,y) =0; S7 calculates the true length, true width, and fracture dip angle of the structural surface cracks in the surrounding rock of the tunnel.

2. The method for measuring the fracture morphology of surrounding rock in a roadway according to claim 1, characterized in that, The spatial parameters of the photogrammetry device within the tunnel include the working distance, photographic height, width of the visible rock surface area, and field of view. The photographic working distance is the vertical distance between the center point of the camera lens and the surrounding rock of the tunnel to be measured. The photography height is the height between the center point of the camera lens and the floor of the tunnel. The formula for calculating the width of the visible rock surface area is: ， In the formula, W f Width of the visible rock face area of ​​the tunnel surrounding rock, in mm; a The length of the camera projection sensor is in mm. b Width of the camera projection sensor, in mm; H f Height of the visible rock surface area, in mm; D w Photographic working distance, mm; F The focal length of the camera lens is in mm. The field of vision L f The calculation formula is: ， In the formula, L f For field of view, mm 2 ; S d The size of the camera projection sensor, i.e., the sensor length. a and width b The product of mm 2 ; D w Photographic working distance, mm; F The focal length of the camera lens is in mm.

3. The method for measuring the fracture morphology of surrounding rock in a roadway according to claim 1, characterized in that, The steps to convert a color original image to a grayscale image are as follows: Use MATLAB to obtain the R, G, and B component values ​​of each pixel in the original color image, convert the R, G, and B component values ​​into individual grayscale values, and use the grayscale values ​​to replace the R, G, and B component values ​​of pixels in the original color image's bitmap image.

4. The method for measuring the fracture morphology of roadway surrounding rock according to claim 3, characterized in that, The formula for converting grayscale values ​​is: ， In the formula, I(x,y) This represents the grayscale value corresponding to the pixel in the original image. I r (x,y), I g (x,y), I b (x,y) These are the RGB values ​​of the pixels in the original image.

5. The method for measuring the fracture morphology of surrounding rock in a roadway according to claim 1, characterized in that, Calculate the cumulative distribution function for the gray-level histogram of the local region to identify the total frequency of the local region from the lowest gray-level value to the current gray-level value; The formula for calculating the cumulative distribution function is: ， In the formula, CDF(i) is the cumulative distribution function of the gray-level histogram; P(j) It is grayscale value j The pixel proportions in the grayscale histogram.

6. The method for measuring the fracture morphology of roadway surrounding rock according to claim 1, characterized in that, By transforming each pixel in a local region using the cumulative distribution function, its grayscale value is remapped to an equalized value, thereby equalizing the grayscale histogram of that local region. Specifically: Will CDF Normalize to range[ 0,L-1 ],in L It is the number of gray levels (8-bit image). L =256), then we have: .

7. The method for measuring the fracture morphology of surrounding rock in a roadway according to claim 1, characterized in that, Using MATLAB, the grayscale image is divided into multiple local regions, and the size of the local regions is preferably set to 5×5. Obtain the grayscale value of each pixel within the local region, and use MATLAB to build the corresponding grayscale histogram for the local region.

8. The method for measuring the fracture morphology of surrounding rock in a roadway according to claim 1, characterized in that, The conversion formula for adaptive gamma correction is: ， In the formula, GC(r k ) For adaptive gamma correction normalization transformation; r k For the input of the first k grayscale; r k max The maximum grayscale value input; γ To adjust the parameters: γ < 1 maps the input grayscale to a higher grayscale range, thus brightening the image, while γ > 1, on the contrary, has the effect of darkening the image.

9. The method for measuring the fracture morphology of surrounding rock in a roadway according to claim 1, characterized in that, The cumulative distribution function is used to perform adaptive image correction on the enhanced grayscale image, and the weighted distribution function is used to fine-tune the grayscale histogram of the local region. The weight distribution function is expressed as follows: ， In the formula, γ Here is the weight distribution function; r j It is the first in a local area j grayscale; pdf w (r j ) It is the first j The maximum value of the gray level probability density function.

10. The method for measuring the fracture morphology of surrounding rock in a roadway according to claim 1, characterized in that, The Sobel operator includes the following steps: First, define the Sobel convolution kernel: ， In the formula, G x For horizontal convolution kernels; G y Vertical convolution kernel; Second, the smoothed image is convolved using a Sobel kernel to obtain the gradients in the x and y directions: ， In the formula, G x (x,y) for x Orientation gradient; G y (x,y) for y Orientation gradient; G x (i,j) x-axis i and ordinate j Positional convolution kernel x Orientation gradient; G y (i,j) x-axis i and ordinate j Positional convolution kernel y Orientation gradient; I g (x+i,y+j) To represent the image after grayscale smoothing (x+i, y+j) The pixel value at the location.

11. The method for measuring the fracture morphology of surrounding rock in a roadway according to claim 1, characterized in that, Calculating the true length, true width, and dip angle of structural surface fractures in the surrounding rock of the tunnel includes the following steps: First, the true length of the structural surface fractures in the surrounding rock of the tunnel is defined as... l Width is u The pixel length is l 1, pixel width is u 1. The following formula can be used to obtain: ， In the formula, l The actual length of the structural surface fracture in the surrounding rock of the tunnel, in mm; u The actual width of the structural surface cracks in the surrounding rock of the tunnel, in mm; l 1 represents the pixel length of the structural surface cracks in the surrounding rock of the tunnel, in mm; u 1 represents the pixel width of the structural surface cracks in the surrounding rock of the tunnel, in mm; P s The distance on the sensor represented by a unit pixel distance; D w Photographic working distance, mm; F The focal length of the camera lens is in mm. Second, the pixel connection line of the enhanced grayscale image of the cracks in the surrounding rock structure of the tunnel is approximated as a straight line, and the dip angle of the cracks in the surrounding rock structure of the tunnel is calculated based on the linear regression algorithm. The regression formula for the linear regression algorithm is: ， In the formula, y The dependent variable is the target variable that needs to be predicted or explained. x Represents the independent variable, which is the factor affecting y Factors or predictor variables; and These are the average coordinates of the skeleton points of the crack pixels on the pixel connection line; and The first i The x and y coordinates of each point; N The number of fracture skeleton points; c These are the regression coefficients; d The intercept of the regression equation; The formula for calculating the fracture dip angle of the surrounding rock structural surface in a roadway is: ， In the formula, δ The dip angle of the fracture on the structural surface of the surrounding rock of the tunnel; c is the regression coefficient.

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