CN113011368B - Mine exploitation surface conduction goaf crack identification method and electronic equipment - Google Patents
Mine exploitation surface conduction goaf crack identification method and electronic equipment Download PDFInfo
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Abstract
The invention discloses a mine exploitation surface conduction goaf crack identification method and electronic equipment, wherein the method comprises the following steps: acquiring a thermal infrared image of the ground surface obtained by the unmanned aerial vehicle carrying the thermal infrared imaging device according to a designed route and a high-altitude flight; identifying cracks in the thermal infrared image by adopting an edge detection algorithm, and extracting to generate an edge detection image; extracting a relation of crack temperature in the thermal infrared image and the crack accumulation length and a relation of crack width and the crack accumulation length; judging whether the fracture conducts the goaf according to the relation of the fracture temperature and the fracture accumulation length and the relation of the fracture width and the fracture accumulation length. The mine exploitation ground surface conduction goaf crack identification method based on unmanned aerial vehicle infrared can acquire ground surface crack thermal infrared images in a lossless manner, can quickly identify ground surface cracks, and can effectively judge whether the ground surface cracks above the mine exploitation goaf are conducted or not.
Description
Technical Field
The invention relates to the technical field related to coal mines, in particular to electronic equipment for a method for identifying cracks of a conducting goaf on the surface of a mine exploitation.
Background
At present, the distribution characteristics of ground surface cracks and subsidence basins are analyzed and counted by using high-resolution unmanned aerial vehicle remote sensing images, coal mining ground subsidence disasters such as subsidence pits and ground surface cracks are interpreted and identified, subsidence values are predicted by using MSPS software, a subsidence contour map of a mining area is obtained, goaf ground surface subsidence is monitored and analyzed based on InSAR technology, and crack distribution map is discussed and drawn for application problems of three-dimensional laser scanning technology in subsidence monitoring.
However, the prior art does not have a reliable and effective means for identifying and judging whether the mine mining ground surface cracks conduct goaf, and the existing mine mining ground surface conduction goaf crack identification method is characterized by detecting through geological radar and a transient electromagnetic instrument. Geological radar and transient electromagnetic instrument detection have limitations in construction and detection, the cost of consuming human resources is high, and the accuracy of the detection result is not high. It is difficult to detect surface fractures, particularly in large areas of the mine.
Disclosure of Invention
Based on the above, there is a need to provide a method for identifying the cracks of the mine mining ground surface conduction goaf and an electronic device, aiming at the technical problem that no reliable and effective means is available in the prior art for identifying the cracks of the mine mining ground surface and judging whether the cracks conduct the goaf.
The invention provides a mine exploitation surface conduction goaf crack identification method, which comprises the following steps:
Acquiring a thermal infrared image of the ground surface obtained by the unmanned aerial vehicle carrying the thermal infrared imaging device according to a designed route and a high-altitude flight;
Identifying cracks in the thermal infrared image by adopting an edge detection algorithm, and extracting to generate an edge detection image;
extracting a relation of crack temperature in the thermal infrared image and the crack accumulation length and a relation of crack width and the crack accumulation length;
Judging whether the fracture conducts the goaf according to the relation of the fracture temperature and the fracture accumulation length and the relation of the fracture width and the fracture accumulation length.
Further, the extracting the relation of the crack temperature and the crack width in the thermal infrared image to the crack accumulation length specifically includes:
Dividing the thermal infrared image into M multiplied by N pixels, wherein the pixel coordinate of each pixel is the row and the column of the pixel in the thermal infrared image;
Extracting a temperature value of each pixel from a thermal infrared image to generate a temperature matrix containing M multiplied by N temperature matrix elements, wherein each temperature matrix element of the temperature matrix is a temperature value of a corresponding pixel coordinate in the thermal infrared image;
Determining pixel coordinates of a crack edge in an edge detection image in the thermal infrared image, reserving temperature matrix elements corresponding to the pixel coordinates of the crack edge in a temperature matrix, and modifying other thermometer matrix elements to 0;
Acquiring an included angle between each pixel row of the crack in the thermal infrared image and the vertical direction;
calculating the temperature of each pixel row of the crack in the thermal infrared image, the width of each pixel row and the cumulative length of the crack based on the temperature matrix and the included angle of each pixel row of the crack in the thermal infrared image and the vertical direction;
and (3) sequentially splicing and integrating the temperature of each pixel row of the crack, the width of each pixel row and the crack accumulation length to obtain the relation of the crack temperature and the crack accumulation length and the relation of the crack width and the crack accumulation length.
Further, the calculating the temperature of each pixel row, the width of each pixel row and the accumulated length of the crack in the thermal infrared image based on the temperature matrix and the included angle between each pixel row and the vertical direction of the crack in the thermal infrared image specifically includes:
calculating the temperature of each pixel row of the crack in the thermal infrared image Wherein T (i, j) is a temperature matrix element of an ith row and a jth column of the temperature matrix, num i is a number of non-zero temperature matrix elements of the ith row in the temperature matrix, and T i is a temperature of the ith pixel row of the crack in the thermal infrared image;
calculating the width of each pixel row of the crack in the thermal infrared image Wherein n is the resolution of the thermal infrared image, and W i is the width of the ith pixel row of the crack in the thermal infrared image;
calculating the accumulated length of the crack of each pixel row in the thermal red image Wherein s is the line number of a pixel line of a crack starting end in the thermal infrared image, theta m is the included angle between the mth pixel line of the crack in the thermal infrared image and the vertical direction, and L i is the cumulative length of the crack of the ith pixel line of the crack in the thermal infrared image.
Further, the acquiring the included angle between each pixel row and the vertical direction of the slit in the thermal infrared image specifically includes:
The crack is divided into a plurality of sections, and an included angle between each section of the crack and the vertical direction in the thermal infrared image is obtained;
And the included angle between each pixel row and the vertical direction in the thermal infrared image is the included angle between the section where the pixel row is located and the vertical direction.
Further, the calculating the temperature of the crack in each pixel row, the width of each pixel row and the cumulative length of the crack before the temperature matrix and the included angle between each pixel row and the vertical direction in the thermal infrared image further includes:
counting the number of non-zero temperature matrix elements in each row of the temperature matrix, and storing the number as a pixel number array containing M number of array elements, wherein each number of array elements in the pixel number array is the number of non-zero temperature matrix elements in the row corresponding to the array elements in the temperature matrix.
Further, the calculating the temperature of the crack in each pixel row, the width of each pixel row and the cumulative length of the crack before the temperature matrix and the included angle between each pixel row and the vertical direction in the thermal infrared image further includes:
Determining a pixel row of the crack, which is covered by the crack in the thermal infrared image, and deleting temperature matrix elements corresponding to other pixel rows except the crack pixel row in a temperature matrix;
Determining a first pixel row of a crack in the thermal infrared image according to the pixel row of the crack starting end;
correcting the row number of the temperature matrix element corresponding to the slit pixel row in the temperature matrix as follows: the original line number of the temperature matrix element is subtracted from the original line number of the temperature matrix element corresponding to the first pixel line of the crack, and then one is added.
Further, the calculating the temperature of the crack in each pixel row, the width of each pixel row and the cumulative length of the crack before the temperature matrix and the included angle between each pixel row and the vertical direction in the thermal infrared image further includes:
determining pixels of a size reference object from the thermal infrared image, wherein the size reference object is arranged on the ground surface photographed by the unmanned aerial vehicle in advance;
Acquiring the real size of a size reference object;
the resolution n of the thermal infrared image is determined based on the true size of the size reference and the pixels of the size reference.
Further, judging whether the fracture conducts the goaf according to the relation of the fracture temperature and the fracture cumulative length and the relation of the fracture width and the fracture cumulative length, specifically including:
Judging that the fracture conducts the goaf if the relation of the fracture temperature and the fracture accumulation length is inversely related to the relation of the fracture width and the fracture accumulation length;
And if the relation of the fracture temperature and the fracture accumulation length is positively correlated with the relation of the fracture width and the fracture accumulation length, judging that the fracture is not conducted in the goaf.
Further:
The method for acquiring the thermal infrared image of the ground surface obtained by carrying the thermal infrared imaging device on the unmanned aerial vehicle according to the designed route and the aerial altitude flight specifically comprises the following steps: acquiring a thermal infrared image of the earth surface obtained by the unmanned aerial vehicle carrying the thermal infrared imaging device in the daytime and a thermal infrared image of the earth surface obtained in the early morning according to a designed route and a designed voyage flight, taking the thermal infrared image of the earth surface obtained in the daytime as the thermal infrared image of the daytime, and taking the thermal infrared image of the earth surface obtained in the early morning as the thermal infrared image of the early morning;
Judging whether the fracture conducts the goaf according to the relation of the fracture temperature and the fracture accumulation length and the relation of the fracture width and the fracture accumulation length, wherein the method specifically comprises the following steps of:
Judging that the crack is conducted in the goaf if the relation of the crack temperature and the crack accumulation length calculated based on the daytime thermal infrared image is inversely related to the relation of the crack width and the crack accumulation length, and the relation of the crack temperature and the crack accumulation length calculated based on the early morning thermal infrared image is not related to the relation of the crack width and the crack accumulation length;
If the relation of the crack temperature and the crack accumulation length obtained based on the early morning thermal infrared image is positively correlated with the relation of the crack width and the crack accumulation length, the crack cannot be identified in the thermal infrared image of the earth surface obtained in the daytime, and the crack cannot be conducted in the thermal infrared image of the earth surface obtained in the early morning, judging the goaf.
The present invention provides an electronic device including:
at least one processor; and
A memory communicatively coupled to at least one of the processors; wherein,
The memory stores instructions executable by at least one of the processors to enable the at least one processor to perform a mine production surface conduction goaf fracture identification method as previously described.
The mine exploitation ground surface conduction goaf crack identification method based on unmanned aerial vehicle infrared can acquire ground surface crack thermal infrared images in a lossless manner, can quickly identify ground surface cracks, and can effectively judge whether the ground surface cracks above the mine exploitation goaf are conducted or not. Meanwhile, whether the ground surface cracks conduct the goaf is judged according to the identification results of the ground surface cracks in the high-temperature environment and the low-temperature environment, and the criterion is simple. The method is simple and convenient to operate, has small workload, and has positive significance for quick identification and timely treatment of the mine ground surface cracks.
Drawings
FIG. 1 is a working flow chart of a mine exploitation surface conduction goaf crack identification method according to an embodiment of the invention;
FIG. 2 is a workflow diagram of a mine exploitation surface conduction goaf crack identification method based on unmanned aerial vehicle infrared according to a preferred embodiment of the invention;
FIG. 3 is a schematic illustration of a unmanned aerial vehicle route in accordance with an embodiment of the present invention;
FIG. 4 is a schematic view of a visible light image, a thermal infrared image and an edge detection image according to an embodiment of the present invention;
FIG. 5a is a schematic illustration of the extraction results of crack 1 at 11:00 am;
Fig. 5b shows a crack 1 early morning 5: 00;
FIG. 5c is a graph showing the extraction results of crack 2 early morning 5:00;
FIG. 6 is a graph showing the results of temperature information extraction data for sand (soil) and vegetation;
fig. 7 is a schematic diagram of a hardware structure of an electronic device according to the present invention.
Detailed Description
The invention will now be described in further detail with reference to the drawings and to specific examples.
Fig. 1 is a working flow chart of a method for identifying cracks in a mine mining surface conduction goaf, which comprises the following steps:
Step S101, acquiring a thermal infrared image of the earth surface obtained by the unmanned aerial vehicle carrying the thermal infrared imaging device according to a designed route and a high altitude flight;
Step S102, recognizing cracks in the thermal infrared image by adopting an edge detection algorithm, and extracting and generating an edge detection image;
step S103, extracting the relation of the crack temperature in the thermal infrared image and the crack accumulation length and the relation of the crack width and the crack accumulation length;
step S104, judging whether the fracture conducts the goaf according to the relation of the fracture temperature and the fracture accumulation length and the relation of the fracture width and the fracture accumulation length.
Specifically, the thermal infrared imaging device is carried by an unmanned plane to fly according to a designed route and altitude, and then the ground surface thermal infrared image is acquired in step S101. Step S102 is then performed to identify and extract surface fractures in the thermal infrared image using an edge detection algorithm. And then, executing step S103, and extracting temperature information in the thermal infrared image and the width and length of the ground surface crack, so as to obtain the relation of the crack temperature and the crack accumulation length and the relation of the crack width and the crack accumulation length. And finally, step S104, judging whether the ground surface fracture conducts the goaf according to the relation of the fracture temperature and the fracture accumulation length and the relation of the fracture width and the fracture accumulation length.
The mine exploitation ground surface conduction goaf crack identification method based on unmanned aerial vehicle infrared can acquire ground surface crack thermal infrared images in a lossless manner, can quickly identify ground surface cracks, and can effectively judge whether the ground surface cracks above the mine exploitation goaf are conducted or not. Meanwhile, whether the ground surface cracks conduct the goaf is judged according to the identification results of the ground surface cracks in the high-temperature environment and the low-temperature environment, and the criterion is simple. The method is simple and convenient to operate, has small workload, and has positive significance for quick identification and timely treatment of the mine ground surface cracks.
In one embodiment, the relationship between the fracture temperature and the fracture length and the relationship between the fracture width and the fracture length in the extracted thermal infrared image specifically includes:
Dividing the thermal infrared image into M multiplied by N pixels, wherein the pixel coordinate of each pixel is the row and the column of the pixel in the thermal infrared image;
Extracting a temperature value of each pixel from a thermal infrared image to generate a temperature matrix containing M multiplied by N temperature matrix elements, wherein each temperature matrix element of the temperature matrix is a temperature value of a corresponding pixel coordinate in the thermal infrared image;
Determining pixel coordinates of a crack edge in an edge detection image in the thermal infrared image, reserving temperature matrix elements corresponding to the pixel coordinates of the crack edge in a temperature matrix, and modifying other thermometer matrix elements to 0;
Acquiring an included angle between each pixel row of the crack in the thermal infrared image and the vertical direction;
calculating the temperature of each pixel row of the crack in the thermal infrared image, the width of each pixel row and the cumulative length of the crack based on the temperature matrix and the included angle of each pixel row of the crack in the thermal infrared image and the vertical direction;
and (3) sequentially splicing and integrating the temperature of each pixel row of the crack, the width of each pixel row and the crack accumulation length to obtain the relation of the crack temperature and the crack accumulation length and the relation of the crack width and the crack accumulation length.
Specifically, the thermal infrared image is divided into m×n pixels, and the pixel coordinates of each pixel are the row and column in which the pixel is located in the thermal infrared image.
And then extracting the temperature value of each pixel from the thermal infrared image to generate a temperature matrix containing M multiplied by N temperature matrix elements, wherein the row number and the column number of each temperature matrix element in the temperature matrix are in one-to-one correspondence with the pixels in the thermal infrared image, so that the temperature value of the corresponding pixel coordinate in the thermal infrared image is extracted into the temperature matrix.
The edge detection image is a gray image obtained by identifying the thermal infrared image by an edge detection algorithm, so that each pixel in the edge detection image corresponds to each pixel in the thermal infrared image one by one, and each pixel in the edge detection image corresponds to each temperature matrix element in the temperature center one by one. The computer may be used to manually erase the irrelevant non-fractured edges in the edge detection image and fill the fracture interior regions. The thermal infrared image temperature information can be extracted by Maxlm DL software to derive an mxn temperature matrix. And then, importing the temperature matrix of the processed edge detection image and the thermal infrared image into MATLAB software, positioning by using pixel coordinates of the crack edge, retaining elements corresponding to the crack position in the temperature matrix, and modifying the rest elements to 0. And finally, acquiring an included angle between each pixel row of the crack in the thermal infrared image and the vertical direction, calculating the temperature of each pixel row of the crack in the thermal infrared image, the width of each pixel row and the crack accumulation length, and sequentially splicing and integrating the temperature of each pixel row of the crack, the width of each pixel row and the crack accumulation length to obtain the relation of the crack temperature and the crack accumulation length and the relation of the crack width and the crack accumulation length.
According to the embodiment, through the corresponding relation between the thermal infrared image and the edge detection image, each parameter of the crack in the thermal infrared image is determined, so that the relation of the crack temperature and the crack accumulation length and the relation of the crack width and the crack accumulation length are obtained.
In one embodiment, the calculating the temperature of each pixel row of the thermal infrared image, the width of each pixel row and the accumulated length of the crack based on the temperature matrix and the included angle between each pixel row of the crack in the thermal infrared image and the vertical direction specifically includes:
calculating the temperature of each pixel row of the crack in the thermal infrared image Wherein T (i, j) is a temperature matrix element of an ith row and a jth column of the temperature matrix, num i is a number of non-zero temperature matrix elements of the ith row in the temperature matrix, and T i is a temperature of the ith pixel row of the crack in the thermal infrared image;
calculating the width of each pixel row of the crack in the thermal infrared image Wherein n is the resolution of the thermal infrared image, and W i is the width of the ith pixel row of the crack in the thermal infrared image;
calculating the accumulated length of the crack of each pixel row in the thermal red image Wherein s is the line number of a pixel line of a crack starting end in the thermal infrared image, theta m is the included angle between the mth pixel line of the crack in the thermal infrared image and the vertical direction, and L i is the cumulative length of the crack of the ith pixel line of the crack in the thermal infrared image.
In this embodiment, the calculation formulas of T i、Wi and L i can calculate the width, temperature and position coordinates along the crack corresponding to any position of the crack, and can realize the correspondence of the three, thereby realizing quantitative characterization of the crack temperature and the crack development morphology.
In one embodiment, the acquiring an included angle between each pixel row of the slit in the thermal infrared image and the vertical direction specifically includes:
The crack is divided into a plurality of sections, and an included angle between each section of the crack and the vertical direction in the thermal infrared image is obtained;
And the included angle between each pixel row and the vertical direction in the thermal infrared image is the included angle between the section where the pixel row is located and the vertical direction.
In the embodiment, the slit is divided into a plurality of sections, so that the included angle of each section is used as the included angle of the pixel row included in the section, thereby simplifying calculation and improving efficiency.
In one embodiment, before calculating the temperature of each pixel row, the width of each pixel row and the accumulated length of the crack in the thermal infrared image based on the temperature matrix and the included angle between each pixel row and the vertical direction of the crack in the thermal infrared image, the method further includes:
counting the number of non-zero temperature matrix elements in each row of the temperature matrix, and storing the number as a pixel number array containing M number of array elements, wherein each number of array elements in the pixel number array is the number of non-zero temperature matrix elements in the row corresponding to the array elements in the temperature matrix.
In the embodiment, the pixel number array is set, so that the number of non-zero temperature matrix elements in each row of the temperature matrix is extracted, and calculation is simplified.
In one embodiment, before calculating the temperature of each pixel row, the width of each pixel row and the accumulated length of the crack in the thermal infrared image based on the temperature matrix and the included angle between each pixel row and the vertical direction of the crack in the thermal infrared image, the method further includes:
Determining a pixel row of the crack, which is covered by the crack in the thermal infrared image, and deleting temperature matrix elements corresponding to other pixel rows except the crack pixel row in a temperature matrix;
Determining a first pixel row of a crack in the thermal infrared image according to the pixel row of the crack starting end;
correcting the row number of the temperature matrix element corresponding to the slit pixel row in the temperature matrix as follows: the original line number of the temperature matrix element is subtracted from the original line number of the temperature matrix element corresponding to the first pixel line of the crack, and then one is added.
In the embodiment, the temperature matrix is corrected, coordinates of the beginning end and the end of the crack are determined, so that i=1 at the beginning end of the crack and i=imax at the end of the crack are achieved, calculation is simplified, and efficiency is improved.
In one embodiment, before calculating the temperature of each pixel row, the width of each pixel row and the accumulated length of the crack in the thermal infrared image based on the temperature matrix and the included angle between each pixel row and the vertical direction of the crack in the thermal infrared image, the method further includes:
determining pixels of a size reference object from the thermal infrared image, wherein the size reference object is arranged on the ground surface photographed by the unmanned aerial vehicle in advance;
Acquiring the real size of a size reference object;
the resolution n of the thermal infrared image is determined based on the true size of the size reference and the pixels of the size reference.
In the embodiment, the resolution of the thermal infrared image is calculated by adopting the dimension reference object, so that the accurate crack width and the accurate crack accumulation length are obtained.
In one embodiment, the determining whether the fracture conducts the goaf according to the relation between the fracture temperature and the fracture cumulative length and the relation between the fracture width and the fracture cumulative length specifically includes:
Judging that the fracture conducts the goaf if the relation of the fracture temperature and the fracture accumulation length is inversely related to the relation of the fracture width and the fracture accumulation length;
And if the relation of the fracture temperature and the fracture accumulation length is positively correlated with the relation of the fracture width and the fracture accumulation length, judging that the fracture is not conducted in the goaf.
Specifically, if the fracture conducts the goaf, for a certain accumulated length of the fracture, when the corresponding fracture width increases, air flows downwards due to the fact that the fracture conducts the goaf, so that the fracture temperature corresponding to the fracture accumulated length decreases, namely the relation of the fracture temperature and the fracture accumulated length is inversely related to the relation of the fracture width and the fracture accumulated length, and accordingly the fracture conducted goaf can be judged. If the crack is not conducted with the goaf, for the crack accumulation length, when the crack width is larger, the crack grows deeper, and internal heat is radiated more, so that the crack temperature corresponding to the crack accumulation length rises, namely the relation of the crack temperature and the crack width is positively correlated with the relation of the crack accumulation length, and the crack is judged to be not conducted with the goaf.
According to the method for judging the crack conduction goaf, the relation between the crack width and the crack temperature is comprehensively considered, so that whether the ground surface cracks of mine exploitation conduct the goaf or not is effectively identified.
In one embodiment:
The method for acquiring the thermal infrared image of the ground surface obtained by carrying the thermal infrared imaging device on the unmanned aerial vehicle according to the designed route and the aerial altitude flight specifically comprises the following steps: acquiring a thermal infrared image of the earth surface obtained by the unmanned aerial vehicle carrying the thermal infrared imaging device in the daytime and a thermal infrared image of the earth surface obtained in the early morning according to a designed route and a designed voyage flight, taking the thermal infrared image of the earth surface obtained in the daytime as the thermal infrared image of the daytime, and taking the thermal infrared image of the earth surface obtained in the early morning as the thermal infrared image of the early morning;
Judging whether the fracture conducts the goaf according to the relation of the fracture temperature and the fracture accumulation length and the relation of the fracture width and the fracture accumulation length, wherein the method specifically comprises the following steps of:
Judging that the crack is conducted in the goaf if the relation of the crack temperature and the crack accumulation length calculated based on the daytime thermal infrared image is inversely related to the relation of the crack width and the crack accumulation length, and the relation of the crack temperature and the crack accumulation length calculated based on the early morning thermal infrared image is not related to the relation of the crack width and the crack accumulation length;
If the relation of the crack temperature and the crack accumulation length obtained based on the early morning thermal infrared image is positively correlated with the relation of the crack width and the crack accumulation length, the crack cannot be identified in the thermal infrared image of the earth surface obtained in the daytime, and the crack cannot be conducted in the thermal infrared image of the earth surface obtained in the early morning, judging the goaf.
Specifically, the unmanned aerial vehicle carries a thermal infrared imaging device to fly once according to the path shown in fig. 3 in the daytime at noon and in the period of the lowest air temperature in the day before sunrise in the early morning, so that two groups of ground surface thermal infrared images are obtained. According to the ground surface heat infrared image and the edge detection result in the two environments of day and night, the goaf is conducted by the ground surface cracks which can be identified in the day and in the early morning, important attention and timely landfill treatment are needed, and the goaf is not conducted by the cracks which are identified only in the environment with lower outside temperature in the early morning.
According to the embodiment, the two thermal infrared images of the daytime and the early morning are added, so that the leak that cracks of the non-conducted goaf cannot be identified in the daytime when the air temperature is high is overcome, whether the cracks are conducted or not can be compared and distinguished, the risk of the cracks is evaluated, the timely treatment of the cracks with air leakage threat is facilitated, and the method has positive significance in avoiding spontaneous combustion and ignition of residual coal in the goaf.
As shown in FIG. 2, the method for identifying the cracks of the mine exploitation surface conduction goaf based on the unmanned aerial vehicle infrared comprises the following steps:
And step S201, determining a monitoring range, unmanned aerial vehicle altitude, heading overlapping degree and a route according to the mine and the working face positions.
Specifically, the geographical position of a mine and the specific position of a working face are studied in the field, the positions and boundaries of the mine and the working face on the ground surface are determined, the proper monitoring range, the unmanned aerial vehicle altitude, the coverage range and the heading overlap degree are determined, and finally the unmanned aerial vehicle route is determined;
Step S202, carrying a thermal infrared imaging device by using an unmanned aerial vehicle, and flying according to a designed route and a designed altitude to acquire an earth surface thermal infrared image.
And the unmanned aerial vehicle flies according to the designed route and the designed altitude to acquire the surface thermal infrared image. As shown in fig. 3, the unmanned aerial vehicle route is preferably an "arcuate" route 33, with the route 33 covering the mine or face boundary line 31 and being located within the monitored area boundary line 32.
Specifically, the mine boundary refers to a mine exploitation boundary and is formed by a plurality of line segments at the inner edge of a coal pillar of the contrast well Tian Bianjie on the surface. The working face boundary line is a rectangular area boundary line formed by four line segments of the ground surface, which are used for contrasting the current positions of the coal face cutting hole, the transportation gate, the return air gate and the working face. The monitoring area boundary line is a rectangular area boundary line corresponding to expansion of the working surface boundary line to the rear of the cutting hole by 100m and expansion of the working surface boundary line to the outer side of the current position of the transportation cis-slot, the return air cis-slot and the working surface by 50 m.
Specifically, aerial photographing is performed in the daytime with higher temperature and the early morning with lower temperature respectively, and thermal infrared images in the daytime and the early morning are obtained.
And step S203, identifying and extracting the ground surface cracks in the thermal infrared image by using an edge detection algorithm.
Specifically, the edge detection algorithm is an edge detection algorithm based on cellular automata. After format conversion and image preprocessing are carried out on the thermal infrared image, MATLAB software is utilized to carry out edge detection based on cellular automata on images of the two environments of the day and the night, and the threshold value is continuously adjusted to enable crack extraction to be complete and irrelevant non-crack edges to be filtered as far as possible.
And S204, extracting temperature information in the thermal infrared image and the width and length of the ground surface crack.
Specifically, the temperature information of the thermal infrared image comprises soil temperature, vegetation temperature and crack temperature.
Specifically, the extraction method of soil temperature and vegetation temperature comprises the following steps:
And importing Maxlm DL the thermal infrared image into software, respectively setting a certain number of measuring points in a soil area and a vegetation area, reading the temperature values of pixels where the measuring points are located, and respectively calculating the temperature average values of the measuring points in the soil area and the vegetation area to be respectively used as the soil temperature and the vegetation temperature.
Specifically, the temperature information of the thermal infrared image comprises soil temperature, vegetation temperature and crack temperature, wherein the soil temperature is the average value of a certain amount of pixel point temperature of a soil area in the thermal infrared image, and the vegetation temperature is the average value of a certain amount of pixel point temperature of a vegetation area;
Specifically, the temperature, width and length of the crack according to the present invention can be expressed as:
Wherein i is the number of pixel rows counted from the start end of the crack; num i is the number of i-th row pixels from the start of the crack; θ is the included angle between the crack and the vertical direction, which changes along with the different positions of the crack, and θ i is the included angle between the ith row of pixels from the starting end of the crack and the vertical direction; n is the centimeter resolution of the thermal infrared image in pixels/cm; t (i, j) is the temperature value of the ith row from the crack starting end and the jth pixel from the crack left edge pixel, and the unit is the temperature value; t i is the temperature of the ith row from the beginning of the crack in degrees Celsius; w i is the width of the ith row from the start end of the crack, and the unit is cm; l i is the cumulative length from the start of the crack to the position of the ith row, in m. Since i is the number of pixel lines counted from the crack start end, the counting can be directly counted from the 1 st line to the i st line of the crack start end. Meanwhile, since the cumulative length is in meters (m) and the resolution n is in pixels/centimeter (pixel/cm), it is multiplied by 100 to adjust the unit.
Specifically, the temperature, length and width extraction steps of the crack:
first, determining the resolution N of the image, the number of pixel rows M and the number of pixel columns N according to the thermal infrared image and the size reference object.
Secondly, manually erasing irrelevant non-crack edges in the edge detection image by using a computer, and filling the crack inner area;
thirdly, extracting thermal infrared image temperature information through Maxlm DL software to derive an MxN temperature matrix;
Step four, importing temperature matrixes of the processed edge detection image and the thermal infrared image into MATLAB software, positioning by using pixel coordinates of the crack edge, retaining elements corresponding to the crack position in the temperature matrix, and modifying other elements to 0;
Fifthly, counting the number of non-zero elements in each row of the temperature matrix by using MATLAB software, and storing the number as a one-dimensional array formed by M elements, wherein the i element Num i of Num in the one-dimensional array corresponds to the number of non-zero elements in the i row of the temperature matrix;
A sixth step of correcting the temperature matrix and the one-dimensional array Num after MATLAB processing by combining Maxlm DL software, determining coordinates of a crack starting end and a crack tail end, enabling i=1 at the crack starting end and i=imax at the crack tail end, determining a value of imax and the temperature T (i, j) of each pixel point of the crack, wherein imax is the total line number covered by the crack in a thermal infrared image;
Seventh, measuring acute angle included angles between the fracture and the longitudinal direction of the thermal infrared image in a segmented mode, marking the segmentation points of each segment of the fracture as i1, i2, i3 and … ik, and marking the segmentation included angles as theta' 1-i1,θ'i1-i2,…θ'ik-imax;
Eighth, when i is more than or equal to 1 and less than or equal to i1, theta 1＝θ2＝…＝θi1＝θ'1-i1, according to T (i, j) and Num i, calculating the temperature, width and accumulated length of each pixel row of the first section of the crack according to a formula of T i,Wi,Li;
When i1 is more than i and less than or equal to i2, theta i1+1＝θi1+2＝…＝θi2＝θ'i1-i2, calculating the temperature, the width and the accumulated length of each pixel row of the second section of the crack according to a formula of T i,Wi,Li according to T (i, j) and Num i;
…
When ik is less than i and less than or equal to imax, theta ik+1＝θik+2＝…＝θimax＝θ'ik-imax, calculating the temperature, width and accumulated length of each pixel row of the k+1th subsection of the slit according to a formula of T i,Wi,Li according to T (i, j) and Num i;
And tenth, splicing and integrating the temperature, the width and the accumulated length of each section of the crack in sequence.
And S204, judging whether the ground surface cracks conduct the goaf.
Specifically, according to the thermal infrared images and the edge detection results in the two environments of the day and the night, the ground surface cracks which can be identified in the day and the early morning conduct the goaf, and the cracks which are identified only in the environment with lower outside temperature in the early morning do not conduct the goaf.
(1) The unmanned aerial vehicle is used for carrying the thermal infrared imaging device to fly according to a preset route, so that the ground surface thermal infrared image is obtained, and the degree of automation is high.
(2) And the crack in the thermal infrared image is identified and extracted by using an edge detection algorithm, the temperature information of the thermal infrared image, the width and the length of the crack are analyzed, and whether the crack is conducted with the goaf is judged, so that the crack of the mining area can be rapidly and efficiently distinguished, the operation is simple, and the working efficiency is high.
As an example, a crack was monitored on a mine surface in the west using an unmanned aerial vehicle-mounted thermal infrared imaging device, and each of the images was taken at 11:00 a.m. and 5:00 a.m.. Edge detection is carried out on thermal infrared images at two moments of 11:00 am and 5:00 am respectively. Firstly, determining the resolution of an image according to a thermal infrared image and a size reference object, secondly, manually erasing irrelevant non-crack edges in an edge detection image by using a computer, filling the crack interior area, extracting thermal infrared image temperature information by Maxlm DL software, deriving M multiplied by N, importing a temperature matrix of the processed edge detection image and the thermal infrared image into MATLAB software, positioning by using pixel coordinates of the crack edges, retaining elements corresponding to the crack positions in the temperature matrix, and finally obtaining thermal infrared images 43 and 11 of a surface crack such as a visible light image 41 and a thermal infrared image 42 of 11:00 and a thermal infrared image 43 and 11 of 5:00 shown in FIG. 4: 00 edge detection map 44, and 5:00 edge detection map 45. The results of the extraction of the temperature, width and length of the crack are shown in FIGS. 5a-5c, wherein FIG. 5a is the result of the extraction of crack 1 at 11:00 am and FIG. 5b is crack 1 at 5 am: 00, and FIG. 5c shows the results of 5:00 in the morning of crack 2. The temperature information extraction data of sand (soil) and vegetation is shown in fig. 6.
According to the result, the crack 1 can be identified as the ground surface crack for conducting the goaf at 11:00 am and 5:00 am. 11:00 am, and the temperature is lower at the position with large crack width. In the early morning 5:00, the temperature of the cracks does not change obviously with the width; the crack 2 can be identified only in the position of 5:00 a.m. and the crack width is larger, the temperature is higher, and the crack 2 does not conduct the goaf.
Fig. 7 is a schematic diagram of a hardware structure of an electronic device according to the present invention, including:
at least one processor 701; and
A memory 702 communicatively coupled to at least one of the processors 701; wherein,
The memory 702 stores instructions executable by at least one of the processors to enable the at least one processor to perform the mine production surface conduction goaf fracture identification method as previously described.
One processor 701 is illustrated in fig. 7.
The electronic device may further include: an input device 703 and a display device 704.
The processor 701, the memory 702, the input device 703 and the display device 704 may be connected by a bus or other means, in the figures by way of example.
The memory 702 is used as a non-volatile computer readable storage medium, and may be used to store a non-volatile software program, a non-volatile computer executable program, and modules, such as program instructions/modules corresponding to a method for identifying a crack in a mine mining surface conduction goaf in an embodiment of the present application, for example, a method flow shown in fig. 1. The processor 701 executes various functional applications and data processing by running nonvolatile software programs, instructions and modules stored in the memory 702, that is, implements the mine exploitation surface conduction goaf fracture identification method in the above embodiment.
Memory 702 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the stored data area may store data created from the use of mine mining surface conduction goaf fracture identification methods, and the like. In addition, the memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 702 optionally includes memory remotely located relative to the processor 701, which may be connected via a network to a device performing the mine mining surface conduction goaf fracture identification method. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 703 may receive input user clicks and generate signal inputs related to user settings and function control of the mine mining surface conduction goaf fracture identification method. The display device 704 may include a display apparatus such as a display screen.
The one or more modules are stored in the memory 702 and when executed by the one or more processors 701 perform the mine mining surface conduction goaf fracture identification method of any of the method embodiments described above.
The mine exploitation ground surface conduction goaf crack identification method based on unmanned aerial vehicle infrared can acquire ground surface crack thermal infrared images in a lossless manner, can quickly identify ground surface cracks, and can effectively judge whether the ground surface cracks above the mine exploitation goaf are conducted or not. Meanwhile, whether the ground surface cracks conduct the goaf is judged according to the identification results of the ground surface cracks in the high-temperature environment and the low-temperature environment, and the criterion is simple. The method is simple and convenient to operate, has small workload, and has positive significance for quick identification and timely treatment of the mine ground surface cracks.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (8)
1. The method for identifying the cracks of the conducting goaf on the surface of the mine exploitation is characterized by comprising the following steps of:
Acquiring a thermal infrared image of the ground surface obtained by the unmanned aerial vehicle carrying the thermal infrared imaging device according to a designed route and a high-altitude flight;
Identifying cracks in the thermal infrared image by adopting an edge detection algorithm, and extracting to generate an edge detection image;
extracting a relation of crack temperature in the thermal infrared image and the crack accumulation length and a relation of crack width and the crack accumulation length;
Judging whether the fracture conducts the goaf according to the relation of the fracture temperature and the fracture accumulation length and the relation of the fracture width and the fracture accumulation length;
the relation of the crack temperature and the crack width in the extracted thermal infrared image relative to the crack accumulation length and the relation of the crack width relative to the crack accumulation length specifically comprise the following steps:
Dividing the thermal infrared image into M multiplied by N pixels, wherein the pixel coordinate of each pixel is the row and the column of the pixel in the thermal infrared image;
Extracting a temperature value of each pixel from a thermal infrared image to generate a temperature matrix containing M multiplied by N temperature matrix elements, wherein each temperature matrix element of the temperature matrix is a temperature value of a corresponding pixel coordinate in the thermal infrared image;
Determining pixel coordinates of a crack edge in an edge detection image in the thermal infrared image, reserving temperature matrix elements corresponding to the pixel coordinates of the crack edge in a temperature matrix, and modifying other thermometer matrix elements to 0;
Acquiring an included angle between each pixel row of the crack in the thermal infrared image and the vertical direction;
calculating the temperature of each pixel row of the crack in the thermal infrared image, the width of each pixel row and the cumulative length of the crack based on the temperature matrix and the included angle of each pixel row of the crack in the thermal infrared image and the vertical direction;
the temperature of each pixel row of the crack, the width of each pixel row and the crack accumulation length are spliced and integrated in sequence to obtain the relation of the crack temperature and the crack accumulation length and the relation of the crack width and the crack accumulation length;
Judging whether the fracture conducts the goaf according to the relation of the fracture temperature and the fracture accumulation length and the relation of the fracture width and the fracture accumulation length, wherein the method specifically comprises the following steps of:
Judging that the fracture conducts the goaf if the relation of the fracture temperature and the fracture accumulation length is inversely related to the relation of the fracture width and the fracture accumulation length;
And if the relation of the fracture temperature and the fracture accumulation length is positively correlated with the relation of the fracture width and the fracture accumulation length, judging that the fracture is not conducted in the goaf.
2. The method for identifying the fissure in the mine exploitation surface conduction goaf according to claim 1, wherein the calculating the temperature of each pixel row of the fissure in the thermal infrared image, the width of each pixel row and the fissure accumulation length based on the temperature matrix and the included angle between each pixel row of the fissure in the thermal infrared image and the vertical direction specifically comprises:
calculating the temperature of each pixel row of the crack in the thermal infrared image Wherein T (i, j) is a temperature matrix element of an ith row and a jth column of the temperature matrix, num i is a number of non-zero temperature matrix elements of the ith row in the temperature matrix, and T i is a temperature of the ith pixel row of the crack in the thermal infrared image;
calculating the width of each pixel row of the crack in the thermal infrared image Wherein n is the resolution of the thermal infrared image, and W i is the width of the ith pixel row of the crack in the thermal infrared image;
calculating the accumulated length of the crack of each pixel row in the thermal red image Wherein s is the line number of a pixel line of a crack starting end in the thermal infrared image, theta m is the included angle between the mth pixel line of the crack in the thermal infrared image and the vertical direction, and L i is the cumulative length of the crack of the ith pixel line of the crack in the thermal infrared image.
3. The mine exploitation surface conduction goaf crack identification method of claim 1, wherein the acquiring the included angle between each pixel row of the crack in the thermal infrared image and the vertical direction specifically comprises:
The crack is divided into a plurality of sections, and an included angle between each section of the crack and the vertical direction in the thermal infrared image is obtained;
And the included angle between each pixel row and the vertical direction in the thermal infrared image is the included angle between the section where the pixel row is located and the vertical direction.
4. The method for identifying fissures in a mine exploitation surface conduction goaf according to claim 1, wherein calculating the temperature of the fissures in each pixel row, the width of each pixel row and the accumulated length of the fissures before calculating the fissures in the thermal infrared image based on the temperature matrix and the included angle of each pixel row of the fissures in the thermal infrared image with the vertical direction, further comprises:
counting the number of non-zero temperature matrix elements in each row of the temperature matrix, and storing the number as a pixel number array containing M number of array elements, wherein each number of array elements in the pixel number array is the number of non-zero temperature matrix elements in the row corresponding to the array elements in the temperature matrix.
5. The method for identifying fissures in a mine exploitation surface conduction goaf according to claim 1, wherein calculating the temperature of the fissures in each pixel row, the width of each pixel row and the accumulated length of the fissures before calculating the fissures in the thermal infrared image based on the temperature matrix and the included angle of each pixel row of the fissures in the thermal infrared image with the vertical direction, further comprises:
Determining a pixel row of the crack, which is covered by the crack in the thermal infrared image, and deleting temperature matrix elements corresponding to other pixel rows except the crack pixel row in a temperature matrix;
Determining a first pixel row of a crack in the thermal infrared image according to the pixel row of the crack starting end;
correcting the row number of the temperature matrix element corresponding to the slit pixel row in the temperature matrix as follows: the original line number of the temperature matrix element is subtracted from the original line number of the temperature matrix element corresponding to the first pixel line of the crack, and then one is added.
6. The method for identifying fissures in a mine exploitation surface conduction goaf according to claim 1, wherein calculating the temperature of the fissures in each pixel row, the width of each pixel row and the accumulated length of the fissures before calculating the fissures in the thermal infrared image based on the temperature matrix and the included angle of each pixel row of the fissures in the thermal infrared image with the vertical direction, further comprises:
determining pixels of a size reference object from the thermal infrared image, wherein the size reference object is arranged on the ground surface photographed by the unmanned aerial vehicle in advance;
Acquiring the real size of a size reference object;
the resolution n of the thermal infrared image is determined based on the true size of the size reference and the pixels of the size reference.
7. The mine exploitation earth surface conduction goaf fracture identification method is characterized by comprising the following steps of:
The method for acquiring the thermal infrared image of the ground surface obtained by carrying the thermal infrared imaging device on the unmanned aerial vehicle according to the designed route and the aerial altitude flight specifically comprises the following steps: acquiring a thermal infrared image of the earth surface obtained by the unmanned aerial vehicle carrying the thermal infrared imaging device in the daytime and a thermal infrared image of the earth surface obtained in the early morning according to a designed route and a designed voyage flight, taking the thermal infrared image of the earth surface obtained in the daytime as the thermal infrared image of the daytime, and taking the thermal infrared image of the earth surface obtained in the early morning as the thermal infrared image of the early morning;
Judging whether the fracture conducts the goaf according to the relation of the fracture temperature and the fracture accumulation length and the relation of the fracture width and the fracture accumulation length, wherein the method specifically comprises the following steps of:
Judging that the crack is conducted in the goaf if the relation of the crack temperature and the crack accumulation length calculated based on the daytime thermal infrared image is inversely related to the relation of the crack width and the crack accumulation length, and the relation of the crack temperature and the crack accumulation length calculated based on the early morning thermal infrared image is not related to the relation of the crack width and the crack accumulation length;
If the relation of the crack temperature and the crack accumulation length obtained based on the early morning thermal infrared image is positively correlated with the relation of the crack width and the crack accumulation length, the crack cannot be identified in the thermal infrared image of the earth surface obtained in the daytime, and the crack cannot be conducted in the thermal infrared image of the earth surface obtained in the early morning, judging the goaf.
8. An electronic device, comprising:
at least one processor; and
A memory communicatively coupled to at least one of the processors; wherein,
The memory stores instructions executable by at least one of the processors to enable the at least one of the processors to perform the mine mining surface conduction goaf fracture identification method of any one of claims 1 to 7.
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