CN116597389B - Geological disaster monitoring and early warning method based on image processing - Google Patents
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Abstract
The invention relates to the technical field of image data processing, in particular to a geological disaster monitoring and early warning method based on image processing, which comprises the following steps: collecting a geological overlook image, carrying out grey-scale treatment on the geological overlook image, carrying out binarization and region division on the geological overlook image after grey-scale treatment, obtaining a mark sequence of a divided region, calculating the crack edge rate in the divided region according to the mark sequence, obtaining a mark image according to the crack edge rate, obtaining the crack edge of an accurate geological collapse region by using the mark image, carrying out density clustering on the crack edge of the accurate geological collapse region, and predicting the collapse region. According to the method, the acquired geological image is analyzed, the marked image is acquired according to the characteristics of the cracks of the geological collapse area to carry out watershed segmentation, so that the excessive segmentation of a watershed algorithm is avoided; and after the crack edge of the suspected collapse area is obtained, obtaining an accurate crack edge in combination with the feature, and predicting the collapse area.
Description
Technical Field
The invention relates to the technical field of image data processing, in particular to a geological disaster monitoring and early warning method based on image processing.
Background
Geological disasters refer to disasters caused by damage and rupture of ground surface layers or substances such as underground rocks and soil, wherein ground subsidence is a common geological disaster, and mainly refers to underground cavities formed by damage of overlying strata, so that rock and soil bodies sink or collapse in the cavities to form collapse pits with different forms on the ground surface. The collapse is not completely without symptoms, and cracks surrounding the collapse pit are often formed in the collapse area before the collapse, so that annular or arc-shaped cracks with unequal sizes are formed, and the initially smaller cracks can be gradually expanded to cause geological collapse along with the time, so that early warning is needed to be generated when the situation that the ground is above is found to prompt people to bypass.
The traditional watershed segmentation algorithm has good response to weak edges in the image, but is sensitive to textures and noise in the gradient image because of the self texture characteristics of the uncollapsed area, and easily causes an oversarting phenomenon, which can lead to meaningless segmentation results, so that the image needs to be adaptively marked.
Disclosure of Invention
The invention provides a geological disaster monitoring and early warning method based on image processing, which aims to solve the existing problems.
The geological disaster monitoring and early warning method based on image processing adopts the following technical scheme:
the embodiment of the invention provides a geological disaster monitoring and early warning method based on image processing, which comprises the following steps:
acquiring a gray level image of a geological overlook image;
carrying out mean value filtering treatment on a gray level image of the geological overlooking image to obtain a filtered image, marking the gray level value of a pixel point in the filtered image as 1 when the gray level value of the pixel point in the filtered image is larger than the gray level value of the pixel point in the corresponding gray level image, marking the gray level value of the pixel point in the filtered image as 0 when the gray level value of the pixel point in the filtered image is smaller than or equal to the gray level value of the pixel point in the corresponding gray level image, obtaining a binary image, and dividing the binary image into areas;
acquiring a marking sequence of a dividing region, acquiring the jump number of the marking sequence according to the marking change in the marking sequence, acquiring the crack edge rate of the marking sequence according to the jump number of the marking sequence and the number of the continuous marks in the marking sequence, acquiring the crack edge rate of the dividing region according to the crack edge rate of the marking sequence, and acquiring a marking image according to the crack edge rate of the dividing region;
dividing watershed according to the marked image to obtain a divided area, obtaining the difference of two different divided edges according to the curvature of pixel points on any two different divided edges in the divided area, and obtaining the edge of the target crack according to the difference;
and carrying out density clustering on the edges of the target cracks, and predicting a collapsed region.
Further, the specific acquisition method for acquiring the mark sequence of the divided region is as follows:
judging whether other pixel points marked as 1 exist in eight adjacent areas of the starting point by taking the pixel point marked as 1 nearest to the upper left corner pixel point in any divided area as the starting point, if not, re-selecting the pixel point marked as 1 nearest to the upper left corner pixel point in the divided area as a new starting point, and judging in the eight adjacent areas of the new starting pointIf other pixel points marked as 1 exist, if other pixel points marked as 1 exist in the eight neighborhood of the selected new starting point, a two-dimensional Cartesian coordinate system is established by taking the selected new starting point as a coordinate center, the angle marks of the other pixel points marked as 1 in the eight neighborhood of the new starting point are counted, the mark with the included angle of 0-90 degrees is marked as "+", the mark with the included angle of 90-180 degrees is marked as "-", and the mark with the included angle of 180-270 degrees is marked as ";"marking the angle between 270-360 degrees as">And counting the included angles of the pixel points with the mark value of 1 in the eight neighborhood of the starting point by taking the pixel points with the mark of 1 in the eight neighborhood of the new starting point as the starting point and establishing a two-dimensional Cartesian coordinate system, carrying out angle marking according to the degrees of the included angles, traversing in sequence until the pixel points with the mark value of 1 are not in the eight neighborhood of the starting point, obtaining a mark sequence, and obtaining all the mark sequences in any divided area.
Further, the step of obtaining the crack edge rate of the marking sequence according to the jump number of the marking sequence and the number of the continuous marks in the marking sequence comprises the following specific steps:
，
in the method, in the process of the invention,for the +.>The number of hops of the tag sequence,/-, is->An exponential function with a natural constant as a base, +.>As a hyperbolic tangent function;
the acquisition method of (1) is as follows: marking fragments of the marker sequence with the same and continuously distributed markers and the number of the continuous markers being more than or equal to 2 as continuous markers in the marker sequence to obtain the +.>The number of consecutive tags in the tag sequence is denoted +.>；
Is->The number of markers comprised by the longest consecutive marker in the marker sequence,/->Is->The>The number of marks included in each successive mark, +.>For the +.>Crack edge rate for each marker sequence.
Further, the step of obtaining the crack edge rate of the dividing region according to the crack edge rate of the marking sequence and obtaining the marking image according to the crack edge rate of the dividing region comprises the following specific steps:
and acquiring the crack edge rates of all the mark sequences in any one of the divided areas, selecting the maximum crack edge rate in the mark sequences as the crack edge rate of the divided areas, presetting an edge threshold, marking the divided areas with the crack edge rate larger than the edge threshold as 0 when the crack edge rate of the divided areas is larger than the edge threshold, and marking the divided areas with the crack edge rate smaller than or equal to the edge threshold as 1 when the crack edge rate of the divided areas is smaller than or equal to the edge threshold, so as to finally obtain the marked image.
Further, the method obtains the difference of two different dividing edges according to the curvature of the pixel points on any two different dividing edges in the dividing region, and comprises the following specific steps:
marking any one parting line in the parting region as a current edge, and marking the other parting line except the current edge in the parting region as a comparison edge;
，
in the method, in the process of the invention,for the current edge->Curvature of individual pixels, +.>For the total number of pixels on the current edge, < >>To compare the%>Curvature of individual pixels, +.>To compare the total number of pixels on the edge, +.>For the current edge to ratioDistance between circle centers of edges +.>As hyperbolic tangent function, +.>Representing the difference between the current edge and the comparison edge.
Further, the distance between the centers of the circles of the current edge and the comparison edge is specifically as follows:
and performing circle fitting on the current edge and the comparison edge by using a least square method to obtain the circle center positions of the current edge and the comparison edge, and obtaining the distance between the circle centers of the current edge and the comparison edge according to the circle center positions.
Further, the method for obtaining the edge of the target crack according to the difference comprises the following specific steps:
and acquiring the difference between any one edge and all other edges, presetting a difference threshold, taking two edges corresponding to the difference threshold as crack edges of a collapse area, when the difference is larger than or equal to the difference threshold, indicating that the crack edges which are not the collapse area exist in the two edges, acquiring the difference between all edges and other edges, and obtaining an edge set with the difference smaller than the difference threshold, wherein the edges in the edge set are marked as target crack edges.
The technical scheme of the invention has the beneficial effects that: the acquired geological image is analyzed, and the mark image is acquired according to the characteristics of the cracks of the geological collapse area to carry out watershed segmentation, so that the excessive segmentation of a watershed algorithm is avoided; and after the crack edge of the suspected collapse area is obtained, obtaining an accurate crack edge in combination with the feature, and predicting the collapse area.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the image processing-based geological disaster monitoring and early warning method of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of the geological disaster monitoring and early warning method based on image processing according to the invention, which is provided by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the geological disaster monitoring and early warning method based on image processing provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a geological disaster monitoring and early warning method based on image processing according to an embodiment of the present invention is shown, and the method includes the following steps:
and S001, collecting a geological overlook image and carrying out grey-scale treatment on the geological overlook image.
Specifically, a camera is installed at the bottom of the unmanned aerial vehicle, the unmanned aerial vehicle with the camera is utilized to shoot a geological overlook image in the air, after the geological overlook image is obtained, the geological overlook image is subjected to graying, and a gray level map of the geological overlook image is obtained, which is biased to the analysis of the subsequent steps.
So far, the unmanned aerial vehicle carrying the shooting camera acquires a geological overlooking image, and the geological overlooking image after graying is obtained.
It should be noted that, ground subsidence is a relatively common geological disaster, mainly refers to that an overlying strata is destroyed to form a subsurface cavity, so that a rock-soil body is sunk or collapses in the cavity, collapse pits with different forms are formed on the ground surface, the collapse is not completely free of symptoms, cracks surrounding the collapse pits are often formed in a subsidence area before collapse, annular or arc-shaped cracks with different sizes are formed, texture features exist in a geological surface area, and meanwhile, edges of the collapse area cannot be positioned due to inaccuracy of edges detected by an edge detection algorithm because part of the cracks are relatively small, a watershed segmentation algorithm has good response to weak edges in an image, but the watershed segmentation algorithm is sensitive to textures and noise in a gradient image because of the fact that the normal area of the geological surface also has self texture features, and overspartitional phenomenon is easily caused.
Further, in order to avoid the over-division phenomenon, the following description is made. According to the embodiment, the image is divided into the areas, the crack edge rate of each area is obtained, the marked image is obtained according to the crack edge rate of each area, and watershed segmentation is carried out according to the marked image, so that excessive segmentation is avoided. And obtaining the accurate edge of the collapse area according to the characteristics of the edge of the collapse area.
And step S002, binarizing and dividing the grey geological overlook image into areas.
It should be noted that, because the geological region has self texture features and the gray value distribution is discrete, for the crack edge, the gray value of the pixel point is smaller, the gray value of the edge pixel point after the mean value filtering treatment is influenced by the surrounding brighter pixel point, so that the gray value is larger, the filtered image and the gray value of the pixel point corresponding to the gray image are compared to obtain a binary image, and the binary image is further divided into regions.
Specifically, the average value filtering processing is performed on the gray level image of the geological overlooking image to obtain a filtered image, the gray level value of the pixel point at the corresponding position in the filtered image is compared with the gray level value of the pixel point in the gray level image, when the gray level value of the pixel point in the filtered image is larger than the gray level value of the pixel point in the corresponding gray level image, the gray level value of the pixel point is marked as 1, and when the gray level value of the pixel point in the filtered image is smaller than or equal to the gray level value of the pixel point in the corresponding gray level image, the gray level value of the pixel point is marked as 0, so that a pair of binary images is obtained.
Further, the binary image is processedThe equal division of the regions is described by taking n=10 as an example in this embodiment.
Thus, each divided region of the binary image is obtained.
And S003, acquiring a mark sequence of the dividing region, calculating the crack edge rate in the dividing region according to the mark sequence, and obtaining a mark image according to the crack edge rate.
It should be noted that, in step S002, a binary image has been acquired and a split region operation has been performed, in which the pixel point of the crack edge in the collapsed region is marked as 1, and since the crack edge satisfies a certain direction extension, if only one pixel point is isolated, the point is unlikely to be a crack edge point, and the crack edge rate of each split region is calculated according to the direction characteristics of the crack edge.
Specifically, the pixel point marked as 1 nearest to the upper left corner pixel point in any divided area is taken as a starting point, whether other pixel points marked as 1 exist in the eight neighborhood of the starting point is judged, if no other pixel points marked as 1 exist in the eight neighborhood of the selected starting point, the pixel point marked as 1 nearest to the upper left corner pixel point in the divided area is selected again as a new starting point, whether other pixel points marked as 1 exist in the eight neighborhood of the new starting point is judged, if other pixel points marked as 1 exist in the eight neighborhood of the selected new starting point, a two-dimensional Cartesian coordinate system is established by taking the selected new starting point as a coordinate center, the angle marks of the other pixel points marked as 1 in the eight neighborhood of the new starting point, the marks as "+" sign the other pixel points with the included angles between 0 and 90 degrees, the marks as "-" the included angles between 180 and 270 degrees, and the marks as "-"."marking the angle between 270-360 degrees as">And counting the included angles of the pixel points with the mark value of 1 in the eight neighborhood of the starting point by taking the pixel points with the mark of 1 in the eight neighborhood of the new starting point as the starting point and establishing a two-dimensional Cartesian coordinate system, carrying out angle marking according to the degrees of the included angles, traversing in sequence until the pixel points with the mark value of 1 are not in the eight neighborhood of the starting point, obtaining a mark sequence, and obtaining all the mark sequences in any divided area.
It should be noted that, the multiple marker sequences obtained after the statistics are all continuous in the divided regions, i.e. all eight neighborhood connections.
Further, the jump times of the marking sequence are obtained according to the marking change in the marking sequence, and the crack edge rate is obtained according to the jump times of the marking sequence and the number of the continuous marks in the marking sequence, specifically as follows:
，
in the method, in the process of the invention,for the +.>The number of hops of the tag sequence,/-, is->An exponential function with a natural constant as a base, +.>Is a hyperbolic tangent function used for normalization;
the acquisition method of (1) is as follows: the tag sequence will be identical in tagMarking fragments of the marking sequence which are continuously distributed and have the continuous marking number of more than or equal to 2 as continuous marks to obtain the +.>The number of consecutive tags in the tag sequence is denoted +.>；
Is->The number of markers comprised by the longest consecutive marker in the marker sequence,/->Is->The>The number of marks included in each successive mark, +.>For the +.>Crack edge rate for each marker sequence.
When the number of hops of the marking sequence is larger, the probability that the sequence contains the edge sequence is smaller,for the significance of the longest continuous segment number in the current marking sequence, when the significance of the longest continuous segment number in the current marking sequence is larger, the edge rate of the crack in the current marking sequence is larger, and the mark is indicated as +.>As the wayThe probability of containing cracks in the sequence is high when the number of hops of the sequence is smaller and the significance of the number of longest continuous segments is larger, namely the crack edge rate in the region corresponding to the sequence is high.
Further, the crack edge rate of all the mark sequences in any one of the divided areas is obtained, the largest crack edge rate in the mark sequences is selected as the crack edge rate of the divided areas, an edge threshold is preset, when the crack edge rate of the divided areas is larger than the edge threshold, the divided areas possibly contain the edges of the geological collapse areas, the divided areas with the crack edge rate larger than the edge threshold of the divided areas are marked as 0, when the crack edge rate of the divided areas is smaller than or equal to the edge threshold, the divided areas do not contain the edges of the geological collapse areas, and the divided areas with the crack edge rate smaller than or equal to the edge threshold of the divided areas are marked as 1, so that the marked image is finally obtained. In this embodiment, an edge threshold of 0.6 is taken as an example.
Thus, a marker image is acquired.
And S004, acquiring the accurate crack edge of the geological collapse area by using the marked image.
Specifically, a gradient image of a gray image is obtained by using a sobel operator, and watershed segmentation is performed on the gray image by using the gradient image based on a mark image, so that a plurality of segmentation areas are obtained.
The method is characterized in that the split edges of the split areas are the crack edges of the possible geological collapse areas, the characteristics of the edges of the collapse areas are combined to obtain the accurate geological collapse edges, the cracks of the known collapse areas are annular cracks formed around the collapse areas, the difference among the cracks is calculated according to the characteristics, and the cracks with small difference are the cracks of the collapse areas.
Specifically, any one of the divided edges in the divided region is designated as a current edge, and another divided edge other than the current edge in the divided region is designated as a comparison edge, so that the following description is facilitated. And performing circle fitting on the current edge and the comparison edge by using a least square method to obtain the circle center positions of the current edge and the comparison edge.
Further, the difference between the current edge and the comparison edge is obtained according to the curvatures of the current edge pixel point and the comparison edge pixel point, specifically as follows:
，
in the method, in the process of the invention,for the current edge->Curvature of individual pixels, +.>For the total number of pixels on the current edge, < >>To compare the%>Curvature of individual pixels, +.>To compare the total number of pixels on the edge, +.>For the distance between the centre of the current edge and the centre of the comparison edge, < >>Is a hyperbolic tangent function for normalization, +.>Representing the difference between the current edge and the comparison edge.
It should be noted that the number of the substrates,representing the average curvature of the current edge +.>Representing the average curvature of the comparison edge, a smaller difference in average curvature of the two edges indicates a smaller difference in the two edges, i.e., the two edges may be edges of the collapsed region, and a smaller distance between the current edge and the center of the comparison edge indicates a smaller difference in the two edges, i.e., the two edges may be cracks of the collapsed region.
Further, the difference between any one edge and all other edges is obtained, a difference threshold is set, two edges corresponding to the difference threshold are used as crack edges of a collapse area, when the difference is larger than or equal to the difference threshold, the crack edges which are not the collapse area exist in the two edges, the difference between all edges and other edges is obtained, an edge set with the difference smaller than the difference threshold is obtained, and the edges in the edge set are marked as target crack edges, namely the accurate crack edges of the geological collapse area. In this embodiment, the difference threshold is 0.4.
Thus, the accurate crack edge of the geological collapse area is obtained.
And S005, performing density clustering on the crack edges of the accurate geological collapse area, and predicting the collapse area.
Specifically, DBSCAN clustering is performed on the target crack edge pixel points obtained in the step S004, and convex hull detection is performed on the clustering result to generate a collapse area.
And early warning can be made in advance according to the predicted collapse area to remind pedestrians to detour.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. The geological disaster monitoring and early warning method based on image processing is characterized by comprising the following steps of:
acquiring a gray level image of a geological overlook image;
carrying out mean value filtering treatment on a gray level image of the geological overlooking image to obtain a filtered image, marking the gray level value of a pixel point in the filtered image as 1 when the gray level value of the pixel point in the filtered image is larger than the gray level value of the pixel point in the corresponding gray level image, marking the gray level value of the pixel point in the filtered image as 0 when the gray level value of the pixel point in the filtered image is smaller than or equal to the gray level value of the pixel point in the corresponding gray level image, obtaining a binary image, and dividing the binary image into areas;
acquiring a marking sequence of a dividing region, acquiring the jump number of the marking sequence according to the marking change in the marking sequence, acquiring the crack edge rate of the marking sequence according to the jump number of the marking sequence and the number of the continuous marks in the marking sequence, acquiring the crack edge rate of the dividing region according to the crack edge rate of the marking sequence, and acquiring a marking image according to the crack edge rate of the dividing region;
dividing watershed according to the marked image to obtain a divided area, obtaining the difference of two different divided edges according to the curvature of pixel points on any two different divided edges in the divided area, and obtaining the edge of the target crack according to the difference;
carrying out density clustering on the edges of the target cracks, and predicting a collapsed region;
the specific acquisition method for acquiring the mark sequence of the divided region comprises the following steps:
judging whether other 1-marked pixel points exist in the eight-neighborhood of the starting point by taking the 1-marked pixel point closest to the upper left corner pixel point in any one of the divided areas as a starting point, if no other 1-marked pixel points exist in the eight-neighborhood of the selected starting point, reselecting the 1-marked pixel point closest to the upper left corner pixel point in the divided areas as a new starting point, judging whether other 1-marked pixel points exist in the eight-neighborhood of the new starting point, if other 1-marked pixel points exist in the eight-neighborhood of the selected new starting point, establishing a two-dimensional Cartesian coordinate system by taking the selected new starting point as a coordinate center, counting the angle marks of the other 1-marked pixel points in the eight-neighborhood of the new starting point, marking the angle between 0 and 90 degrees as "+" and marking the angle between 90 and 18 degrees as "+"0 degree is marked as "-" and 180-270 degrees is marked as "","marking the angle between 270-360 degrees as">And counting the included angles of the pixel points with the mark value of 1 in the eight neighborhood of the starting point by taking the pixel points with the mark of 1 in the eight neighborhood of the new starting point as the starting point and establishing a two-dimensional Cartesian coordinate system, carrying out angle marking according to the degrees of the included angles, traversing in sequence until the pixel points with the mark value of 1 are not in the eight neighborhood of the starting point, obtaining a mark sequence, and obtaining all the mark sequences in any divided area.
2. The geological disaster monitoring and early warning method based on image processing according to claim 1, wherein the step of obtaining the crack edge rate of the marking sequence according to the jump number of the marking sequence and the number of the continuous marks in the marking sequence comprises the following specific steps:
，
in the method, in the process of the invention,for the +.>The number of hops of the tag sequence,/-, is->An exponential function with a natural constant as a base, +.>As a hyperbolic tangent function;
the acquisition method of (1) is as follows: marking fragments of the marker sequence with the same and continuously distributed markers and the number of the continuous markers being more than or equal to 2 as continuous markers in the marker sequence to obtain the +.>The number of consecutive tags in the tag sequence is denoted +.>；
Is->The number of markers comprised by the longest consecutive marker in the marker sequence,/->Is->The>The number of marks included in each successive mark, +.>For the +.>Crack edge rate for each marker sequence.
3. The geological disaster monitoring and early warning method based on image processing according to claim 1, wherein the step of obtaining the crack edge rate of the divided area according to the crack edge rate of the marking sequence and obtaining the marking image according to the crack edge rate of the divided area comprises the following specific steps:
and acquiring the crack edge rates of all the mark sequences in any one of the divided areas, selecting the maximum crack edge rate in the mark sequences as the crack edge rate of the divided areas, presetting an edge threshold, marking the divided areas with the crack edge rate larger than the edge threshold as 0 when the crack edge rate of the divided areas is larger than the edge threshold, and marking the divided areas with the crack edge rate smaller than or equal to the edge threshold as 1 when the crack edge rate of the divided areas is smaller than or equal to the edge threshold, so as to finally obtain the marked image.
4. The geological disaster monitoring and early warning method based on image processing according to claim 1, wherein the difference between two different dividing edges is obtained according to the curvature of the pixel points on any two different dividing edges in the dividing region, comprising the following specific steps:
marking any one parting line in the parting region as a current edge, and marking the other parting line except the current edge in the parting region as a comparison edge;
，
in the method, in the process of the invention,for the current edge->Curvature of individual pixels, +.>For the total number of pixels on the current edge, < >>To compare the%>Curvature of individual pixels, +.>To compare the total number of pixels on the edge, +.>For the distance between the centre of the current edge and the centre of the comparison edge, < >>As hyperbolic tangent function, +.>Representing the difference between the current edge and the comparison edge.
5. The geological disaster monitoring and early warning method based on image processing according to claim 4, wherein the distance between the centers of the circles of the current edge and the comparison edge is as follows:
and performing circle fitting on the current edge and the comparison edge by using a least square method to obtain the circle center positions of the current edge and the comparison edge, and obtaining the distance between the circle centers of the current edge and the comparison edge according to the circle center positions.
6. The geological disaster monitoring and early warning method based on image processing according to claim 1, wherein the obtaining the target crack edge according to the difference comprises the following specific steps:
and acquiring the difference between any one edge and all other edges, presetting a difference threshold, taking two edges corresponding to the difference threshold as crack edges of a collapse area, when the difference is larger than or equal to the difference threshold, indicating that the crack edges which are not the collapse area exist in the two edges, acquiring the difference between all edges and other edges, and obtaining an edge set with the difference smaller than the difference threshold, wherein the edges in the edge set are marked as target crack edges.
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