CN115311260A - Road surface quality detection method for highway traffic engineering - Google Patents
Road surface quality detection method for highway traffic engineering Download PDFInfo
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Abstract
The invention relates to the field of image data processing, in particular to a road surface quality detection method for highway traffic engineering. The method comprises the steps of obtaining target images corresponding to a target road surface according to obtained frame shooting images corresponding to the target road surface, wherein each target image comprises a complete crack; calculating crack type judgment indexes corresponding to the target images, and judging the crack types of the target images according to the crack type judgment indexes; the crack type evaluation index comprises a ratio of horizontal and vertical fluctuation ranges, difference degrees of row and average wave crests and column and average wave crests, fluctuation degrees of rows and curves and fluctuation degrees of columns and curves; and estimating the crack width of the crack according to the number of pixel points occupied by the total area of the crack on each target image and the number of edge pixel points. The method realizes the judgment of the crack type of the target road surface and the estimation of the crack width based on the image of the target road surface obtained by shooting, and also realizes the detection of the road surface quality of the road traffic engineering.
Description
Technical Field
The invention relates to the field of image data processing, in particular to a road surface quality detection method for highway traffic engineering.
Background
As infrastructure construction in China, the highway not only meets the traveling requirements of people, but also has a fundamental effect on the development of economy, so that the quality of highway engineering must be ensured. However, the quality of the roadbed and the pavement of the highway at present has some problems, and the development of highway safety business is influenced.
The cracks are one of the most common, most easily-occurring and earliest-occurring diseases in various damages of the road surface, can influence the road appearance and the driving comfort, are easy to expand to cause structural damage of the road surface, and shorten the service life of the road surface. Therefore, the cracks appear on the road surface, the sealing repair is required to be carried out in time, otherwise, rainwater and other impurities can enter the surface layer structure and the roadbed along the cracks, the bearing capacity of the road surface is reduced, and the local or flaky damage of the road surface is accelerated. The crack types mainly comprise longitudinal cracks, transverse cracks and tortoise-shaped net cracks, the causes of the cracks are different, the maintenance requirements of the cracks with different widths are different, and how to judge the type and the corresponding severity of the pavement cracks is realized has important significance for the development of highway maintenance work.
Disclosure of Invention
The invention aims to provide a road traffic engineering road surface quality detection method in order to judge the type and severity of a road surface crack.
The invention provides a road surface quality detection method for a road traffic engineering, which comprises the following steps:
acquiring each frame of shot image corresponding to a target road surface, and carrying out graying and reverse processing on each frame of shot image corresponding to the target road surface to obtain each frame of grayscale reverse image corresponding to the target road surface;
enhancing and binarizing each frame of gray-scale reverse image corresponding to the target road surface, judging whether each frame of image is a suspected crack image according to row and column data corresponding to each processed frame of image, and recording each suspected crack image as each target image if each suspected crack image exists in each processed frame of image corresponding to the target road surface and no suspected crack image exists in an adjacent frame of image; if the adjacent frame suspected crack images exist, performing image splicing processing on the adjacent frame suspected crack images to obtain 1 or more target images, wherein each target image comprises a complete crack;
calculating a crack type judgment index corresponding to each target image, and judging the crack type of each target image according to the crack type judgment index; the crack type evaluation index comprises a ratio of a horizontal fluctuation range to a vertical fluctuation range, a difference degree between a row and an average peak and between a column and the average peak, a fluctuation degree of the row and a curve, and a fluctuation degree of the column and the curve; and estimating the crack width of the crack according to the number of pixel points occupied by the total area of the crack on each target image and the number of edge pixel points.
Further, the determining whether each frame image is a suspected crack image according to the row and column data corresponding to each processed frame image includes:
and performing accumulation analysis on pixel values in each frame of binarized image according to the directions of rows and columns, and calculating a row and column white pixel accumulation curve, wherein the formula is as follows:
wherein the content of the first and second substances,
the second in the row pixel accumulation sequence corresponding to the image after binarization of a certain frame
The row and the column are then shifted,
the first in the column gray scale accumulation sequence corresponding to the image after the frame binarization
The sum of the columns is given by,
the total number of rows and the total number of columns corresponding to the image after the frame binarization;
representing the binarized image of the frame
Go to the first
Pixel values of column pixels;
if the image after binarization of a certain frame continuously meets the requirement
Is greater than
Or/and continuously satisfy
Is greater than
And judging the image after the frame binarization as a suspected crack image.
Further, the ratio of the horizontal and vertical fluctuation ranges corresponding to the target images is calculated by using the following formula:
wherein the content of the first and second substances,
is the ratio of the horizontal and vertical fluctuation range corresponding to a certain target image,
in the target image
The lower limit value of the line coordinates of (c),
in the target image
The upper limit value of the line coordinate of (a);
in the target image
The lower limit value of the column coordinates of (c),
in the target image
The upper limit value of the column coordinates of (c),
is as follows
The sum of the row pixel values is then,
is a first
The sum of the row pixel values.
Further, the difference degree between the row and the average peak corresponding to each target image and the column and the average peak is calculated by using the following formula:
wherein, the first and the second end of the pipe are connected with each other,
the degree of difference between the row and average peak and the column and average peak of a certain target image,
the size of the peak on the line and curve of the target image,
for the number of peaks on the line and curve of the target image,
for the column of the target image and the size of the peak on the curve,
the number of peaks on the column and curve for the target image.
Further, the line and curve fluctuation degrees and the column and curve fluctuation degrees corresponding to the target images are calculated by the following formulas:
wherein the content of the first and second substances,
the extent of fluctuation of the lines and curves of a certain target image,
for the degree of fluctuation of the columns and curves of the target image,
for the fluctuation ratio of the lines and curves of the target image,
for the fluctuation ratio of the columns and curves of the target image,
for the average peak to valley amplitude variation on the line and curve of the target image,
for the average peak-to-valley amplitude variation on the column and curve of the target image,
summing the absolute values of the differences between all adjacent peaks and valleys on the line and curve of the target image,
summing the absolute values of the differences between all adjacent peaks and troughs on the column and curve of the target image,
is the number of the line and the peak value on the curve of the target imageThe amount of the compound (A) is,
for the columns of the target image and the number of peaks on the curve,
in the target image
The lower limit value of the line coordinates of (a),
in the target image
Upper limit value of the row coordinate of (1);
in the target image
The lower limit value of the column coordinates of (c),
in the target image
The upper limit value of the column coordinates of (c),
is as follows
The sum of the row pixel values is then,
is as follows
The sum of the row pixel values.
Further, the fracture types include transverse fractures, longitudinal fractures, and fissured network fractures.
Further, the judging the crack type of each target image according to the crack type evaluation index includes:
ratio of horizontal to vertical fluctuation range
The degree of difference between the row and column average peaks and the average peak
And degree of line and curve fluctuation
Extent of column and curve fluctuation
Judging that a transverse crack exists at the position corresponding to the target image;
ratio of horizontal to vertical fluctuation range
The degree of difference between the row and column average peaks and the average peak
And degree of line and curve fluctuation
Extent of column and curve fluctuation
Judging that a longitudinal crack exists at the position corresponding to the target image;
if the former two conditions are not satisfied, the existence of the crack net-shaped crack at the corresponding position of the target image is judged.
Further, the estimating the crack width according to the number of pixel points occupied by the total crack area on each target image and the number of edge pixel points includes:
accumulating the rows and the longitudinal coordinate values on the curve, and estimating the number of pixels occupied by the total area of the cracks as follows:
wherein the content of the first and second substances,
the total area of the crack of a certain target image occupies the number of pixels,
for the total number of columns of the target image,
is a first
The sum of the row pixel values;
canny edge detection is carried out on the target image to extract edges, and the total value of edge pixels is recorded as
Then the seam width estimation formula is:
wherein the content of the first and second substances,
the width of the crack on the target image is taken as the width of the crack,
in proportional units to the real size of the pixel.
Has the advantages that: the method realizes the judgment of the crack type of the target pavement and the estimation of the crack width of the target pavement based on the image of the target pavement obtained by shooting, namely realizes the detection of the pavement quality of highway traffic engineering, and can realize the targeted maintenance of the target pavement based on the obtained crack type and the crack width.
Drawings
FIG. 1 is a flow chart of a method for detecting road surface quality in road traffic engineering according to the present invention;
FIG. 2 is a gray scale inversion plot of the transverse fractures of the present invention;
FIG. 3 is a gray scale inversion plot of longitudinal cracks of the present invention;
FIG. 4 is a grayscale inversion of a cracked web crack of the present invention;
FIG. 5 is a schematic diagram of rows and columns and curves of transverse slits of the present invention;
FIG. 6 is a schematic diagram of the rows and columns and curves of longitudinal fractures of the present invention;
fig. 7 is a schematic diagram of rows and columns and curves of a crazing network of cracks of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention.
In order to realize the judgment of the type and the severity of the pavement crack, the embodiment provides a method for detecting the pavement quality of the highway traffic engineering, as shown in fig. 1, the method comprises the following steps:
(1) Acquiring each frame of shot image corresponding to a target road surface, and carrying out graying and reverse processing on each frame of shot image corresponding to the target road surface to obtain each frame of grayscale reverse image corresponding to the target road surface;
in the using process of the highway, various quality problems are generated on the pavement, and pavement cracks are the most common road diseases. In order to improve the material proportion of the road for subsequent construction, the type of the pavement crack and the crack width need to be effectively estimated, and an accurate suggestion is provided for road maintenance.
In order to effectively estimate the type and width of a crack of a target pavement, a shot image of the target pavement is obtained in the embodiment, and the shot image is ensured to be a horizontal image of the pavement in the shooting process.
In this embodiment, the actual shooting size corresponding to the image obtained by shooting is a square, and the pixel size of the image obtained by shooting is recorded as
. In this embodiment, the captured image is a color RGB image, the color RGB image is a three-layer channel, and in order to reduce the amount of calculation, the RGB image is subjected to graying processing to obtain a grayscale image with only one layer of channel. Specifically, in this embodiment, the RGB three components are weighted and averaged according to a psychological formula, and in a gray scale image obtained through graying, the lowest gray value 0 is black, and the highest gray value 255 is white. Then carrying out gray scale reversal operation, and subtracting the gray scale value corresponding to each pixel point from 255 to obtain a gray scale reversal image; the gray scale inverse map highlights crack defect information and is convenient to analyze, and for example, fig. 2, 3 and 4 are gray scale inverse maps of transverse, longitudinal and cracking reticular cracks respectively.
(2) Enhancing and binarizing each frame of gray-scale reverse image corresponding to the target road surface, judging whether each frame of image is a suspected crack image according to row and column data corresponding to each processed frame of image, and if a suspected crack image exists in each processed frame of image corresponding to the target road surface and no adjacent frame of suspected crack image exists, marking each suspected crack image as each target image; if the adjacent frame suspected crack images exist, performing image splicing processing on the adjacent frame suspected crack images to obtain 1 or more target images, wherein each target image comprises a complete crack;
whether it is the low or high gray value portion of the crack, it is characterized by significant pixel differences from the background. Because the introduction of noise in the acquisition process and the characteristics of the road texture determine that the road image contains a large amount of noise, the gray scale reverse image needs to be enhanced before further processing, namely, the gray scale reverse image is subjected to preliminary denoising processing.
And then, the denoised image is subjected to binarization processing, and the obtained image may contain crack defects and may be a perfect road surface, so that the automatic threshold binarization of the image has great interference. In view of this, the embodiment uses the OSTU large law binarization method for the denoised standard transverse crack image to extract the threshold with better segmentation effect
. The OSTU is also called a maximum inter-class difference method, and realizes automatic selection of a global threshold by counting histogram characteristics of the whole image, and a specific selection process is the prior art and is not described herein any further. The threshold is applied after each frame of gray reverse image is enhanced
Pixel value in the image is greater than
Setting the pixels of (1) as foreground, namely setting the foreground area (corresponding to the suspected pavement crack area) as white (the pixel value is 1); is less than
I.e. the background area (corresponding to the road pavement area) is set to black (pixel value 0).
In the actually collected image, the generation positions of the cracks have randomness (appear at different positions in the image), the road surface crack measuring vehicle is dynamically driven, so the shooting device is also dynamic, the shot images show continuous characteristics, and a plurality of frames of images containing the cracks can correspond to the same crack.
In the embodiment, the running speed of the vehicle for measuring the road surface crack and the frequency of the image shot by the shooting device (camera) are controlled so that the overlapping ratio of two adjacent images is about 50 percent, and the images corresponding to the target road surface are recorded
Sheets, each image being marked
If the current shot image is
Then the previous picture is numbered
And each image has its corresponding geographical location information, giving coordinates for subsequent maintenance work.
If a suspected crack area exists in a certain frame of image, a more obvious white pixel area exists in the frame of image; and if no crack defect exists, the frame image is completely black. In view of this, in this embodiment, the pixel values in each frame of binarized image are subjected to accumulation analysis in the row and column directions, and a row and column white pixel accumulation curve is calculated, where the formula is as follows:
wherein the content of the first and second substances,
binarized image for a frameThe first in the corresponding row pixel accumulation sequence
The sum of the row pixel values is then summed,
the second in the column gray scale accumulation sequence corresponding to the image after the frame binarization
The sum of the row pixel values is then summed,
and the total number of rows and the total number of columns corresponding to the binarized image of the frame.
Representing the binarized image of the frame
Go to the first
The pixel value (0 or 1) of the column pixel.
If the image after binarization of a certain frame continuously meets the requirement
Is greater than
Or/and continuously satisfy
Is greater than
And judging the image after the frame binarization as a suspected crack image, and extracting and analyzing the image. Specifically, the row pixel accumulation sequences corresponding to the binarized image of the frame are respectively accumulatedAnd fitting the curve with the column pixel accumulation sequence to obtain a row accumulation curve (H) and a column sum accumulation curve (L) corresponding to the frame binary image.
Since the crack region is less likely to exist in a single image and more likely to exist in several adjacent images (i.e., the full view of the crack is not captured in a single frame image). If several adjacent frames of binary images are extracted in the above process, the overlapped regions in the several adjacent frames of binary images are partially overlapped in rows and columns. Let the neighboring image set be
In which there is a total of
Sheet, with the smallest ordinal number
The largest ordinal number is
. By being adjacent
And
for example, the pixel values and their positions corresponding to all the peaks and valleys of their respective lines and curves (H) are obtained, forming a sequence of lines and pixel values from small to large (corresponding to the image from top to bottom) according to the line positions
The coincidence matching of the sequence on the two images is carried out (the pixel values are the same), and the coincident sequence part on the two image lines and the curve is required to be continuous (in the process, if the coincidence condition does not exist between the two continuous images, the two continuous images are separated, and a continuous image set is reestablished). Extracting the maximum wave peak value and its line in the overlapped partCoordinates of at
The peak and line positions on the image are respectively recorded as
、
In a
Respectively on the image
、
Wherein
. Therefore, two adjacent images can be spliced to form a new image with the longitudinal size of
The following can be obtained:
similarly, the next adjacent images are processed again according to the method
Sub-splices, i.e. pairs
The adjacent images are performed together
The image ruler with complete cracks can be obtained by secondary splicingCun is composed of
. Similarly, if the image is a single image containing a crack, then this time
Of size of
(order)
) Is still available
（
A positive integer).
And marking each image obtained after the splicing and each single image containing the crack as a target image, so that one or more target images can be obtained in the embodiment, and each target image comprises 1 complete crack. In this embodiment, the number of fingers is 2 or more.
(3) Calculating crack type judgment indexes corresponding to the target images, and judging the crack types of the target images according to the crack type judgment indexes; the crack type evaluation index comprises a ratio of a horizontal fluctuation range to a vertical fluctuation range, a difference degree between a row and an average peak and between a column and the average peak, a fluctuation degree of the row and a curve, and a fluctuation degree of the column and the curve; and estimating the crack width of the crack according to the number of pixel points occupied by the total area of the crack on each target image and the number of edge pixel points.
Cracks are one of the most prominent forms of failure in asphalt pavements. The cracks appearing on the asphalt highway pavement are divided into transverse cracks, longitudinal cracks and cracked reticular cracks according to different causes, and the different types of cracks reflect the perfection degree of different qualities of the highway construction. Wherein:
transverse cracking: the cracks are approximately vertical to the center line of the road and are distributed in a regular transverse mode, and one crack appears at intervals; the cracks bend, sometimes with a small number of branch cracks; the transverse construction joints of the pavement are not treated well, the joints are not tightly and poorly combined, and the pavement shrinks due to temperature reduction to cause transverse cracking.
Longitudinal cracking: the sections are cracked along the direction of the route, some sections are very long, and some sections are distributed. The reason for this is that the two connecting parts are not treated when the asphalt surface layer is paved in different frames, the compactness of the roadbed is not uniform, or the edge of the roadbed is soaked by water to generate uneven settlement.
Cracking and network cracking: the net-shaped cross cracking occurs on the local part of the road surface, some small pieces crack, and the road surface settlement phenomenon is shown in the cracking range. The reasons for this are poor quality of asphalt and asphalt mixture, or soft and muddy ash layer in the pavement structure, loose aggregate layer and poor water stability.
In order to realize the analysis of the crack types and the severity degrees on each target image, any target image is processed as follows:
the update columns and formulas are:
wherein the content of the first and second substances,
is the total number of lines of a certain target image.
The row and formula are not changed, the embodiment takes the three crack images as an example, the row and column and curve of the transverse cracks are obtained as shown in fig. 5, the row and column and curve of the longitudinal cracks are obtained as shown in fig. 6, and the row and column and curve of the cracking reticular cracks are obtained as shown in fig. 7. The main differences of the three fracture images are reflected in the degree and interval of fluctuation. Therefore, the present embodiment analyzes the fluctuation range and the fluctuation degree corresponding to the target image, and the process is as follows:
(1) analyzing a fluctuation range;
firstly, extracting the fluctuation interval
Line coordinate range of
，
Is composed of
The lower limit value of the line coordinates of (c),
is composed of
Upper limit value of the row coordinate of (1);
is noted as
，
Is composed of
The lower limit value of the column coordinates of (c),
is composed of
The upper limit value of the column coordinate of (1). The ratio of the statistical horizontal and vertical fluctuation ranges is as follows:
wherein the content of the first and second substances,
for the ratio of the horizontal and vertical fluctuation ranges corresponding to a certain target image, the transverse crack is integral
Very small, longitudinal cracks
Large, and a crack network is a local defect in between.
(2) And (5) analyzing the fluctuation degree.
Extracting all peak and trough values on the row and column curves, and recording the common value on the row and curve
Wave crest
、
Wave trough
Common to column and curve
Wave crest
、
Each wave trough
And all the wave crest and trough coordinates are counted as
、
、
、
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
is the size of the peaks on the line and curve,
is the magnitude of the valley on the line and curve,
is the number of the line and the wave crest on the curve,
is the number of troughs on the row and curve,
the size of the peaks on the columns and curves,
the size of the valleys on the columns and curves,
the number of peaks on the column and curve,
the number of valleys on the columns and curves.
Peak difference: the row and upper average peak values of the transverse cracks are much larger than the average peak values of the column and upper average peak values, the longitudinal cracks are just opposite, and the crazing and reticulating cracks are not obviously different. The degree of difference between the row and average peaks and the column and average peaks is:
wherein the content of the first and second substances,
the difference degree between the row and the average peak of a certain target image and the column and the average peak.
Fluctuation difference: the row and curve fluctuation degree of the transverse cracks is much larger than the row and curve fluctuation difference, and the longitudinal cracks are opposite, and the crack network crack fluctuation difference is similar. In this embodiment, the fluctuation ratio is calculated by using the number of peaks and the number of valleys, and the formula:
wherein the content of the first and second substances,
、
are respectively a curve
、
The fluctuation ratio of (c).
Calculate the average peak to valley amplitude variance over the rows, columns and curves: the abscissa of the peak and trough on the line and curve
、
Arranged from small to large, pixel values between adjacent peaks and troughs are calculated
、
Is obtained by summing the absolute values of the differences
Then, the average peak and valley amplitude variation on the line and curve is:
wherein the content of the first and second substances,
the absolute value of the difference between all adjacent peaks and troughs on the line and curve is summed.
In the same way, the average peak amplitude and trough amplitude change on the column and the curve is obtained as follows:
wherein, the first and the second end of the pipe are connected with each other,
the absolute value of the difference between all adjacent peaks and troughs on the column and curve is summed.
Calculating the fluctuation degrees of the rows, the columns and the curves according to the fluctuation ratio and the amplitude change of the average wave crests and wave troughs:
wherein
Is the degree of fluctuation of the line and curve,
the degree of fluctuation of the columns and curves.
The cracks can be divided into micro cracks, small cracks, middle cracks and large cracks according to the sizes, the corresponding size intervals are 0-5mm, 5-15mm, 15-25mm and more than 25mm, the corresponding repair work is micro cracks, small cracks and middle cracks, the cracks can be opened and cleaned, sealant can be poured under pressure for crack pouring, and the large cracks of the asphalt pavement are repaired by an asphalt thermal regeneration repairing process. The ratio of the real size to the pixel is recorded as
(unit: mm/pixel), and then the slit width of the slit is estimated.
Estimating the total area of the crack pixels: accumulating the rows and the longitudinal coordinate values on the curve, and estimating the number of pixels occupied by the total area of the cracks as follows:
estimating the width of a crack: canny edge detection is carried out on the target image to extract edges, so that two edges are reserved in a gap, and the total value of edge pixels is recorded as
Then the seam width estimation formula is:
wherein the content of the first and second substances,
the width of the crack on the target image is shown.
According to the analysis result, crack type judgment and repair suggestion are carried out, and the specific contents are as follows:
judging the crack type: if the crack characteristics satisfy
、
And is
Then judging that a transverse crack exists at the position corresponding to the target image; if the crack characteristics satisfy
、
And is
Then judging that a longitudinal crack exists at the position corresponding to the target image; if the two conditions are not satisfied, the existence of the cracked net-shaped cracks at the corresponding positions of the target image is judged.
And (4) repair suggestion: if it is
Then, slotting and cleaning the seams and pouring sealant under pressure to fill the seams; if it is
Then the asphalt hot recycling repair process is recommended for repair.
According to the process, the judgment of the crack type and the estimation of the crack width in each target image are realized, namely the detection of the road surface quality of the highway traffic engineering is realized, and the targeted maintenance of the target road surface can be realized based on the obtained crack type and the crack width.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications or substitutions do not cause the essential features of the corresponding technical solutions to depart from the scope of the technical solutions of the embodiments of the present application, and are intended to be included within the scope of the present application.
Claims (8)
1. A road traffic engineering pavement quality detection method is characterized by comprising the following steps:
acquiring each frame of shot image corresponding to a target road surface, and performing graying and reverse processing on each frame of shot image corresponding to the target road surface to obtain each frame of gray reverse image corresponding to the target road surface;
enhancing and binarizing each frame of gray-scale reverse image corresponding to the target road surface, judging whether each frame of image is a suspected crack image according to row and column data corresponding to each processed frame of image, and if a suspected crack image exists in each processed frame of image corresponding to the target road surface and no adjacent frame of suspected crack image exists, marking each suspected crack image as each target image; if the adjacent frame suspected crack images exist, performing image splicing processing on the adjacent frame suspected crack images to obtain 1 or more target images, wherein each target image comprises a complete crack;
calculating crack type judgment indexes corresponding to the target images, and judging the crack types of the target images according to the crack type judgment indexes; the crack type evaluation index comprises a ratio of a horizontal fluctuation range to a vertical fluctuation range, a difference degree between a row and an average peak and between a column and the average peak, a fluctuation degree of the row and a curve, and a fluctuation degree of the column and the curve; and estimating the crack width of the crack according to the number of pixel points occupied by the total area of the crack on each target image and the number of edge pixel points.
2. The method for detecting the road surface quality of the road traffic engineering according to claim 1, wherein the step of judging whether each frame of image is a suspected crack image according to the row and column data corresponding to each processed frame of image comprises the following steps:
and performing accumulation analysis on pixel values in each frame of binarized image according to the directions of rows and columns, and calculating a row and column white pixel accumulation curve, wherein the formula is as follows:
wherein the content of the first and second substances,
the first in the row pixel accumulation sequence corresponding to the image after binarization of a certain frame
The row and the column are then shifted,
the first in the column gray scale accumulation sequence corresponding to the image after the frame binarization
The sum of the columns is given by,
the total number of rows and the total number of columns corresponding to the image after the frame binarization;
representing the binarized image of the frame
Go to the first
Pixel values of column pixels;
if the image after binarization of a certain frame continuously meets the requirement
Is greater than
Or/and continuously satisfy
Is greater than
Then the image after the frame binarization is judged as a suspected crack image.
3. The method for detecting the road surface quality of the road traffic engineering according to claim 1, wherein the ratio of the horizontal and vertical fluctuation ranges corresponding to the target images is calculated by using the following formula:
wherein the content of the first and second substances,
is the ratio of the horizontal and vertical fluctuation range corresponding to a certain target image,
in the target image
In the row coordinate ofThe value of the limit is set as,
in the target image
Upper limit value of the row coordinate of (1);
in the target image
The lower limit value of the column coordinates of (c),
in the target image
The upper limit value of the column coordinate of (c),
is as follows
The sum of the row pixel values is then summed,
is a first
The sum of the row pixel values.
4. The method for detecting the road surface quality of the road traffic engineering according to claim 1, wherein the difference degree between the row and the column of the average peak corresponding to each target image and the average peak is calculated by using the following formula:
wherein the content of the first and second substances,
the degree of difference between the row and average peak and the column and average peak of a certain target image,
the size of the peak on the line and curve of the target image,
for the number of peaks on the line and curve of the target image,
for the column of the target image and the size of the peak on the curve,
the columns of the target image and the number of peaks on the curve.
5. The road surface quality detection method for road traffic engineering according to claim 1, characterized in that the line and curve fluctuation degrees and the column and curve fluctuation degrees corresponding to each target image are calculated by using the following formulas:
wherein, the first and the second end of the pipe are connected with each other,
the degree of fluctuation of the lines and curves of a certain target image,
for the degree of fluctuation of the columns and curves of the target image,
for the fluctuation ratio of the lines and curves of the target image,
for the fluctuation ratio of the columns and curves of the target image,
for the average peak to valley amplitude variation on the line and curve of the target image,
for the average peak-to-valley amplitude variation on the columns and curves of the target image,
summing the absolute values of the differences between all adjacent peaks and valleys on the line and curve of the target image,
summing the absolute values of the differences between all adjacent peaks and troughs on the column and curve of the target image,
for the number of peaks on the line and curve of the target image,
for the columns of the target image and the number of peaks on the curve,
in the target image
The lower limit value of the line coordinates of (a),
in the target image
The upper limit value of the line coordinate of (a);
in the target image
The lower limit value of the column coordinate of (c),
in the target image
The upper limit value of the column coordinates of (c),
is a first
The sum of the row pixel values is then,
is a first
The sum of the row pixel values.
6. The method for detecting the road surface quality of the road traffic engineering according to claim 1, wherein the crack types comprise transverse cracks, longitudinal cracks and crack network cracks.
7. The method for detecting the road surface quality of the road traffic engineering according to claim 6, wherein the judging of the crack type of each target image according to the crack type judging index comprises the following steps:
ratio of horizontal to vertical fluctuation range
The degree of difference between the row and column average peaks and the average peak
And degree of line and curve fluctuation
Extent of column and curve fluctuation
Judging that a transverse crack exists at the position corresponding to the target image;
ratio of horizontal to vertical fluctuation range
The degree of difference between the row and column average peaks and the average peak
And degree of line and curve fluctuation
Extent of column and curve fluctuation
Judging that a longitudinal crack exists at the position corresponding to the target image;
if the two conditions are not satisfied, the existence of the cracked net-shaped cracks at the corresponding positions of the target images is judged.
8. The method for detecting the road surface quality of the road traffic engineering according to claim 1, wherein the estimating of the crack width according to the number of pixel points occupied by the total crack area and the number of edge pixel points on each target image comprises:
accumulating the rows and the longitudinal coordinate values on the curve, and estimating the number of pixels occupied by the total area of the cracks as follows:
wherein, the first and the second end of the pipe are connected with each other,
the total area of the crack of a certain target image occupies the number of pixels,
for the total number of columns of the target image,
is a first
The sum of the row pixel values;
canny edge detection is carried out on the target image to extract edges, and the total value of edge pixels is recorded as
Then the slot width estimation formula is:
wherein the content of the first and second substances,
the width of the crack on the target image is taken as the width of the crack,
in units of proportion to the real size of the pixel.
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