Road construction progress tracking and monitoring method

Abstract

The invention relates to the technical field of image processing, in particular to a road construction progress tracking and monitoring method, which comprises the following steps: acquiring a road construction image of a target road, and determining a suspected road connected domain from the road construction image; determining a first possibility that the suspected road connected domain is a road area; selecting the suspected road connected domain of which the first possibility is greater than or equal to a first threshold value as a road area; determining a second possibility that the road area is a paved road area according to the first possibility of the road area and the gray value of the pixel point in the road area, and selecting the road area of which the second possibility is greater than or equal to a second threshold value as the paved road area; and determining the construction progress ratio of the target road in the road construction image according to the first sum value of the first areas of all the paved road areas in the road construction image and the second sum value of the second areas of all the road areas in the target road. According to the invention, the accuracy of the tracking and monitoring result of the road construction progress is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly relates to a method for tracking and monitoring the progress of road construction. Background Art

[0002] Tracking and monitoring the progress of road construction is a management activity to ensure the progress of the project plan, and timely discover and solve potential problems. Effective progress tracking and monitoring can understand the actual progress of the road. Through means such as real-time data collection, progress comparison, and quality control, the tracking and monitoring of road construction progress can optimize construction management, improve efficiency and safety, and ensure that the project can be completed on time and with high quality.

[0003] In some scenarios, during the process of tracking and monitoring the progress of road construction, drones equipped with cameras are often used to take pictures of the road, and then the K-means clustering image segmentation algorithm is used to segment the taken pictures, so as to identify the paved road area from the taken pictures. Since the K-means clustering image segmentation algorithm only clusters pixel points of the same color into one category, and there are non-road areas in the image that are similar in color to the paved road area, using color to screen the road area and non-road area has low accuracy, and it is easy to misjudge the paved road area, thereby resulting in low accuracy of the tracking and monitoring results of the road construction progress. Summary of the Invention

[0004] In order to solve the technical problem of low accuracy of the tracking and monitoring results of the road construction progress, the purpose of the present invention is to provide a method for tracking and monitoring the progress of road construction, and the specific technical solution adopted is as follows: First aspect, an embodiment of the present invention provides a method for tracking and monitoring the progress of road construction, including: obtaining a road construction image of a target road, and determining a suspected road connected region from the road construction image; determining the minimum bounding rectangle of the suspected road connected region and multiple straight line segments of the suspected road connected region; according to a first parameter of the minimum bounding rectangle and a second parameter of each of the straight line segments, determining a pavement shape regularity analysis parameter for the suspected road connected region to be a road region; the first parameter is a first length of the longest side of the minimum bounding rectangle and a second length of the shortest side; the second parameter is the length of the straight line segment; according to the gray value of the pixel points in the suspected road connected region and a first number of pixel points, determining a subgrade color contrast parameter for the suspected road connected region to be a road region; based on the pavement shape regularity analysis parameter and the subgrade color contrast parameter, determining a first possibility that the suspected road connected region is a road region; selecting a suspected road connected region with a first possibility greater than or equal to a first threshold as a road region; according to the first possibility of the road region and the gray value of the pixel points in the road region, determining a second possibility that the road region is a paved road region, and selecting a road region with a second possibility greater than or equal to a second threshold as a paved road region; according to a first sum value of the first areas of all the paved road regions in the road construction image and a second sum value of the second areas of all the road regions in the target road, determining a construction progress ratio of the target road in the road construction image.

[0005] Optionally, determining a suspected road connected region from the road construction image includes: performing an operation on the road construction image using a K-means clustering image segmentation algorithm to obtain multiple clustering clusters; performing an operation on two adjacent road construction images in the clustering clusters using an image alignment operation, and recording a region where the overlapping connected regions in the two adjacent road construction images change as a suspected change region; determining the connected region corresponding to the suspected change region as a suspected road connected region.

[0006] Optionally, determining the minimum bounding rectangle of the suspected road connected region and multiple straight line segments of the suspected road connected region includes: determining the minimum convex polygon containing all the pixel points in the suspected road connected region; determining a rectangle parallel to the sides by rotating each side of the minimum convex polygon; calculating a third area of each rotated rectangle, and selecting the rectangle with the minimum third area as the minimum bounding rectangle; performing an operation on the suspected road connected region using an edge detection algorithm to obtain multiple edge points; converting each edge point to polar coordinates, and accumulating the number of occurrences at the position of each polar coordinate in the Hough space; identifying the position with the highest accumulated number of occurrences of each polar coordinate in the Hough space, and determining the straight line segments in the suspected road connected region.

[0007] Optionally, the pavement shape regularity analysis parameters for determining that the suspected road connected region is a road region based on the first parameter of the minimum bounding rectangle and the second parameter of each straight line segment include: calculating a first ratio between the first length of the longest side of the minimum bounding rectangle and the second length of the shortest side of the minimum bounding rectangle; adding up the lengths of all the straight line segments in the suspected road connected region to obtain a first sum value; calculating a second ratio between the first sum value and the fourth area of the suspected road connected region, and a first product between the first ratio and the second ratio; and performing a normalization process on the first product to obtain the pavement shape regularity analysis parameters of the road region.

[0008] Optionally, the subgrade color contrast parameters for determining that the suspected road connected region is a road region based on the gray values of the pixel points and the first quantity of the pixel points in the suspected road connected region include: adding up the gray values of all the pixel points in the suspected road connected region to obtain a second sum value, and a third ratio between the second sum value and the first quantity; calculating a first difference between the gray value of each pixel point in the suspected road connected region and the third ratio, and adding up the first differences to obtain a third sum value; calculating a fourth ratio between the third sum value and the first quantity; and performing an inverse normalization process on the fourth ratio to obtain the subgrade color contrast parameters of the suspected road connected region as a road region.

[0009] Optionally, the first possibility of determining that the suspected road connected region is a road region based on the pavement shape regularity analysis parameters and the subgrade color contrast parameters includes: calculating a second product between the pavement shape regularity analysis parameters and the subgrade color contrast parameters; and performing a normalization process on the second product to obtain the first possibility of the suspected road connected region being a road region.

[0010] Optionally, the second possibility of determining that the road region is a paved road region based on the first possibility of the road region and the gray values of the pixel points in the road region includes: calculating a first average value of the gray values of all the pixel points in the road region, and a fifth ratio between the first possibility and the first average value; and performing a normalization process on the fifth ratio to obtain the second possibility of the road region being a paved road region.

[0011] Optionally, the construction progress ratio of the target road in the road construction image is determined based on the first sum value of the first areas of all the paved road regions in the road construction image and the second sum value of the second areas of all the road regions in the target road, including: determining a sixth ratio between the first sum value and the second sum value as the construction progress ratio of the target road in the road construction image.

[0012] Optionally, obtaining the road construction image of the target road includes: using a drone to take pictures of the target road at the same position every day to obtain a construction image; and performing an operation on the construction image using a mean filtering algorithm to obtain the road construction image.

[0013] The present invention has the following beneficial effects: First, obtain the road construction image of the target road, and determine the suspected road connected region from the road construction image; then, according to the first parameter of the minimum circumscribed rectangle of the suspected road connected region, the second parameter of multiple straight line segments of the suspected road connected region, the gray value of the pixel points in the suspected road connected region, and the first quantity of the pixel points, determine the first possibility that the suspected road connected region is a road region; and select the suspected road connected regions with the first possibility greater than or equal to the first threshold as the road regions; Secondly, according to the first possibility of the road region and the gray value of the pixel points in the road region, determine the second possibility that the road region is a paved road region, and select the road regions with the second possibility greater than or equal to the second threshold as the paved road regions; Finally, according to the first sum value of the first areas of all the paved road regions in the road construction image and the second sum value of the second areas of all the road regions in the target road, determine the construction progress ratio of the target road in the road construction image.

[0014] In this way, the embodiments of the present invention can use various features of the suspected road connected region to further screen the road regions and the paved road regions. For example, use the first parameter of the minimum circumscribed rectangle in the suspected road connected region, the second parameter of multiple straight line segments of the suspected road connected region, the gray value of the pixel points in the suspected road connected region, and the first quantity of the pixel points, etc. to determine the paved road region, and finally determine the construction progress ratio of the target road in the road construction image according to the first sum value of the first areas of all the paved road regions and the second sum value of the second areas of all the road regions in the target road. It improves the accuracy of the distinction between the road regions and the non-road regions, avoids misjudgment of the paved road regions, and further improves the accuracy of the tracking and monitoring results of the road construction progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of a method for tracking and monitoring road construction progress provided by an embodiment of the present invention.

[0017] Figure 2 It is a schematic structural diagram of a system for tracking and monitoring road construction progress provided by an embodiment of the present invention.

[0018] Figure 3 It is a schematic structural diagram of a system for tracking and monitoring road construction progress provided by another embodiment of the present invention. Detailed implementation manners

[0019] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details a method for tracking and monitoring the progress of road construction proposed according to the present invention, including its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0021] The following specifically describes the specific solution of a method for tracking and monitoring the progress of road construction provided by the present invention in combination with the accompanying drawings.

[0022] Embodiment 1: Please refer to Figure 1 , which shows a flowchart of a method for tracking and monitoring the progress of road construction provided by an embodiment of the present invention, including: S101. Obtain a road construction image of a target road, and determine a suspected road connected region from the road construction image.

[0023] Specifically, the target road refers to the road for which the progress of road construction needs to be tracked. Among them, as an optional embodiment of the present invention, obtaining the road construction image of the target road includes: First, use a drone to take pictures of the target road at the same position every day to obtain a construction image; then perform an operation on the construction image using a mean filter algorithm to obtain a road construction image.

[0024] Among them, in the embodiment of the present invention, a drone is used to take pictures of the entire road where the construction has ended at the same position every day to obtain the corresponding road construction image every day. Further, in order to improve the image processing efficiency, the embodiment of the present invention also performs an operation on the road construction image using mean filtering to remove the noise points in the road construction image. Among them, mean filtering is a well-known technology, and the specific method is not introduced in the embodiment of the present invention here.

[0025] Further, in order to accurately obtain the paved road area, first, the embodiments of the present invention need to perform operations on the road construction image using the K-means clustering image segmentation algorithm to obtain several connected regions. As the road construction progresses every day, the area of the road will gradually change. Taking advantage of this change feature, the adjacent two road construction images are aligned to obtain several suspected road regions with changes. Since the non-road areas will also have slight changes due to the error of algorithm segmentation, the next step of screening needs to be carried out through the characteristics of the road connected regions where the changing non-road areas are located. The shape of the road region tends to be long and strip-shaped, and the difference in gray values of different pixel points within the road region is small, and the edge is relatively regular, that is, the edge line of the road region is mostly a straight line. Therefore, using the above characteristics to screen the road region, after obtaining the road region, since the unpaved road is a compacted soil road surface and its color is close to white, the gray value of the pixel points belonging to the unpaved road region in the road construction image is relatively high, while the paved road is paved with asphalt and the color of the asphalt is black, so the gray value of the pixel points belonging to the paved road region in the road construction image is relatively low. Using this feature again, the paved road region is screened out.

[0026] Further, as the road construction progresses every day, the area of the paved road will gradually change, which is manifested in the road construction image as the same area of adjacent road construction images will change. Therefore, using the above characteristics, several suspected changing regions of each road construction image are obtained.

[0027] Further, as an optional embodiment of the present invention, determining the suspected road connected region from the road construction image includes: performing operations on the road construction image using the K-means clustering image segmentation algorithm to obtain multiple clustering clusters; performing operations on two adjacent road construction images in the clustering clusters using image alignment operations, and recording the region where the overlapping connected regions in the two adjacent road construction images have changed as the suspected changing region; determining the connected region corresponding to the suspected changing region as the suspected road connected region.

[0028] Specifically, the embodiments of the present invention perform operations on each road construction image using the K-means clustering image segmentation algorithm according to the preset number of clusters to obtain several clustering clusters, and each clustering cluster corresponds to a connected region. Among them, the preset number of clusters in the embodiments of the present invention can be 4, and this is taken as an example for description. After clustering to obtain multiple clustering clusters, the embodiments of the present invention take the th road construction image as an example, and use image alignment operations to perform operations on the th road construction image and the th road construction image, and record the region where the overlapping connected regions in the th road construction image have changed as the suspected changing region.

[0029] Denote all the connected components where the suspected changed regions are located as suspected road connected components. Perform operations on each road construction image according to the above process to obtain several suspected changed regions and corresponding suspected road connected components for each road construction image.

[0030] S102. Determine the first possibility that the suspected road connected component is a road region based on the first parameter of the minimum bounding rectangle of the suspected road connected component, the second parameter of multiple straight line segments of the suspected road connected component, the gray value of the pixel points in the suspected road connected component, and the first quantity of the pixel points.

[0031] Specifically, through the above process, several suspected road connected components of each road construction image are obtained in the embodiments of the present invention. Since non-road regions may also have slight changes due to algorithm segmentation errors, there are non-road regions in the suspected paved road connected components. Therefore, further screening needs to be performed according to the characteristics of the suspected road connected components. The shapes of the suspected road connected components are relatively regular and tend to be long and strip-shaped, and the colors within the regions are relatively single, and the differences in the gray values of different pixel points are very small. The embodiments of the present invention utilize these characteristics to calculate the first possibility that the suspected road connected component is a road region, and further obtain the road region.

[0032] Furthermore, for any suspected road connected component of a road construction image, if its shape is closer to a long and strip shape, it means that the suspected road connected component has strong extensibility and is relatively narrow in space, and this shape usually conforms to the characteristics of the road region. Therefore, in this case, the greater the possibility that the suspected road connected component is determined to be a road region. And roads usually present relatively straight and regular edge contours. Especially when the roads are artificially planned and designed, they often exhibit high straight line characteristics. Therefore, regions with regular edges and a shape tending to be straight highly conform to the characteristics of the road region. Therefore, the embodiments of the present invention calculate the road surface shape regularity analysis parameter for each suspected road connected component being a road region according to the above characteristics.

[0033] Furthermore, the true road area is usually paved with uniform materials, and its color presents relatively single and stable characteristics. Due to the lack of significant color changes on the road surface, the gray value differences between different pixel points in the road construction image located in the road area are relatively small and usually remain within a relatively uniform range. This similarity in gray values is an important recognition feature of the road area, forming an obvious contrast with areas with large gray value fluctuations in the surrounding environment, such as grasslands, buildings, or water areas. By analyzing the gray value differences in the road construction image, the road area can be effectively identified, further improving the accuracy of road detection. According to the above characteristics, the embodiment of the present invention calculates the subgrade color contrast parameter of each suspected road connected domain being a road area. Finally, based on this road surface morphology regularity analysis parameter and the subgrade color contrast parameter, the first possibility of the suspected road connected domain being a road area is calculated.

[0034] Furthermore, as an optional embodiment of the present invention, determining the first possibility of the suspected road connected domain being a road area according to the first parameter of the minimum circumscribed rectangle of the suspected road connected domain, the second parameter of multiple straight line segments of the suspected road connected domain, the gray value of the pixel points in the suspected road connected domain, and the first number of pixel points includes: determining the minimum circumscribed rectangle of the suspected road connected domain and multiple straight line segments of the suspected road connected domain; determining the road surface morphology regularity analysis parameter of the suspected road connected domain being a road area according to the first parameter of the minimum circumscribed rectangle and the second parameter of each straight line segment; determining the subgrade color contrast parameter of the suspected road connected domain being a road area according to the gray value of the pixel points in the suspected road connected domain and the first number of pixel points; determining the first possibility of the suspected road connected domain being a road area based on the road surface morphology regularity analysis parameter and the subgrade color contrast parameter. The first parameter is the first length of the longest side of the minimum circumscribed rectangle and the second length of the shortest side; the second parameter is the length of the straight line segment.

[0035] Specifically, in the embodiments of the present invention, the minimum bounding rectangle algorithm is used to operate on each suspected road connected region in each road construction image, and the minimum bounding rectangles of all suspected road connected regions in each road construction image are obtained. Among them, as an optional embodiment of the present invention, determining the minimum bounding rectangle of the suspected road connected region and multiple straight line segments of the suspected road connected region includes: determining the minimum convex polygon containing all pixel points in the suspected road connected region; determining the rectangle parallel to the side by rotating each side of the minimum convex polygon; calculating the third area of each rotated rectangle, and selecting the rectangle with the smallest third area as the minimum bounding rectangle; using the edge detection algorithm to operate on the suspected road connected region to obtain multiple edge points; converting each edge point into polar coordinates, and accumulating the number of occurrences at the position of each polar coordinate in the Hough space; identifying the position with the highest accumulated number of occurrences of each polar coordinate position in the Hough space to determine the straight line segment in the suspected road connected region.

[0036] Specifically, in the embodiments of the present invention, taking the th suspected road connected region in the th road construction image as an example, first find the minimum convex polygon containing all pixel points in the th suspected road connected region in the th road construction image. Secondly, by rotating each side of the minimum convex polygon, find a rectangle parallel to the side that can completely enclose the point set. Then, for each rotated rectangle, calculate its area, and select the rectangle with the smallest area as the final minimum bounding rectangle. Then output the coordinates, width, and height of the minimum bounding rectangle of the th suspected road connected region in the th road construction image.

[0037] More specifically, in the embodiments of the present invention, the Hough line detection algorithm is used to operate on the suspected road connected regions in each road construction image, and several straight line segments of the suspected road connected regions in each road construction image are obtained. In the embodiments of the present invention, still taking the th suspected road connected region in the th road construction image as an example, first use the edge detection algorithm to operate on the th suspected road connected region in the th road construction image to obtain several edge points. Secondly, each edge point is converted into polar coordinates. Then, the number of occurrences is accumulated at the position of each polar coordinate in the Hough space. Then, by identifying the highest value of the accumulated number of occurrences of each polar coordinate position in the Hough space, find the straight line in the road construction image. Extract the straight line segment according to the highest value of the accumulated number of occurrences of each polar coordinate position in the Hough space, and draw the straight line segment back into the th suspected road connected region in the th road construction image.

[0038] Further, as an alternative embodiment of the present invention, the pavement shape regularity analysis parameter for determining the suspected road connected region as a road region according to the first parameter of the minimum circumscribed rectangle and the second parameter of each straight line segment includes: calculating a first ratio between the first length of the longest side of the minimum circumscribed rectangle and the second length of the shortest side of the minimum circumscribed rectangle; adding up the lengths of all the straight line segments in the suspected road connected region to obtain a first sum value; calculating a second ratio between the first sum value and the fourth area of the suspected road connected region, and a first product between the first ratio and the second ratio; and performing a normalization process on the first product to obtain the pavement shape regularity analysis parameter of the road region.

[0039] Specifically, in the embodiment of the present invention, taking the th suspected road connected region in the th road construction image as an example, the specific calculation formula of the pavement shape regularity analysis parameter corresponding to the th suspected road connected region in the th road construction image is as follows: In the above formula, represents the pavement shape regularity analysis parameter of the th suspected road connected region in the th road construction image. represents the length of the longest side of the minimum circumscribed rectangle of the th suspected road connected region in the th road construction image. represents the length of the shortest side of the minimum circumscribed rectangle of the th suspected road connected region in the th road construction image. represents the number of straight line segments of the th suspected road connected region in the th road construction image. The th straight line segment of the th suspected road connected region in the th road construction image. represents the fourth area of the th suspected road connected region in the th road construction image. represents a linear normalization function, which is used to perform a normalization process on

[0040] It should be noted that the th suspected road connected region in the ​The more the shape of a suspected road connected region tends to be strip-shaped, the greater the possibility that the th suspected road connected region in the th road construction image is a road region. The more the number of straight line segments in the th suspected road connected region in the th road construction image, and the longer the number of straight line segments, the more regular the th suspected road connected region in the th road construction image, and the greater the possibility that it is a road region. Thus, through the above embodiments of the present invention, each suspected road connected region of each road construction image is calculated according to the above process, and the road surface shape regularity analysis parameter indicating that each suspected road connected region of each road construction image is a road region is obtained.

[0041] Furthermore, as an optional embodiment of the present invention, according to the gray value of the pixel points and the first number of pixel points in the suspected road connected region, the subgrade color contrast parameter for determining that the suspected road connected region is a road region includes: superimposing the gray values of all pixel points in the suspected road connected region to obtain a second superimposed value, and a third ratio between the second superimposed value and the first number; calculating a first difference between the gray value of each pixel point in the suspected road connected region and the third ratio, and superimposing the first differences to obtain a third superimposed value; calculating a fourth ratio between the third superimposed value and the first number; performing inverse proportional normalization processing on the fourth ratio to obtain the subgrade color contrast parameter for determining that the suspected road connected region is a road region.

[0042] Specifically, in the embodiment of the present invention, still taking the th suspected road connected region in the th road construction image as an example, the specific calculation formula of the subgrade color contrast parameter indicating that the th suspected road connected region in the th road construction image is a road region is as follows: In the above formula, represents the subgrade color contrast parameter indicating that the th suspected road connected region in the th road construction image is a road region. represents the first number of pixel points in the th suspected road connected region in the th road construction image. represents the gray value of the th pixel point in the th suspected road connected region in the th road construction image. represents the exponential function with the natural number as the base. For performing inverse proportional normalization processing on . Denotes the absolute value function, which is used to take the absolute value.

[0043] It should be noted that if the difference in the gray values of different pixel points of the th suspected road connected region in the th road construction image is smaller, the greater the possibility that the suspected road connected region is a road area. Furthermore, in the embodiments of the present invention, each suspected road connected region in each road construction image is calculated according to the above process to obtain the roadbed color contrast parameter of each suspected road connected region in each road construction image being a road area.

[0044] Furthermore, as an optional embodiment of the present invention, determining the first possibility that the suspected road connected region is a road area based on the road surface form regular analysis parameter and the roadbed color contrast parameter includes: calculating the second product between the road surface form regular analysis parameter and the roadbed color contrast parameter; performing normalization processing on the second product to obtain the first possibility that the suspected road connected region is a road area.

[0045] Specifically, in the embodiments of the present invention, still taking the th suspected unpaved road connected region in the th road construction image as an example, the specific calculation formula for the final possibility that the th suspected road connected region in the th road construction image is a road area is as follows: In the above formula, represents the first possibility that the th suspected road connected region in the th road construction image is a road area. represents the road surface form regular analysis parameter of the th suspected road connected region in the th road construction image being a road area. represents the roadbed color contrast parameter of the th suspected road connected region in the th road construction image being a road area. Denotes the linear normalization function, which is used to perform normalization processing on

[0046] In this way, in the embodiments of the present invention, each suspected road connected region in each road construction image is calculated according to the above process to obtain the first possibility that each suspected road connected region in each road construction image is a road area.

[0047] S103: Select the suspected road connection domain with a first possibility greater than or equal to a first threshold as the road area.

[0048] Specifically, after the embodiment of the present invention calculates the first possibility through the above process, it then uses the threshold analysis method to obtain the road area. Road construction image Taking a suspected road connected domain as an example, the process of judging whether it belongs to the road area is as follows: First, set the first threshold , then the Road construction image The first possibility that the suspected road connected domain is the road area Extraction and reuse With the first threshold Perform threshold comparison, if , then Road construction image The suspected road connected domains are road areas. At this point, the road areas in the road construction image are obtained. The embodiment of the present invention obtains several road areas in each road construction image through the above process. Among them, the first threshold value can be determined according to actual conditions, and the embodiment of the present invention does not limit it here.

[0049] S104, determining a second possibility that the road area is a paved road area according to the first possibility of the road area and the grayscale values ​​of the pixels in the road area, and selecting a road area whose second possibility is greater than or equal to a second threshold as a paved road area.

[0050] Specifically, since unpaved roads are compacted dirt roads, the color of which is close to white, the grayscale value of the pixels belonging to the unpaved area in the road construction image is higher, while the paved road area is paved with asphalt, the color of asphalt is black, so the grayscale value of the pixels belonging to the paved road area in the road construction image is lower. The embodiment of the present invention uses the above features to calculate the possibility that the road area is a paved road, and then screens out the paved road area.

[0051] Further, as an optional embodiment of the present invention, determining the second possibility that the road area is a paved road area based on the first possibility of the road area and the grayscale values ​​of the pixels in the road area includes: calculating a first average value of the grayscale values ​​of all pixels in the road area, and a fifth ratio between the first possibility and the first average value; normalizing the fifth ratio to obtain the second possibility that the road area is a paved road area.

[0052] Specifically, the embodiment of the present invention is still based on the first Road construction image Taking a road area as an example, the specific calculation formula corresponding to the probability that the th road area in the th road construction image is a paved road area is as follows: In the above formula, represents the second probability that the th road area in the th road construction image is a paved road area. represents the first probability that the th road area in the th road construction image is a road area. represents the average gray value of all pixel points in the th road area in the th road construction image. represents a linear normalization function, which is used to normalize

[0053] It should be noted that is inversely proportional to because the area of the paved road will be paved with asphalt and the color is close to black, and the average gray value of all pixel points in its area will be very small. Therefore, the larger the average gray value of all pixel points in the th road area in the th road construction image, the smaller the probability that the th road area in the th road construction image is a paved road area. In the embodiment of the present invention, all road areas in each road construction image are calculated according to the above process to obtain the second probability that all road areas in each road construction image are paved roads.

[0054] Furthermore, in the embodiment of the present invention, the threshold analysis method is then used to obtain the paved road area. Taking the th road area in the th road construction image as an example, the process of judging whether it belongs to the paved road area is as follows: First, set the second threshold ; then extract the second probability that the th road area in the th road construction image is a connected domain of the paved road. Then use to compare with the second threshold . If , then the th road area in the The road area is a paved road area, and the paved road area is obtained. Through the above process, several paved road areas of each road construction image are obtained.

[0055] S105 , determining a construction progress ratio of the target road in the road construction image according to a first sum of first areas of all paved road regions in the road construction image and a second sum of second areas of all road regions in the target road.

[0056] Specifically, through the above process of the embodiment of the present invention, the paved road area of ​​each road construction image is obtained, and the construction progress ratio of each road construction image is calculated using the ratio of the area of ​​the paved road area to the area of ​​all road areas.

[0057] Further, as an optional embodiment of the present invention, determining the construction progress ratio of the target road in the road construction image based on the first sum of the first areas of all paved road areas in the road construction image and the second sum of the second areas of all road areas in the target road includes: determining a sixth ratio between the first sum and the second sum as the construction progress ratio of the target road in the road construction image.

[0058] Specifically, the embodiment of the present invention is based on the For example, the road construction image The specific calculation formula corresponding to the construction progress ratio of a road construction image is: In the above formula, Indicates Construction progress ratio of road construction images. Indicates The first sum of the first areas of all paved road areas in the road construction image. Indicates The second sum value of the second areas of all road regions in the road construction image.

[0059] It is worth noting that The larger the sum of the areas of all paved road areas in the road construction image, the The larger the construction progress ratio of the road construction image, the greater the construction progress ratio of the road construction image. Calculate each road construction image according to the above process of the present invention to obtain the construction progress ratio of each road construction image.

[0060] The embodiments of the present invention can further screen the road area and the paved road area by using various features of the suspected road connected domain. For example, the first parameter of the minimum circumscribed rectangle in the suspected road connected domain, the second parameter of multiple straight line segments in the suspected road connected domain, the gray value of the pixel points in the suspected road connected domain, and the first quantity of the pixel points are used to determine the paved road area. Finally, the construction progress ratio of the target road in the road construction image is determined according to the first sum value of the first areas of all the paved road areas and the second sum value of the second areas of all the road areas in the target road. The accuracy of distinguishing between the road area and the non-road area is improved, misjudgment of the paved road area is avoided, and the accuracy of the tracking and monitoring result of the road construction progress is further improved.

[0061] Embodiment 2: Corresponding to the road construction progress tracking and monitoring method provided in the above embodiment, based on the same technical concept, the embodiment of the present invention further provides a road construction progress tracking and monitoring system. This road construction progress tracking and monitoring system is used to execute the above road construction progress tracking and monitoring method. Figure 2 As shown in the structural schematic diagram of a road construction progress tracking and monitoring system provided for another embodiment of the present invention. Figure 2 As shown in the figure. The road construction progress tracking and monitoring system 200 includes: an acquisition module 201, configured to acquire a road construction image of a target road and determine a suspected road connected domain from the road construction image; a determination module 202, configured to determine the first possibility that the suspected road connected domain is a road area according to the first parameter of the minimum circumscribed rectangle of the suspected road connected domain, the second parameter of multiple straight line segments of the suspected road connected domain, the gray value of the pixel points in the suspected road connected domain, and the first quantity of the pixel points; a selection module 203, configured to select the suspected road connected domain with the first possibility greater than or equal to the first threshold as the road area; the determination module 202 is further configured to determine the second possibility that the road area is a paved road area according to the first possibility of the road area and the gray value of the pixel points in the road area, and select the road area with the second possibility greater than or equal to the second threshold as the paved road area; the determination module 202 is further configured to determine the construction progress ratio of the target road in the road construction image according to the first sum value of the first areas of all the paved road areas in the road construction image and the second sum value of the second areas of all the road areas in the target road.

[0062] The embodiments of the present invention can further screen the road area and the paved road area by using various features of the suspected road connected domain. For example, the first parameter of the minimum circumscribed rectangle in the suspected road connected domain, the second parameter of multiple straight line segments in the suspected road connected domain, the gray value of the pixel points in the suspected road connected domain, and the first number of pixel points are used to determine the paved road area. Finally, the construction progress ratio of the target road in the road construction image is determined according to the first sum value of the first areas of all the paved road areas and the second sum value of the second areas of all the road areas in the target road. The accuracy of distinguishing between the road area and the non-road area is improved, the misjudgment of the paved road area is avoided, and the accuracy of the tracking and monitoring result of the road construction progress is further improved.

[0063] Embodiment III: Corresponding to the road construction progress tracking and monitoring method provided in the above embodiment, based on the same technical concept, the embodiments of the present invention also provide a road construction progress tracking and monitoring system. This road construction progress tracking and monitoring system is used to execute the above road construction progress tracking and monitoring method. Figure 3 FIG. is a schematic structural diagram of a road construction progress tracking and monitoring system provided by another embodiment of the present invention, as Figure 3 shown. The road construction progress tracking and monitoring system may vary greatly due to configuration or performance differences. It may include one or more processors 301 and a memory 302. The memory 302 is used to store computer programs that can run on the processor 301. The processor 301 is used to execute the programs stored in the memory 302 to implement each step in the above Figure 1 method embodiments. Among them, the memory 302 can be short-term storage or persistent storage. The application programs stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the road construction progress tracking and monitoring system.

[0064] Furthermore, the processor 301 can be set to communicate with the memory 302 and execute a series of computer-executable instructions in the memory 302 on the road construction progress tracking and monitoring system. The road construction progress tracking and monitoring system may also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input / output interfaces 305, and one or more keyboards 306.

[0065] Specifically, in this embodiment, the road construction progress tracking and monitoring system includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the bus. The memory is used to store computer programs. The processor is used to execute the programs stored in the memory to implement the above Figure 1Each step in the method embodiments has the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be elaborated herein again.

[0066] It should be noted that the road construction progress tracking and monitoring system provided by the embodiments of the present invention and the road construction progress tracking and monitoring method provided by the embodiments of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned road construction progress tracking and monitoring method, and has the same or similar beneficial effects. The repeated parts will not be elaborated again.

[0067] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A road construction progress tracking and monitoring method, characterized in that: include: Acquire a road construction image of a target road, and determine a suspected road connected domain from the road construction image; Determine the minimum circumscribed rectangle of the suspected road connected domain and multiple straight line segments of the suspected road connected domain; determine the pavement morphology regularity analysis parameters of the suspected road connected domain as a road area according to the first parameter of the minimum circumscribed rectangle and the second parameter of each of the straight line segments; the first parameter is the first length of the longest side of the minimum circumscribed rectangle and the second length of the shortest side; the second parameter is the length of the straight line segment; determine the roadbed color contrast parameters of the suspected road connected domain as a road area according to the gray value of the pixel points and the first number of the pixel points in the suspected road connected domain; determine the first possibility that the suspected road connected domain is a road area based on the pavement morphology regularity analysis parameters and the roadbed color contrast parameters; Selecting a suspected road connection domain whose first possibility is greater than or equal to a first threshold as the road area; Determine, according to the first possibility of the road area and the grayscale values ​​of the pixels in the road area, a second possibility that the road area is a paved road area, and select a road area where the second possibility is greater than or equal to a second threshold as the paved road area; A construction progress ratio of the target road in the road construction image is determined according to a first sum of first areas of all paved road regions in the road construction image and a second sum of second areas of all road regions in the target road.

2. The road construction progress tracking and monitoring method according to claim 1 is characterized in that: Determining the suspected road connected domain from the road construction image includes: Using a K-means clustering image segmentation algorithm to perform operations on the road construction image to obtain a plurality of clusters; Performing an operation on two adjacent road construction images in the cluster using an image alignment operation, and recording a region in which the overlapping connected domains in the two adjacent road construction images have changed as a suspected changed region; A connected domain corresponding to the suspected change area is determined as the suspected road connected domain.

3. The road construction progress tracking and monitoring method according to claim 1 is characterized in that: The determining of the minimum circumscribed rectangle of the suspected road connected domain and the plurality of straight line segments of the suspected road connected domain comprises: Determine a minimum convex polygon containing all pixels in the suspected road connected domain; By rotating each side of the minimum convex polygon, a rectangle parallel to the side is determined; Calculate the third area of ​​each rotated rectangle, and select the rectangle with the smallest third area as the minimum circumscribed rectangle; Using an edge detection algorithm to calculate the suspected road connected domain to obtain multiple edge points; Convert each edge point to polar coordinates and accumulate the number of occurrences for each polar coordinate position in Hough space; The position with the highest cumulative occurrence of each polar coordinate position in the Hough space is identified to determine the straight line segment in the suspected road connected domain.

4. The road construction progress tracking and monitoring method according to claim 1 is characterized in that: The road surface morphology regularization analysis parameter of determining that the suspected road connected region is a road area according to the first parameter of the minimum circumscribed rectangle and the second parameter of each straight line segment includes: Calculating a first ratio between a first length of the longest side of the minimum circumscribed rectangle and a second length of the shortest side of the minimum circumscribed rectangle; Superimposing the lengths of all straight line segments in the suspected road connected domain to obtain a first superimposed value; Calculating a second ratio of the first superposition value to a fourth area of ​​the suspected road connected domain, and a first product of the first ratio and the second ratio; The first product is normalized to obtain a road surface morphology regularization analysis parameter of the road area.

5. The road construction progress tracking and monitoring method according to claim 1 is characterized in that: The determining, based on the grayscale values ​​of the pixels in the suspected road connected region and the first number of pixels, that the suspected road connected region is a road area subgrade color contrast parameter comprises: Superimposing the grayscale values ​​of all pixels in the suspected road connected domain to obtain a second superimposed value and a third ratio between the second superimposed value and the first number; Calculating a first difference between the grayscale value of each pixel in the suspected road connected domain and the third ratio, and superimposing the first differences to obtain a third superimposed value; calculating a fourth ratio between the third superposition value and the first number; The fourth ratio is subjected to inverse proportional normalization processing to obtain a roadbed color contrast parameter where the suspected road connected domain is a road area.

6. The road construction progress tracking and monitoring method according to claim 1 is characterized in that: The first possibility of determining that the suspected road connected region is a road area based on the road surface morphology regularization analysis parameter and the roadbed color contrast parameter includes: Calculating a second product between the pavement morphology regularity analysis parameter and the roadbed color contrast parameter; The second product is normalized to obtain a first possibility that the suspected road connected region is a road area.

7. The road construction progress tracking and monitoring method according to claim 1 is characterized in that: The second possibility of determining that the road area is a paved road area according to the first possibility of the road area and the grayscale values ​​of the pixels in the road area includes: Calculating a first average value of grayscale values ​​of all pixels in the road area and a fifth ratio between the first likelihood and the first average value; The fifth ratio is normalized to obtain a second possibility that the road area is a paved road area.

8. The road construction progress tracking and monitoring method according to claim 1 is characterized in that: Determining the construction progress ratio of the target road in the road construction image according to a first sum of first areas of all paved road areas in the road construction image and a second sum of second areas of all road areas in the target road comprises: A sixth ratio between the first sum and the second sum is determined as a construction progress ratio of the target road in the road construction image.

9. The road construction progress tracking and monitoring method according to claim 1 is characterized in that: The acquiring of the road construction image of the target road comprises: Using a drone to photograph the target road at the same location of the target road every day to obtain a construction image; A mean filter algorithm is used to operate the construction image to obtain the road construction image.

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