Road paving construction index monitoring method and system

Abstract

The invention is suitable for the technical field of paving monitoring, and provides a road paving construction index monitoring method and system, and the method comprises the following steps: receiving manual monitoring index information and visual monitoring index information, the visual monitoring index information comprising a plurality of road paving images; the integrity and conformity of the manual monitoring index information are detected, and manual index monitoring information is generated; geometric correction is carried out on the road paving images, calibration length cross bars are identified, the paving width is determined based on the calibration length cross bars, and each road paving image comprises the calibration length cross bars; the road paving image is analyzed based on the computer vision technology, the paving surface quality is determined, and the paving surface quality comprises the flatness, the crack condition and the pothole condition; and determining visual index monitoring information according to the paving width, the flatness, the crack condition and the pothole condition. According to the invention, manual monitoring and visual monitoring are combined, and the advantages of the manual monitoring and visual monitoring are utilized to realize comprehensive coverage and monitoring of a construction site.

Description

Technical Field

[0001] The present invention relates to the technical field of paving monitoring, and in particular to a method and system for monitoring road paving construction indicators. Background Art

[0002] Road paving construction is a key link in ensuring road quality. Traditional road paving construction monitoring mainly relies on manual on-site monitoring. Traditional methods are not only time-consuming and labor-intensive, but also greatly affected by human factors, making it difficult to ensure the accuracy and consistency of data. In recent years, with the advancement of science and technology, especially the development of computer vision technology, new means have been provided for monitoring road paving construction. However, relying solely on visual monitoring or manual monitoring has its own limitations. Although manual monitoring can directly obtain actual data from the construction site, it is often difficult to achieve full coverage and real-time monitoring when facing large-scale, complex and changeable construction sites. At the same time, the accuracy and completeness of the monitoring data are also easily affected by various factors such as the skill level and work attitude of the monitoring personnel.

[0003] Although computer vision technology-based monitoring can achieve real-time monitoring and automatic data processing, in actual applications, due to the complexity and diversity of road paving construction, the accuracy and stability of visual monitoring results will be affected. Therefore, it is necessary to provide a road paving construction indicator monitoring method and system to solve the above problems. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a road paving construction index monitoring method and system to solve the problems existing in the above-mentioned background technology.

[0005] The present invention is implemented as follows: a method for monitoring road paving construction indicators, the method comprising the following steps: Receiving manual monitoring index information and visual monitoring index information, wherein the manual monitoring index information includes a plurality of construction indexes and monitoring data, and the visual monitoring index information includes a plurality of road paving images; Detect the completeness and compliance of manual monitoring indicator information and generate manual indicator monitoring information; Performing geometric correction on the road paving image in the visual monitoring index information, identifying the calibration length crossbar, and determining the paving width based on the calibration length crossbar, wherein each road paving image includes the calibration length crossbar; Analyze road paving images based on computer vision technology to determine the paving surface quality, including flatness, cracks and potholes; Determine visual indicator monitoring information based on paving width, flatness, crack conditions and pothole conditions.

[0006] Another object of the present invention is to provide a road paving construction index monitoring system, the system comprising: A monitoring index information module, used to receive manual monitoring index information and visual monitoring index information, wherein the manual monitoring index information includes a number of construction indexes and monitoring data, and the visual monitoring index information includes a number of road paving images; The manual indicator monitoring module is used to detect the completeness and compliance of manual monitoring indicator information and generate manual indicator monitoring information; A paving width determination module is used to perform geometric correction on the road paving image in the visual monitoring index information, identify the calibration length crossbar, and determine the paving width based on the calibration length crossbar, and each road paving image contains the calibration length crossbar; A surface quality determination module is used to analyze the road paving images based on computer vision technology to determine the paving surface quality, which includes flatness, cracks and potholes; The visual indicator monitoring module is used to determine the visual indicator monitoring information according to the paving width, flatness, crack conditions and pothole conditions.

[0007] Compared with the prior art, the present invention has the following beneficial effects: The present invention combines manual monitoring and visual monitoring, and utilizes the advantages of each to achieve comprehensive coverage and real-time monitoring of the construction site. By detecting the completeness and compliance of the manual monitoring index information, the manual index monitoring information is generated, thus ensuring the integrity and reliability of the data. The road paving images in the visual monitoring index information are geometrically corrected, and the calibration length crossbars are automatically identified. The paving width is automatically determined based on the calibration length crossbars. The road paving images are analyzed according to computer vision technology to determine the paving surface quality. This fully utilizes the advantages of computer vision technology and improves the accuracy and stability of the visual monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 The present invention is a flow chart of a method for monitoring indicators of road paving construction.

[0009] Figure 2 The present invention is a flow chart for generating artificial indicator monitoring information in a road paving construction indicator monitoring method.

[0010] Figure 3 The present invention is a flow chart for identifying a cross bar of a calibrated length in a method for monitoring indicators of road paving construction.

[0011] Figure 4 The present invention is a flow chart for determining the paving width in a road paving construction indicator monitoring method.

[0012] Figure 5 The present invention is a flow chart for determining the paving surface quality in a road paving construction indicator monitoring method.

[0013] Figure 6 The figure is a structural diagram of a road paving construction index monitoring system. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0015] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0016] like Figure 1 As shown, an embodiment of the present invention provides a method for monitoring road paving construction indicators, the method comprising the following steps: S100, receiving manual monitoring index information and visual monitoring index information, wherein the manual monitoring index information includes a plurality of construction indexes and monitoring data, and the visual monitoring index information includes a plurality of road paving images; S200, detecting the completeness and conformity of the manual monitoring indicator information, and generating manual indicator monitoring information; S300, geometrically correcting the road paving image in the visual monitoring index information, identifying the calibration length crossbar, and determining the paving width based on the calibration length crossbar, wherein each road paving image includes the calibration length crossbar; S400, which uses computer vision technology to analyze road paving images to determine the paving surface quality, including flatness, cracks and potholes; S500 determines visual indicator monitoring information based on paving width, flatness, crack conditions and pothole conditions.

[0017] It should be noted that road paving construction is a key link in ensuring road quality, and relying solely on visual monitoring or manual monitoring has its own limitations. Although manual monitoring can directly obtain actual data from the construction site, it is often difficult to achieve comprehensive coverage and real-time monitoring when faced with large-scale, complex and changeable construction sites. At the same time, the accuracy and completeness of the monitoring data are also easily affected by various factors such as the skill level and work attitude of the monitoring personnel. Although monitoring based on computer vision technology can achieve real-time monitoring and automatic data processing, in actual applications, due to the complexity and diversity of road paving construction, the accuracy and stability of visual monitoring results will be affected. The embodiments of the present invention are intended to solve the above problems.

[0018] In an embodiment of the present invention, manual monitoring and visual monitoring are combined, and the advantages of each are utilized to achieve comprehensive coverage and real-time monitoring of the construction site. Specifically, it is first necessary to obtain manual monitoring index information and visual monitoring index information. The manual monitoring index information includes a number of construction indicators and monitoring data, which can provide direct and accurate information of the construction site. The visual monitoring index information includes a number of road paving images, and the road paving images are used to automatically monitor the indicators with high accuracy. Then, the completeness and conformity of the manual monitoring index information will be tested to generate manual index monitoring information. In this way, the integrity and reliability of the data can be ensured, which helps to reduce the impact of human factors on the monitoring results and improve the accuracy and consistency of the data. Then, the road paving image in the visual monitoring index information will be geometrically corrected, and the calibration length crossbar will be automatically identified, and the paving width will be automatically determined based on the calibration length crossbar.

[0019] It should be noted that each road paving image contains a calibrated length bar, which has a certain length value. Finally, the road paving image will be analyzed based on computer vision technology to determine the paving surface quality, which includes flatness, cracks and potholes. The visual indicator monitoring information is determined based on the paving width, flatness, cracks and potholes. In this way, the advantages of computer vision technology are fully utilized to improve the accuracy and stability of visual monitoring results.

[0020] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of detecting the integrity and compliance of the manual monitoring indicator information and generating the manual indicator monitoring information specifically includes: S201, determining whether the construction indicators in the manual monitoring indicator information are complete, and obtaining the completeness of the manual indicators; S202, determining whether the monitoring data corresponding to each construction indicator meets the specification, and obtaining the compliance degree of the manual indicator; S203: Generate artificial indicator monitoring information according to the artificial indicator completeness and artificial indicator compliance.

[0021] In an embodiment of the present invention, after receiving manual monitoring index information, it is automatically determined whether the construction indicators in the manual monitoring index information are complete, that is, whether the construction indicators are complete, so that the completeness of the manual indicators can be obtained, and then it is automatically determined whether the monitoring data corresponding to each construction indicator meets the specifications, that is, whether the construction indicators are within the corresponding qualified range, so that the manual indicator compliance can be obtained, and then the manual indicator monitoring information can be generated.

[0022] like Figure 3As shown, as a preferred embodiment of the present invention, the step of geometrically correcting the road paving image in the visual monitoring index information and identifying the calibration length crossbar specifically includes: S301, comparing the road paving image with the reference template image to determine a number of control points; S302, calculating correction parameters based on control points and an affine transformation model, and performing geometric correction; S303, retrieve the color features and contour features of the calibration length crossbar, and identify the calibration length crossbar based on the color features and contour features.

[0023] In the embodiment of the present invention, in order to better perform subsequent processing, it is necessary to perform geometric correction on the road paving image. Specifically, the road paving image will be compared with the reference template image. The reference template image is set in advance, and several control points are determined. The control points will be evenly distributed in the entire image. The more control points there are, the better the correction effect is usually. The control points include intersections of roads, corners of buildings, etc. Then, the correction parameters are calculated based on the control points and the affine transformation model, and geometric correction is performed to check whether the corrected image meets the requirements, including whether the distortion is eliminated, whether the original information of the image is maintained, etc. Finally, the color features and contour features of the calibration length crossbar are retrieved, and the calibration length crossbar in the image is automatically identified based on the color features and contour features.

[0024] As a preferred embodiment of the present invention, the step of comparing the road paving image with the reference template image to determine a plurality of control points specifically includes the following sub-steps: The road paving image and the reference template image are converted into grayscale images to obtain a road grayscale image and a template grayscale image respectively; The SIFT algorithm is used to perform feature detection on the road grayscale image and the template grayscale image to obtain the key point feature description of the road paving image and the key point feature description of the reference template image; Based on K-nearest neighbor matching, each key point feature description in the road paving image is matched with the key point feature description of at least two reference template images to obtain a number of K-nearest neighbor matching pairs; Lowe's ratio test is used to filter the K nearest neighbor matching pairs to obtain several filtered matching pairs; Calculating confidence scores for the plurality of filtered matching pairs to obtain a first calculation result, and normalizing the first calculation result into a probability distribution; Based on the RANSAC algorithm, the probability distribution is used to perform weighted sampling and iterative calculation on several filtered matching pairs. After the iteration is completed, the optimal homography matrix is ​​obtained; Using the optimal homography matrix, the four corner points of the road paving image are perspective transformed to obtain the transformed corner point coordinates, and the transformed corner point coordinates are mapped to the reference template image to obtain the corresponding four control points on the reference template image.

[0025] In the embodiment of the present invention, the SIFT and RANSAC technologies are combined to effectively deal with the noise, occlusion and scale changes in the image, ensure the reliability of feature matching, and add a weighted mechanism of matching confidence on the basis of traditional RANSAC, so that high confidence matching pairs have a higher probability of selection during model fitting. By utilizing the confidence information, the accuracy and robustness of the model estimation are improved.

[0026] As a preferred embodiment of the present invention, the step of calculating correction parameters based on control points and an affine transformation model and performing geometric correction specifically includes the following sub-steps: The control points of the road paving image and the reference template image are respectively described by two sets of linear equations for the transformation relationship of the horizontal and vertical coordinates to obtain a coordinate matrix; Decomposing the coordinate matrix by singular value to obtain affine transformation parameters, and constructing an affine transformation matrix according to the affine transformation parameters; Using an affine transformation matrix to transform each pixel coordinate in the road paving image to obtain a new coordinate; The pixel values ​​of the new coordinates are calculated using the bilinear interpolation method, and then post-processing and boundary adjustment are performed in sequence to obtain the transformed image.

[0027] In the embodiment of the present invention, because the control points directly correspond between the known original and target positions, the use of control points can ensure the accuracy of image transformation, and an accurate transformation model can be obtained by calculating the transformation relationship between these points; and the use of singular value decomposition to solve the linear equations can ensure the accurate calculation of affine transformation parameters, perform well when dealing with insufficient rank matrices and noise interference, and can provide a stable least squares solution to ensure the accuracy of geometric correction.

[0028] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of determining the paving width based on the calibrated length crossbar specifically includes: S304, calculating the pixel length Lp of the calibrated length cross bar in the road paving image according to the identified calibrated length cross bar; S305, calculating the crossbar length ratio Sc, the crossbar length ratio Sc=Lr / Lp, where Lr represents the actual length of the calibrated length crossbar; S306, identify the paving edges parallel to the calibrated length crossbar, determine the pixel width Wp between the edges, and calculate the actual paving width Wr, Wr=Wp×Sc.

[0029] In an embodiment of the present invention, the pixel length Lp of the calibrated length cross bar in the road paving image is determined based on the identified calibrated length cross bar. The actual length Lr of the calibrated length cross bar is known, so that the cross bar length ratio Sc can be calculated, and the cross bar length ratio Sc=Lr / Lp. Then, the paving edge parallel to the calibrated length cross bar is identified, and an edge detection algorithm or a straight line fitting algorithm can be used to identify the edge, and the pixel width Wp between the edges is determined. Finally, the actual paving width Wr is calculated, Wr=Wp×Sc.

[0030] like Figure 5 As shown, as a preferred embodiment of the present invention, the step of analyzing the road paving image based on computer vision technology to determine the paving surface quality specifically includes: S401, traverse each pixel point in the road paving image, count the grayscale value, calculate the average value and standard deviation of the grayscale value, and obtain the overall flatness; S402, detecting crack areas in the road paving image according to a threshold segmentation algorithm, and determining crack conditions according to crack width, length and gray value characteristics; S403, detecting pothole areas in the road paving image according to an edge detection algorithm, and determining pothole conditions according to shape, size and depth features of the potholes.

[0031] In the embodiment of the present invention, in order to determine the flatness, cracks and potholes, each pixel in the road paving image is automatically traversed, the grayscale value is counted, the average and standard deviation of the grayscale value are calculated, and the overall flatness is obtained. Furthermore, the image can be divided into multiple small areas, and the grayscale value distribution of each small area is compared to find out the area with large differences, which may represent uneven areas. Then, the crack area in the road paving image is detected according to the threshold segmentation algorithm, and the crack condition is determined according to the width, length and grayscale value characteristics of the crack; Furthermore, machine learning algorithms (such as K-means clustering, support vector machines, etc.) can be used to further classify and confirm the cracks, and classify them according to the severity of the cracks, such as slight cracks, moderate cracks, and severe cracks. Then, the potholes in the road paving image are detected based on the edge detection algorithm, and the pothole conditions are determined based on the shape, size, and depth characteristics of the potholes. Further, the detected potholes are quantitatively evaluated, such as calculating the area, depth, and volume of the potholes, and the potholes are classified according to the evaluation results, such as small potholes, medium potholes, and large potholes.

[0032] As a preferred embodiment of the present invention, the step of traversing each pixel point in the road paving image, counting the grayscale value, calculating the average value and standard deviation of the grayscale value, and obtaining the overall flatness specifically includes the following sub-steps: The road grayscale image is converted into the frequency domain by Fourier transform and the spectrum is centered to obtain the spectrum; The spectrum is filtered using a filter, and the filtered spectrum is then converted back to the spatial domain by inverse Fourier transform to obtain a filtered image; In a way that each scale corresponds to a different Gaussian blur degree, a multi-scale smoothing operation is performed on the filtered image to obtain a multi-scale smoothed image; Calculate the difference between the filtered image and the multi-scale smoothed image to obtain the differences at different scales; The standard deviation of the differences at different scales is calculated to obtain the local standard deviation at different scales; The local standard deviations of different scales are accumulated and normalized to obtain the normalized local standard deviation; Calculate the overall grayscale mean of the filtered image to obtain the global mean; The normalized local standard deviation is used to measure the degree of change of each part of the image, and the global mean is used as the reference for the overall brightness of the image. The global mean and the normalized local standard deviation are weightedly combined in a linear combination to obtain the overall flatness.

[0033] In the embodiment of the present invention, in order to ensure the accuracy of the overall flatness calculation, the present invention captures the multi-scale features of the image by performing smoothing and local standard deviation calculations at different scales. This method can identify surface changes at different scales, making it suitable for various complex scenes, and by using Fourier transform and frequency domain filtering, the frequency information of interest can be effectively separated and retained. This is very helpful for removing noise and highlighting important details, thereby improving the accuracy of flatness assessment. Finally, the local changes and overall features are combined together to balance the local and global information, which can more comprehensively reflect the complex features of the image, so as to more accurately and comprehensively evaluate the overall flatness of the road paving image.

[0034] As a preferred embodiment of the present invention, the step of detecting the crack area in the road paving image according to the threshold segmentation algorithm and determining the crack condition according to the width, length and gray value characteristics of the crack specifically includes the following sub-steps: The road grayscale image is subjected to contrast enhancement by using local histogram equalization to obtain a contrast enhanced image; The contrast-enhanced image is segmented using an adaptive threshold segmentation method to generate a binary image; The binary image is processed by using ratio operation to obtain a processed image; Perform contour detection on the processed image to obtain several contours and corresponding bounding boxes; calculate the length of each contour and the width of the bounding box, and calculate the average grayscale value of the contour; determine the crack condition based on the length of each contour, the width of the bounding box and the average grayscale value.

[0035] In the embodiment of the present invention, based on the local histogram equalization technology, the contrast of the image is improved, making the cracks more obvious under different lighting conditions. It is helpful to improve the accuracy of detection, especially in uneven lighting or dim environments, and the adaptive threshold segmentation determines the threshold according to the local information of each pixel neighborhood, which can more effectively handle complex backgrounds and uneven lighting, thereby improving the sensitivity and stability of crack detection, removing noise through morphological closing operations, making the boundaries of cracks smoother, effectively reducing false detections, and improving the reliability of the results.

[0036] like Figure 6 As shown, an embodiment of the present invention further provides a road paving construction index monitoring system, the system comprising: The monitoring index information module 100 is used to receive manual monitoring index information and visual monitoring index information, wherein the manual monitoring index information includes a number of construction indexes and monitoring data, and the visual monitoring index information includes a number of road paving images; The manual indicator monitoring module 200 is used to detect the completeness and compliance of the manual monitoring indicator information and generate manual indicator monitoring information; A paving width determination module 300 is used to perform geometric correction on the road paving image in the visual monitoring index information, identify the calibration length crossbar, and determine the paving width based on the calibration length crossbar, each road paving image includes the calibration length crossbar; A surface quality determination module 400 is used to analyze the road paving image based on computer vision technology to determine the paving surface quality, where the paving surface quality includes flatness, cracks and potholes; The visual indicator monitoring module 500 is used to determine the visual indicator monitoring information according to the paving width, flatness, crack conditions and pothole conditions.

[0037] As a preferred embodiment of the present invention, the manual indicator monitoring module 200 includes: The indicator completeness unit is used to determine whether the construction indicators in the manual monitoring indicator information are complete and obtain the manual indicator completeness; The indicator compliance unit is used to determine whether the monitoring data corresponding to each construction indicator meets the specifications and obtain the artificial indicator compliance; The artificial indicator monitoring unit is used to generate artificial indicator monitoring information according to the completeness and compliance of the artificial indicator.

[0038] As a preferred embodiment of the present invention, the paving width determination module 300 includes: A control point determination unit, used to compare the road paving image with the reference template image to determine a number of control points; A geometric correction unit, used for calculating correction parameters based on control points and an affine transformation model to perform geometric correction; The calibration crossbar recognition unit is used to retrieve the color features and contour features of the calibration length crossbar, and recognize the calibration length crossbar based on the color features and contour features.

[0039] As a preferred embodiment of the present invention, the paving width determination module 300 further includes: A pixel length calculation unit, used to calculate the pixel length Lp of the calibrated length crossbar in the road paving image according to the identified calibrated length crossbar; The crossbar length ratio unit is used to calculate the crossbar length ratio Sc, which is the crossbar length ratio Sc=Lr / Lp, where Lr represents the actual length of the crossbar with the calibrated length; The actual paving width unit is used to identify the paving edges parallel to the calibrated length crossbar, determine the pixel width Wp between the edges, and calculate the actual paving width Wr, Wr=Wp×Sc.

[0040] As a preferred embodiment of the present invention, the surface quality determination module 400 includes: The overall smoothness unit is used to traverse each pixel point in the road paving image, count the grayscale value, calculate the average value and standard deviation of the grayscale value, and obtain the overall smoothness; A crack condition determination unit is used to detect crack areas in the road paving image according to a threshold segmentation algorithm, and determine the crack condition according to the width, length and gray value characteristics of the crack; The pothole condition determination unit is used to detect the pothole area in the road paving image according to the edge detection algorithm, and determine the pothole condition according to the shape, size and depth characteristics of the pothole.

[0041] The above only describes in detail the preferred embodiments of the present invention, which is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0042] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0043] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0044] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

Claims

1. A road paving construction index monitoring method, characterized in that: The method comprises the following steps: Receiving manual monitoring index information and visual monitoring index information, wherein the manual monitoring index information includes a plurality of construction indexes and monitoring data, and the visual monitoring index information includes a plurality of road paving images; Detect the completeness and compliance of manual monitoring indicator information and generate manual indicator monitoring information; Performing geometric correction on the road paving image in the visual monitoring index information, identifying the calibration length crossbar, and determining the paving width based on the calibration length crossbar, wherein each road paving image includes the calibration length crossbar; Analyze road paving images based on computer vision technology to determine the paving surface quality, including flatness, cracks and potholes; Determine visual indicator monitoring information based on paving width, flatness, crack conditions and pothole conditions; The step of detecting the completeness and conformity of the manual monitoring indicator information and generating the manual indicator monitoring information specifically includes: Determine whether the construction indicators in the manual monitoring indicator information are complete, and obtain the completeness of the manual indicators; Determine whether the monitoring data corresponding to each construction indicator meets the specifications and obtain the compliance degree of the artificial indicator; Generate artificial indicator monitoring information based on artificial indicator completeness and artificial indicator compliance.

2. The road paving construction index monitoring method according to claim 1 is characterized in that: The step of geometrically correcting the road paving image in the visual monitoring index information and identifying the calibration length crossbar specifically includes: Compare the road paving image with the reference template image to determine a number of control points; Calculate correction parameters based on control points and affine transformation model to perform geometric correction; The color features and contour features of the calibration length crossbar are retrieved, and the calibration length crossbar is identified based on the color features and contour features.

3. The road paving construction index monitoring method according to claim 2 is characterized in that: The step of comparing the road paving image with the reference template image to determine a number of control points specifically includes the following sub-steps: The road paving image and the reference template image are converted into grayscale images to obtain a road grayscale image and a template grayscale image respectively; The SIFT algorithm is used to perform feature detection on the road grayscale image and the template grayscale image to obtain the key point feature description of the road paving image and the key point feature description of the reference template image; Based on K-nearest neighbor matching, each key point feature description in the road paving image is matched with the key point feature description of at least two reference template images to obtain a number of K-nearest neighbor matching pairs; Lowe's ratio test is used to filter the K nearest neighbor matching pairs to obtain several filtered matching pairs; Calculating confidence scores for the plurality of filtered matching pairs to obtain a first calculation result, and normalizing the first calculation result into a probability distribution; Based on the RANSAC algorithm, the probability distribution is used to perform weighted sampling and iterative calculation on several filtered matching pairs. After the iteration is completed, the optimal homography matrix is ​​obtained; Using the optimal homography matrix, the four corner points of the road paving image are perspective transformed to obtain the transformed corner point coordinates, and the transformed corner point coordinates are mapped to the reference template image to obtain the corresponding four control points on the reference template image.

4. The road paving construction index monitoring method according to claim 3 is characterized in that: The step of calculating the correction parameters based on the control points and the affine transformation model and performing geometric correction specifically includes the following sub-steps: The control points of the road paving image and the reference template image are respectively described by two sets of linear equations for the transformation relationship of the horizontal and vertical coordinates to obtain a coordinate matrix; Decomposing the coordinate matrix by singular value to obtain affine transformation parameters, and constructing an affine transformation matrix according to the affine transformation parameters; Using an affine transformation matrix to transform each pixel coordinate in the road paving image to obtain a new coordinate; The pixel values ​​of the new coordinates are calculated using the bilinear interpolation method, and then post-processing and boundary adjustment are performed in sequence to obtain the transformed image.

5. The road paving construction index monitoring method according to claim 4 is characterized in that: The step of determining the paving width based on the calibrated length crossbar specifically includes: According to the identified calibrated length crossbar, the pixel length Lp of the calibrated length crossbar in the road paving image is calculated; Calculate the crossbar length ratio Sc, crossbar length ratio Sc=Lr / Lp, where Lr represents the actual length of the calibrated length crossbar; Identify the paving edges parallel to the calibrated length crossbar, determine the pixel width Wp between the edges, and calculate the actual paving width Wr, Wr=Wp×Sc.

6. The road paving construction index monitoring method according to claim 5 is characterized in that: The step of analyzing the road paving image based on computer vision technology to determine the paving surface quality specifically includes: Traverse each pixel in the road paving image, count the grayscale value, calculate the average and standard deviation of the grayscale value, and obtain the overall flatness; The crack area in the road paving image is detected according to the threshold segmentation algorithm, and the crack condition is determined according to the width, length and gray value characteristics of the crack; The pothole area in the road paving image is detected based on the edge detection algorithm, and the pothole condition is determined based on the shape, size and depth characteristics of the pothole.

7. The road paving construction index monitoring method according to claim 6 is characterized in that: The step of traversing each pixel point in the road paving image, counting the grayscale value, calculating the average value and standard deviation of the grayscale value, and obtaining the overall flatness specifically includes the following sub-steps: The road grayscale image is converted into the frequency domain by Fourier transform and the spectrum is centered to obtain the spectrum; The spectrum is filtered using a filter, and the filtered spectrum is then converted back to the spatial domain by inverse Fourier transform to obtain a filtered image; In a way that each scale corresponds to a different Gaussian blur degree, a multi-scale smoothing operation is performed on the filtered image to obtain a multi-scale smoothed image; Calculate the difference between the filtered image and the multi-scale smoothed image to obtain the differences at different scales; The standard deviation of the differences at different scales is calculated to obtain the local standard deviation at different scales; The local standard deviations of different scales are accumulated and normalized to obtain the normalized local standard deviation; Calculate the overall grayscale mean of the filtered image to obtain the global mean; The normalized local standard deviation is used to measure the degree of change of each part of the image, the global mean is used as the reference of the overall brightness of the image, and the global mean and the normalized local standard deviation are weightedly combined by linear combination to obtain the overall flatness.

8. The road paving construction index monitoring method according to claim 7, characterized in that: The step of detecting the crack area in the road paving image according to the threshold segmentation algorithm and determining the crack condition according to the width, length and gray value characteristics of the crack specifically includes the following sub-steps: The road grayscale image is contrast enhanced by using local histogram equalization to obtain a contrast enhanced image; The contrast-enhanced image is segmented using an adaptive threshold segmentation method to generate a binary image; The binary image is processed by using ratio operation to obtain a processed image; Perform contour detection on the processed image to obtain several contours and corresponding bounding boxes; The length of each contour and the width of the bounding box are calculated, and the average gray value of the contour is calculated. The crack condition is determined based on the length of each contour, the width of the bounding box and the average gray value.

9. A road paving construction index monitoring system, the system being applied to the road paving construction index monitoring method according to any one of claims 1 to 8, characterized in that: The system comprises: A monitoring index information module, used to receive manual monitoring index information and visual monitoring index information, wherein the manual monitoring index information includes a number of construction indexes and monitoring data, and the visual monitoring index information includes a number of road paving images; The manual indicator monitoring module is used to detect the completeness and compliance of manual monitoring indicator information and generate manual indicator monitoring information; A paving width determination module is used to perform geometric correction on the road paving image in the visual monitoring index information, identify the calibration length crossbar, and determine the paving width based on the calibration length crossbar, and each road paving image contains the calibration length crossbar; A surface quality determination module is used to analyze the road paving images based on computer vision technology to determine the paving surface quality, which includes flatness, cracks and potholes; The visual indicator monitoring module is used to determine the visual indicator monitoring information according to the paving width, flatness, crack conditions and pothole conditions.

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