Pavement three-dimensional depth modal deviation calibration method

Abstract

The invention discloses a pavement three-dimensional depth modal deviation calibration method, which relates to the technical field of computer image processing, and comprises the steps of data acquisition and storage, floating data restoration, line granularity deviation calibration and calibration data verification and storage. According to the invention, the high-speed three-dimensional structured light measurement camera assembly is combined with the movable detection platform, so that accurate acquisition of the three-dimensional depth mode of the pavement is realized, and meanwhile, a floating abnormal value in the three-dimensional depth mode is effectively restored by using a floating data restoration method of a dynamic regression parameter and a sliding threshold region, so that the detection accuracy is improved. In addition, the invention further provides a line granularity deviation calibration method based on a dynamic regression parameter reference plane, line-by-line calibration is carried out on the three-dimensional depth mode of the road surface, the line granularity deviation problem is remarkably improved, the data deviation problem is effectively improved, reliable data support is provided for road surface disease analysis, and the accuracy and efficiency of road surface detection are improved.

Description

Technical Field

[0001] The invention relates to the technical field of computer image processing, and in particular to a method for calibrating a road surface three-dimensional depth modal deviation. Background Art

[0002] With the development of transportation infrastructure, the use of advanced imaging equipment and computer vision algorithms to detect road defects has become the key to ensuring regular monitoring and maintenance of road conditions and maintaining their service performance. However, since traditional road defect detection technologies mostly rely on two-dimensional imaging equipment, they lack three-dimensional depth information of the road surface and are often unable to accurately perceive specific types of defects. They are also easily affected by ambient light interference and shadows, resulting in inaccurate detection and low recall rates.

[0003] Structured light imaging technology, with its high-speed three-dimensional measurement and continuous data acquisition capabilities, has become an indispensable mainstream means for pavement defect detection, providing key data support for accurate disease analysis. However, when collecting the three-dimensional depth mode of the pavement, due to unavoidable factors such as equipment vibration or unilateral longitudinal displacement deviation, data deviations often occur, affecting the accuracy of subsequent detection results, seriously restricting the development and application of pavement defect detection technology. Therefore, developing a pavement three-dimensional depth modal deviation calibration method can not only improve the accuracy of subsequent pavement disease detection, but also provide reliable data support for subsequent pavement maintenance and management. Summary of the invention

[0004] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a method for calibrating the three-dimensional depth modal deviation of a road surface. The specific steps of the method include preparation and installation of three-dimensional depth modal imaging equipment, data acquisition and storage, data quality detection, floating data repair, row granularity deviation calibration, and data storage and reporting. It solves the problem of data deviation caused by vibration or unilateral longitudinal displacement deviation when the structured light imaging equipment collects the three-dimensional depth modality of the road surface, and improves the accuracy and efficiency of road surface disease detection.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for calibrating the three-dimensional depth modal deviation of a road surface, wherein the specific steps of the calibration method are as follows:

[0006] S100, data acquisition and storage: install the high-speed 3D structured light measurement camera assembly on the movable detection platform and adjust it to the preset angle and height, connect the camera assembly to the data transmission line through the RS422 interface, connect the HES-2048 incremental encoder to control the trigger, debug the camera parameters according to the test road environment lighting and road characteristics, and monitor whether the histogram and grayscale distribution of the collected data visualization image are normal. The camera assembly processor processes the light signal, and the depth sensing module combines the structured light source to calculate the 3D depth mode of the road surface and form dual-modal data with the 2D intensity image, which is transmitted to the computer in real time for road reconstruction processing and storage;

[0007] S200, floating data repair: read the three-dimensional depth mode, use the floating data repair method based on dynamic regression parameters and sliding threshold area to repair the three-dimensional depth floating outliers, calculate the dynamic regression parameter DRP corresponding to the input three-dimensional depth mode, the dynamic regression parameter is the mean, median and mode of the three-dimensional depth mode with different weight combinations and paranoid variables, define the processing area and the sliding threshold area of ​​the corresponding step length, traverse all the values ​​in the processing area except the center point, and convert the values ​​less than the threshold into The 3D depth modal value is repaired with the minimum value greater than DRP in this area, where exp() represents the exponential function and n represents the 3D depth modal value. This process is repeated until all 3D data processing is completed and the processing result is finally output;

[0008] S300, row granularity deviation calibration: read the three-dimensional depth mode after floating repair and calculate its dynamic regression parameters again, calibrate the data using the row granularity deviation calibration method based on the dynamic regression parameter reference plane, strip the read data in row granularity units, read the three-dimensional depth modal data of a single row granularity, use the dynamic regression parameters as the reference plane, calculate the slope, deviation and unary straight line calibration function of the current row, perform position traversal on the depth data of the current row granularity, the corresponding position index is always from I to the maximum width of the data size, use the slope and deviation of the unary straight line calibration function to calculate the difference between the index position and the dynamic regression parameter, determine the data point offset direction according to the positive or negative difference, if the difference is less than zero, it is offset upward, otherwise it is offset downward, the offset is the absolute value of the difference, until all rows in the three-dimensional data are calibrated, and output the calibration data;

[0009] S400, calibration data verification and storage: visualize the calibrated data, compare it with the data visualization before calibration, observe the improvement of the granularity deviation, randomly select some areas from the calibrated 3D depth mode for detailed analysis, calculate the road surface flatness index and slope change parameters, and compare them with the actual measurement values ​​to verify the accuracy of the calibrated data. After the verification is completed, the original, pre-calibration, post-calibration data and analysis results are classified and stored, and a report containing test section information, equipment status, and data quality assessment is generated.

[0010] Furthermore, the high-speed three-dimensional structured light measurement camera component in S100, data acquisition and storage, acquires data in a linear array manner and performs imaging triggering via a HES-2048 incremental encoder.

[0011] Furthermore, in the S100, the HES-2048 incremental encoder in data acquisition and storage generates a contour trigger signal through an RS422 interface converter, which cooperates with the imaging trigger of the camera component to avoid data stretching and compression, and converts the displacement of one wheel rotation into a periodic 2048 pulse signal.

[0012] Furthermore, the auxiliary software in the data collection and storage in S100 includes cxDiscover, cxExplorer and cxShow3d, cxDiscover is used to monitor the working status of the camera component and modify and fix the IP address corresponding to the camera component, cxExplorer is used to debug the camera component parameters and monitor the quality of the collected data, and cxShow3d is used for pseudo-color visualization of three-dimensional depth mode.

[0013] Furthermore, the specific steps of the floating data repair method based on dynamic regression parameters and sliding threshold area in the floating data repair in S200 are as follows:

[0014] (1) Reading three-dimensional depth modality data I;

[0015] (2) Calculate the corresponding dynamic regression parameters (DRP) for I, the formula is: DRP = exp(0.3Avg(I)+0.5Med(I)+0.2Most(I)+σ), where Avg(·) represents mean calculation, Med(·) represents median statistics, Most(·) represents mode statistics, σ is a small bias variable, and I represents the read-in 3D depth mode;

[0016] (3) Delineate treatment area A (n×n) , where n can be adjusted dynamically, and the center point of A is used as the floating data point I to be repaired a ;

[0017] (4) Traverse area A except I a If there is a number less than If there is a 3D depth floating abnormal value, it will be repaired with the minimum value greater than DRP in this area;

[0018] (5) Repeat steps (3) to (4) with a step length of n until all three-dimensional depth modal data I are processed and the processing results are output.

[0019] Furthermore, the specific steps of the row particle size deviation calibration method based on the dynamic regression parameter reference plane in the row particle size deviation calibration in S300 are:

[0020] (1) Read the 3D depth modality after the floating data is repaired

[0021] (2) Read in Perform row-level extraction to obtain Q n ;

[0022] (3) Read Q n Using DRP as the reference plane, find the current Q n The slope q n With offset s n , forming the corresponding one-dimensional linear calibration function f (q,s) (x) = q n x i +s n ;

[0023] (4) For the current Q n Perform position traversal, the corresponding position index i always ranges from I to the maximum width w of the data size;

[0024] (5) Use a univariate straight line to calibrate the function f (q,s) The slope q of (x) n With deviation s n Calculate the difference err between index position i and DRP, err = abs(DRP-f (q,s) (x i ));

[0025] (6) Current Q n The correction value of each item i is x i ±err;

[0026] (7) Follow steps (4) to (6) until all rows in the 3D data are calibrated and the calibration data is output.

[0027] Furthermore, in the step S300, the calculation formula of the difference err in the particle size deviation calibration is: err = abs(DRP-f (q,s) (x i )), x i Indicates the position index of the current row data point, q n is the current row granularity Q n The slope of the corresponding univariate straight line calibration function, s n is the deviation of the univariate straight line calibration function corresponding to the current row granularity, q n and n Determined according to the fitting straight line equation, the formula is: (q,s) (x) = q n x i +s n .

[0028] Furthermore, in S300, the data offset in the row granularity deviation calibration is calculated, and the upper and lower offsets are err, that is, the distance that the data point needs to be offset, and the offset direction is based on DRP-f (q,s) (x i ) is positive or negative, upward or downward is relative to the reference plane, upward indicates the direction of increasing depth value, downward indicates the direction of decreasing depth value, that is, x i ±err.

[0029] Compared with the prior art, this road surface three-dimensional depth modal deviation calibration method has the following beneficial effects:

[0030] 1. In the data acquisition stage, the present invention uses a high-speed three-dimensional structured light measurement camera component to collect data in a linear array manner, and accurately triggers imaging with an incremental encoder, thereby effectively avoiding the problem of data stretching and compression and ensuring the original accuracy of the data. In the data processing stage, a floating data repair method combining dynamic regression parameters with sliding threshold areas can accurately identify and repair floating outliers in the three-dimensional depth modality, further improving the reliability of the data. In addition, a row granularity deviation calibration method based on a dynamic regression parameter reference plane can effectively calibrate the row granularity deviation in the data, making the final output three-dimensional depth modality more real and accurate, providing strong support for the precise positioning and quantitative analysis of road diseases.

[0031] 2. The present invention realizes real-time monitoring and adjustment of the working status of camera components and data acquisition quality through the integrated application of acquisition auxiliary software, ensuring the stability and efficiency of the data acquisition process. At the same time, the three-dimensional depth mode is visualized using pseudo-color visualization processing technology, which not only improves the readability of the data, but also facilitates users to intuitively observe the location, scope and type of road defects. In addition, through the automatic report generation function, the original data, pre- and post-calibration data and analysis results are classified and stored, and a detailed report containing test section information, equipment status, and data quality assessment content is generated, providing convenient data support for subsequent decision-making analysis.

[0032] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 is a flow chart of a method for calibrating a road surface three-dimensional depth modal deviation;

[0035] Figure 2 This is a schematic diagram of the distribution of data floating;

[0036] Figure 3 This is a schematic diagram of data offset caused by unilateral vertical displacement;

[0037] Figure 4 This is a schematic diagram of the particle size deviation calibration process;

[0038] Figure 5 Schematic diagram of the image quality improvement effect of different filtering methods in testing sample images. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1:

[0041] Urban road detection

[0042] Implementation environment and conditions: A section of urban road was selected as the test section. The road surface had common wear, repairs and slight unevenness, and the traffic flow was moderate, which was convenient for testing. The test was carried out during the day when the weather was clear and the ambient lighting met the normal working requirements of the camera to ensure the quality of the collected data. A specially configured mobile detection platform was used with a stable and moderate driving speed to provide stable motion conditions for data collection.

[0043] Specific implementation steps:

[0044] Data acquisition and storage (S100), properly install the high-speed 3D structured light measurement camera assembly on the detection platform, carefully adjust the camera angle to make it perpendicular to the road surface, and set the appropriate height to ensure that clear and accurate road surface images can be obtained. Connect the camera assembly and the data transmission line via the RS422 interface, and connect the HES-2048 incremental encoder. The encoder generates an accurate contour trigger signal through the RS422 interface converter, and works in conjunction with the camera assembly imaging trigger to accurately convert the displacement of one wheel rotation into 2048 pulse signals, thereby achieving precise control of the data acquisition frequency and position. The actual ambient lighting conditions of the road surface are taken into consideration, and various parameters of the camera, such as exposure time and gain, are carefully adjusted. At the same time, the quality of the collected data is closely monitored with the help of the auxiliary software cxExplorer to ensure that the histogram and grayscale distribution of the visualization image of the collected data are in normal state. The camera component processor quickly processes the light signal, and the depth sensing module combines the structured light source to calculate the three-dimensional depth mode of the road surface, which is formed into dual-modal data with the two-dimensional intensity image and transmitted to the computer in real time for road surface reconstruction and storage. In this process, the cxShow3d software can be used to perform pseudo-color visualization of the three-dimensional depth mode for intuitive observation of the data.

[0045] Floating data repair (S200), the computer reads the collected three-dimensional depth modal data completely, and uses the floating data repair method based on dynamic regression parameters and sliding threshold areas to process the data. First, the dynamic regression parameter DRP is calculated according to the formula: DRP = exp(0.3Avg(I) + 0.5Med(I) + 0.2Most(I) + σ), then a processing area A of appropriate size is defined (its size is flexibly determined according to the local characteristics of the road surface and the data distribution), and the center point of A is set as the floating data point I to be repaired. a Then, traverse the area A except I a For all values ​​other than If there is a floating abnormal value of 3D depth, it is repaired with the minimum value greater than DRP in this area, and this process is repeated with a step length equal to the side length of processing area A until all 3D depth modal data are processed, and finally the repaired 3D depth modal data is output.

[0046] Line granularity deviation calibration (S300), read the three-dimensional depth modal data after floating repair, accurately strip the data according to the line granularity unit, read the three-dimensional depth modal data of a single line granularity, use the previously calculated dynamic regression parameter DRP as the reference plane, and calculate the current slope q through the corresponding algorithm n With offset s n , forming the corresponding one-dimensional straight line calibration function, the formula is: f (q,s) (x) = q n x i +s n Then, the depth data of the current row granularity is comprehensively traversed, corresponding to the position index i from I to the maximum width of the data size, using the one-dimensional straight line calibration function f (q,s) The slope q of (x) n With deviation s n Calculate the difference err between index position i and DRP, the formula is: err = abs(DRP-f (q,s) (x i )) The offset direction of the data point is determined according to the positive or negative situation of the difference err. When DRP-f (q,s) (x i )<0, it shifts upward, otherwise it shifts downward, the offset is err, and the correction value is x i ±err (accurately determine the positive and negative according to the offset direction), follow this operation process until all rows in the 3D data are calibrated and output the calibrated data.

[0047] Verification and storage of calibration data (S400), visualization of the calibrated data, detailed comparison with the visualization of the data before calibration, visual observation of the improvement of the granularity deviation, such as whether the road texture becomes clearer and more continuous, whether the contours of potholes and repair marks become more accurate, random sampling of a certain number and area of ​​areas from the calibrated three-dimensional depth modality for in-depth and detailed analysis, calculation of the road surface flatness index (such as the International Roughness Index IRI) and slope change parameters, strict comparison of the calculated results with the actual measured values ​​(measured using high-precision professional measuring instruments), verification of the accuracy of the calibrated data, and a specific verification method of checking whether the errors between the flatness index and slope change parameters calculated from the calibrated data and the actual measured values ​​are within an allowable range.

[0048] The original, pre-calibration, and post-calibration data as well as detailed analysis results are classified and properly stored, and a detailed report is generated, which includes detailed information on the test section (such as the specific location, length, and width of the section), equipment status (including various camera parameters, and the driving speed of the detection platform), and data quality assessment (covering key content such as error analysis and visual comparison results), providing comprehensive and reliable data support for the maintenance and management of urban roads.

[0049] In summary: the present invention has demonstrated good practicality and effectiveness in urban road detection scenarios. By carefully installing camera components, accurately setting relevant parameters and rationally utilizing auxiliary software, high-quality road surface three-dimensional depth modal data is successfully collected and stored. In the floating data repair link, the method based on dynamic regression parameters and sliding threshold areas effectively handles outliers and improves data accuracy. The granularity deviation calibration further optimizes the data, significantly improving the visualization of road features. The accuracy of the calibrated data in the calculation of flatness index and slope change parameters is verified by comparing with the actual measured values. The generated detailed report provides rich and reliable data basis for the maintenance and management of urban roads, helps to accurately judge road conditions, rationally plan maintenance strategies, and improve the safety and comfort of urban road use. It also provides a reference example for subsequent similar road detection work.

[0050] Embodiment 2:

[0051] Highway Inspection

[0052] Implementation environment and conditions: A section of expressway was selected as the test section. This section has a large traffic volume and high speed. The road surface wear is relatively uniform, but there are typical problems such as some rutting and slight cracks. The inspection is arranged to be carried out at night when the traffic volume is relatively small to minimize the impact on traffic and ensure safe and efficient inspection operations. The weather conditions are good, without precipitation and fog interference, but the ambient light is weak, and it is necessary to fully rely on the lighting system of the inspection equipment itself to assist in data collection. A professionally modified high-speed inspection vehicle is used as a mobile inspection platform. The vehicle has high-speed driving capability and excellent stability, and can run safely and smoothly on the expressway. At the same time, it is equipped with a high-performance shock absorption system to effectively reduce vehicle bumps during driving, creating favorable conditions for data collection.

[0053] Specific implementation steps

[0054] Data collection and storage (S100): accurately install a high-speed 3D structured light measurement camera assembly at a specific position of the inspection vehicle, adjust the camera angle to a suitable angle with the road surface (such as 45 degrees), and set a reasonable height to achieve detection of a wider area of ​​the road surface and obtain more detailed information. Connect the camera assembly to the data transmission line in the vehicle through the RS422 interface, connect the HES-2048 incremental encoder, and reasonably set the encoder parameters to ensure that the camera imaging can be accurately triggered under high-speed driving conditions, and accurately convert the displacement of one wheel rotation into 2048 pulse signals to achieve accurate and synchronous data collection. According to the actual situation of weak nighttime ambient light, the key parameters of the camera's exposure time, gain, and intensity of the structured light source are carefully adjusted. The quality of the collected data is strictly monitored using the auxiliary software cxExplorer to ensure that the histogram and grayscale distribution of the visualization image of the collected data are normal. The camera component processor quickly processes the light signal, and the depth sensing module combines the structured light source to calculate the three-dimensional depth mode of the road surface, forming dual-modal data with the two-dimensional intensity image, which is transmitted to the computer in real time for road reconstruction processing and storage. During this period, the three-dimensional depth mode is visualized in pseudo-color with the help of cxShow3d software to assist in data observation and analysis.

[0055] The floating data repair (S200) completely reads the collected three-dimensional depth modal data, processes the data using a floating data repair method based on dynamic regression parameters and sliding threshold areas, and calculates the dynamic regression parameter DRP according to the formula: DRP = exp(0.3Avg(I) + 0.5Med(I) + 0.2Most(I) + σ), defines a processing area A of appropriate size (its size is determined according to road surface continuity and data correlation), and sets the center point of A as the floating data point I to be repaired. a , traverse area A except I a For all values ​​other than If there is a 3D depth floating abnormal value, it is repaired with the minimum value greater than DRP in this area, and this process is repeated with a step length equal to the side length of the processing area A until all 3D depth modal data are processed and the repaired 3D depth modal data is output.

[0056] Line granularity deviation calibration (S300), read the three-dimensional depth modal data after floating repair, accurately strip the data according to the line granularity unit, read the three-dimensional depth modal data of single line granularity, use the dynamic regression parameter DRP as the reference plane, and calculate the slope q of the current line through the relevant algorithm n With offset s n , forming the corresponding one-dimensional straight line calibration function, the formula is: f (q,s) (x) = q n x i +s n, perform a comprehensive position traversal of the depth data of the current row granularity, the corresponding position index i is always from I to the maximum width w of the data size, and use a one-dimensional straight line to calibrate the slope q of the function n With deviation s n Calculate the difference err between index position i and DRP, the formula is: err = abs(DRP-f (q,s) (x i )) The direction of data point offset is determined according to the positive or negative difference err. (q,s) (x i )<0, it will shift upward, otherwise it will shift downward, the offset is err, and the correction value is x i ±err (positive or negative is determined by the offset direction), follow this operation process until all rows in the 3D data are calibrated and the calibration data is output.

[0057] Verify and store calibration data (S400), visualize the calibrated data, and carefully compare it with the visualization effect of the data before calibration to observe the improvement of the granularity deviation, such as whether the shape of ruts and cracks is clearer, whether the depth measurement is more accurate, randomly select a certain number and area of ​​areas from the calibrated three-dimensional depth mode for in-depth analysis, calculate the flatness index and slope change parameters of the road surface, compare the calculated results with the actual measured values ​​(obtained using high-precision professional measuring instruments), and verify the accuracy of the calibrated data, that is, check whether the errors between the flatness index and slope change parameters calculated from the calibrated data and the actual measured values ​​are within a reasonable range.

[0058] The original, pre-calibration, and post-calibration data and analysis results are properly classified and stored, and a detailed report is generated, including test section information (section location, length, number of lanes), equipment status (camera parameters, detection vehicle speed), and data quality assessment (error analysis, visual comparison results), providing strong data support for the maintenance and management of highway pavement.

[0059] In summary, in the highway detection scenario, the present invention gives full play to its advantages. In view of the actual situation of large traffic volume, high speed and road surface characteristics on the highway, the appropriate detection period and equipment installation method are selected to ensure the feasibility and accuracy of data acquisition. During the data acquisition process, the coordinated work of the camera component and the encoder and the reasonable debugging of the parameters provide a solid data foundation for subsequent processing. The floating data repair and row granularity deviation calibration process effectively improve the data quality. From the visual comparison and the comparison results with the actual measurement values, the calibrated data can more accurately reflect the conditions of rutting and cracks on the road surface, and the accuracy of the flatness index and slope change parameters is effectively verified. The final generated report contains comprehensive information, which provides strong data support for highway maintenance decisions and safety assessments. It is of great significance to ensure the efficient operation and driving safety of highways, and also provides a practical reference for the further development of highway detection technology.

[0060] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A road surface three-dimensional depth modal deviation calibration method, characterized in that: The specific steps of this calibration method are: S100, data acquisition and storage: install the high-speed 3D structured light measurement camera assembly on the movable detection platform and adjust it to the preset angle and height, connect the camera assembly to the data transmission line through the RS422 interface, connect the HES-2048 incremental encoder to control the trigger, debug the camera parameters according to the test road environment lighting and road characteristics, and monitor whether the histogram and grayscale distribution of the collected data visualization image are normal. The camera assembly processor processes the light signal, and the depth sensing module combines the structured light source to calculate the 3D depth mode of the road surface and form dual-modal data with the 2D intensity image, which is transmitted to the computer in real time for road reconstruction processing and storage; S200, floating data repair: read the three-dimensional depth mode, use the floating data repair method based on dynamic regression parameters and sliding threshold area to repair the three-dimensional depth floating outliers, calculate the dynamic regression parameter DRP corresponding to the input three-dimensional depth mode, the dynamic regression parameter is the mean, median and mode of the three-dimensional depth mode with different weight combinations and paranoid variables, define the processing area and the sliding threshold area of ​​the corresponding step length, traverse all the values ​​in the processing area except the center point, and convert the values ​​less than the threshold into The 3D depth modal value is repaired with the minimum value greater than DRP in this area, where exp() represents the exponential function and n represents the 3D depth modal value. This process is repeated until all 3D data processing is completed and the processing result is finally output; S300, row granularity deviation calibration: read the three-dimensional depth mode after floating repair and calculate its dynamic regression parameters again, calibrate the data using the row granularity deviation calibration method based on the dynamic regression parameter reference plane, strip the read data in row granularity units, read the three-dimensional depth modal data of a single row granularity, use the dynamic regression parameters as the reference plane, calculate the slope, deviation and unary straight line calibration function of the current row, perform position traversal on the depth data of the current row granularity, the corresponding position index is always from I to the maximum width of the data size, use the slope and deviation of the unary straight line calibration function to calculate the difference between the index position and the dynamic regression parameter, determine the data point offset direction according to the positive or negative difference, if the difference is less than zero, it is offset upward, otherwise it is offset downward, the offset is the absolute value of the difference, until all rows in the three-dimensional data are calibrated, and output the calibration data; S400, calibration data verification and storage: visualize the calibrated data, compare it with the data visualization before calibration, observe the improvement of the granularity deviation, randomly select some areas from the calibrated 3D depth mode for detailed analysis, calculate the road surface flatness index and slope change parameters, and compare them with the actual measurement values ​​to verify the accuracy of the calibrated data, classify and store the original, pre-calibration, post-calibration data and analysis results, and generate a report containing test section information, equipment status, and data quality assessment.

2. A road surface three-dimensional depth modal deviation calibration method according to claim 1, characterized in that: The high-speed three-dimensional structured light measurement camera component in the S100, data acquisition and storage, acquires data in a linear array manner and performs imaging triggering via a HES-2048 incremental encoder.

3. A road surface three-dimensional depth modal deviation calibration method according to claim 1, characterized in that: In the S100, the HES-2048 incremental encoder in data acquisition and storage generates a contour trigger signal through an RS422 interface converter, which cooperates with the imaging trigger of the camera component to avoid data stretching and compression, and converts the displacement of one wheel rotation into a periodic 2048 pulse signal.

4. A road surface three-dimensional depth modal deviation calibration method according to claim 1, characterized in that: The auxiliary software in S100, data collection and storage includes cxDiscover, cxExplorer and cxShow3d, cxDiscover is used to monitor the working status of the camera component and modify and fix the IP address corresponding to the camera component, cxExplorer is used to debug the camera component parameters and monitor the quality of the collected data, and cxShow3d is used for pseudo-color visualization of three-dimensional depth mode.

5. A road surface three-dimensional depth modal deviation calibration method according to claim 1, characterized in that: The specific steps of the floating data repair method based on dynamic regression parameters and sliding threshold area in the floating data repair in S200 are as follows: (1) Reading three-dimensional depth modality data I; (2) Calculate the corresponding dynamic regression parameter DRP for I, the formula is: DRP = exp(0.3Avg(I) + 0.5Med(I) + 0.2Most(I) + σ), where Avg(·) represents mean calculation, Med(·) represents median statistics, Most(·) represents mode statistics, σ is a small bias variable, and I represents the read-in 3D depth mode; (3) Delineation of treatment area A (n×n) , where n can be adjusted dynamically, and the center point of A is used as the floating data point I to be repaired a ; (4) Traverse area A except I a If there is a number less than If there is a 3D depth floating abnormal value, it will be repaired with the minimum value greater than DRP in this area; (5) Repeat steps (3) to (4) with a step length of n until all three-dimensional depth modal data I are processed and the processing results are output.

6. A road surface three-dimensional depth modal deviation calibration method according to claim 1, characterized in that: The specific steps of the method for calibrating the row particle size deviation based on the dynamic regression parameter reference plane in the row particle size deviation calibration in S300 are as follows: (1) Read the 3D depth modality after the floating data is repaired (2) Read in Perform row-level extraction to obtain Q n ; (3) Read Q n Using DRP as the reference plane, find the current Q n The slope q n With offset s n , forming the corresponding one-dimensional straight line calibration function, the formula is: f (q,s) (x) = q n x i +s n ; (4) For the current Q n Perform position traversal, the corresponding position index i always ranges from I to the maximum width w of the data size; (5) Use a univariate straight line to calibrate the function f (q,s) The slope q of (x) n With deviation s n Calculate the difference err between index position i and DRP, the formula is: err = abs(DRP-f (q,s) (x i )); (6) Current Q n The correction value of each item i is x i ±err; (7) Follow steps (4) to (6) until all rows in the 3D data are calibrated and the calibration data is output.

7. A road surface three-dimensional depth modal deviation calibration method according to claim 1, characterized in that: In the step S300, the calculation formula of the difference err in the particle size deviation calibration is: err = abs (DRP - f (q,s) (x i )), x i Indicates the position index of the current row data point, q n is the current row granularity Q n The slope of the corresponding univariate straight line calibration function, s n is the deviation of the univariate straight line calibration function corresponding to the current row granularity, q n and n Determined according to the fitting straight line equation, the formula is: (q,s) (x) = q n x i +s n .

8. A road surface three-dimensional depth modal deviation calibration method according to claim 1, characterized in that: In the step S300, the data offset is calculated in the row size deviation calibration, the upper and lower offsets are err, and the offset direction is based on DRP-f (q,s) (x i ) is determined by its positive or negative value. Upward or downward is relative to the reference plane. Upward indicates the direction of increasing depth value, and downward indicates the direction of decreasing depth value.

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