Road surface disease line scanning type detection method

Abstract

The invention belongs to the field of intelligent detection, and particularly relates to a road surface disease line scanning type detection method. The invention aims to solve the problems of how to efficiently and accurately detect pavement diseases, so as to timely find and evaluate the pavement diseases, prolong the service life of a road, reduce the maintenance cost and guarantee the driving safety. A scanning device is fixedly arranged at the front end of a carrier to obtain linear array point cloud data capable of representing pavement shape and position information, and a method for obtaining two-step scanning section fitting information is designed according to the linear array point cloud data. And road surface abnormal points are intelligently identified according to the sizes of coordinate points deviating from scanning section fitting straight lines. In addition, the marks arranged on the road shoulder at equal intervals are read in real time through the mark reading device fixedly connected to the carrier, the to-be-detected road surface is divided into a plurality of detection units, and segmented detection is achieved. Through clustering and quantitative analysis of abnormal points, the disease area in each detection unit is effectively evaluated. And finally, detecting pavement disease abnormity and front and back changes in combination with a threshold discrimination method and a reference template comparison method.

Description

Technical Field

[0001] The invention belongs to the field of intelligent detection, and in particular relates to a road surface disease line scanning detection method. Background Art

[0002] Pavement defects refer to local damage to the road surface such as cracks, potholes, subsidence, rutting, bumps, and bulges caused by construction quality problems or external forces. Severe pavement defects will threaten driving safety and lead to serious traffic accidents. Timely discovery of pavement defects can not only ensure driving safety, but also prevent further expansion of pavement defects, thereby increasing road life and reducing repair and maintenance costs. However, manual regular inspections or some existing pavement defect detection methods and equipment often have shortcomings such as high workload, low accuracy, and low detection efficiency, and it is difficult to compare the before and after changes at the same location. Therefore, the development of efficient and reliable pavement defect detection methods is of great significance for pavement engineering acceptance, pavement status and aging assessment, and daily repair and maintenance. Summary of the invention

[0003] In order to achieve efficient detection of pavement defects, the present invention provides a method for detecting pavement defects using 3D point cloud processing technology, which can accurately identify, locate and quantify pavement defects and their anomalies and changes, greatly facilitating the acceptance, maintenance and repair of pavement projects and ensuring driving safety. Different from other detection methods, it has the advantages of convenience and accuracy.

[0004] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions:

[0005] The present invention provides a road surface disease line scanning detection method, comprising:

[0006] Vehicle 1: A conventional four-wheeled motor vehicle, with a logo reading device fixed on its front end for reading logos I, II, III, etc. arranged evenly on the shoulder of the road;

[0007] Scanning device 2: installed at the front end of the carrier, the laser source on it projects the laser vertically downward to form a laser scanning surface;

[0008] The scanning device acquires the y and z coordinates of the dense points distributed on the “I” laser line along the width direction of the road surface, and the x coordinate is obtained by combining the real-time speed of the vehicle and the timestamp of each frame of linear point cloud data, thereby acquiring linear 3D point cloud data representing road surface information by line scanning.

[0009] The detection steps are:

[0010] (1) performing least squares linear fitting on the three-dimensional point cloud data of the linear array to obtain a fitting straight line for the scanned section, and identifying abnormal points representing the diseased area according to the deviation distance between each coordinate point on the linear array and the fitting straight line;

[0011] (2) reading the identification by the reading device to realize segmented detection, clustering and quantitatively analyzing the abnormal points in each detection unit obtained by the division, determining the size of the diseased area, and determining the number of diseases according to the number of sets obtained by clustering processing;

[0012] (3) Detect pavement damage anomalies and changes through the threshold method and the benchmark template comparison method.

[0013] In the above scheme, in step (1), the road surface fitting straight line is solved in two steps:

[0014] The first step is to perform zy straight line fitting based on the least squares method with all the coordinate points of each frame of the linear point cloud as a set to obtain the fitting line

[0015] The second step is to calculate the coordinates of each point to the fitting line If the distance is greater than a certain threshold, the point and its neighboring points are filtered out, and the remaining points are refitted with a zy straight line to obtain a fitting straight line representing the current scanning section. Determine the representation of the entire road surface in the point cloud coordinate system after removing the obvious bumps and bumps. Ay+Bz+C=0 represents the fitting straight line The equation Ay+Bz+C=0 is always satisfied, where A, B, C represent the coefficients of the straight line equation, and z and y represent the vertical and horizontal coordinates in the road surface point cloud data.

[0016] In the above scheme, in step (1), the coordinate point deviates from the scanning section fitting line Identify abnormal points on the road surface by size:

[0017] When a coordinate point p in the linear point cloud i，j (x i ,y i，j , z i，j ) to the scan section fitting line When the distance exceeds the deviation threshold, the coordinate point is considered to be a road surface disease abnormal point:

[0018] p i，j (x i ,y i，j , z i，j )arrive The deviation distance is:

[0019]

[0020] If d i，j ≥T 3σ , then p i，j is an abnormal point, according to the deviation threshold T3σ Identify the current frame line point cloud P i All abnormal points.

[0021] In the above scheme, the deviation threshold T 3σ Determined by the Raida criterion: Sample the road surface without disease in the section to be tested, calculate the distance from the coordinate point to the fitting straight line of the scan section and calculate the standard deviation σ, and the deviation threshold T 3σ It is 3 times the standard deviation σ.

[0022] In the above scheme, in step (2), a segmented detection mechanism is used to determine the location of the abnormal point: by reading the serial number corresponding to the mark through the mark reading device fixed on the vehicle, it can be determined between which two marks the current detection position is located, and then combined with the abnormal frame and its corresponding real-time speed information, the distance from the abnormal position to the mark can be accurately determined. Specifically, the marks are arranged at equal intervals of Δx on the shoulder of the road. When the reading device is facing the mark, the serial number corresponding to the mark is read, and the specific location of the abnormality is found according to the serial number. For example, assuming that the abnormality is identified at time t m , and the time t m At the time t of reading the identification I and reading the identification II I and t II , then we know that the anomaly must appear between markers I and II; assuming that from t I The calculation starts when the marker I is read at time t. If the mth frame linear array data is abnormal, the abnormal identification time t m Corresponding to the mth frame of linear point cloud data after the identification I is read, the abnormal position can be accurately located according to the real-time speed information:

[0023]

[0024] Where v m Indicates the real-time speed of the vehicle when acquiring the mth frame data, x m_I represents the x-distance from the outlier point to the marker I, v i It indicates the real-time speed of the vehicle 1 when the i-th frame of linear point cloud data is obtained starting from reading the identifier I.

[0025] In the above scheme, in step (2), the abnormal points in each detection unit divided by the mark are clustered, the abnormal points belonging to different disease areas are classified into different sets, and the sets are quantitatively analyzed to determine the length, width and maximum fluctuation of each disease area in the detection unit.

[0026] In the above scheme, the clustering process is specifically as follows:

[0027] a1. Randomly select an outlier point p from the current detection unit and use this point as the seed point to construct the set C k, all the unvisited outliers in the area with radius r and centered on p are classified into the set C k , mark point p as visited;

[0028] a2. Traverse all the outliers in the seed point area described in step 1 and repeat step 1 until no new outliers are included in C k , so the set C k The build is complete;

[0029] a3. Randomly select another outlier point from the remaining unvisited points as a new seed point, construct a new set, and repeat steps a1 and a2 until all points in the detection unit are visited. That is, all outliers are classified into corresponding sets according to the proximity relationship. All outliers in the current detection unit are respectively classified into sets {C1, C2, C3...}, where each outlier point set represents a diseased area.

[0030] In the above scheme, each set obtained by clustering is quantitatively analyzed to obtain the length, width and maximum fluctuation of each diseased area. Specifically: principal component analysis is performed on the aforementioned sets {C1, C2, C3...} to obtain the first principal component direction e1 and the second principal component direction e2. Each point in the set is projected onto the two principal component directions. The distance between the first and last projection points of e1 is the length L of the diseased area, and the distance between the first and last projection points of e2 is the width W of the diseased area. The deviation distance d determined by each point in the set is i，j The maximum value is the maximum fluctuation of the diseased area.

[0031] In the above scheme, in step (3), the two modes A and B are combined to detect abnormalities and changes in pavement diseases;

[0032] Mode A: Threshold judgment, that is, when the number of defects or the size of the defect area in the detection unit exceeds the normal allowable value, it is determined that the current detection unit has abnormal road surface defects, and the detection unit needs to be inspected, and the distance from the abnormal point to the mark is used to locate the defect position for targeted repair. Among them, the size of the defect area includes length, width, and maximum undulation;

[0033] Mode B: Benchmark template comparison. Specifically, first complete a preliminary scan of all inspection units in the entire inspection section. According to the number of defects in the inspection unit and the size of each defect area, perform the same processing on each inspection unit again during the subsequent actual inspection scan. Pair the same defect areas before and after the scan through the centroid position relationship, and compare the changes in length, width, maximum undulation and number of defects of the same defect area before and after. In this way, the changes of road surface defects on a time scale can be judged to facilitate corresponding maintenance decisions.

[0034] Beneficial effects:

[0035] The present invention is aimed at road surface disease detection and designs a unique line scanning detection method based on three-dimensional point cloud processing technology, including:

[0036] A method for defining the position of the road surface based on the linear point cloud was designed using the least squares method. The fitting straight line of the scanned section was solved in two steps to obtain the overall road surface equation after removing obvious bumps and accidental factors. A marked segmented detection mechanism was designed to divide the road section into several detection units for easy location of defects. The defect area in each detection unit was effectively evaluated by identifying abnormal road surface points and performing clustering and quantitative analysis. Two detection modes, threshold judgment and benchmark comparison, were designed to respectively identify abnormal and mutated road surface defects.

[0037] Compared with the existing intelligent detection methods for road surface diseases, the present invention has a simple layout, low computational complexity, good real-time performance, and high detection efficiency. It can accurately and comprehensively evaluate road surface diseases. It can not only determine the abnormal length, width, and fluctuation of the diseased area through the threshold method, but also accurately obtain the change in the disease before and after a period of time through the reference template comparison method. It can also accurately locate the location where the disease abnormality occurs, which is convenient for repair and maintenance. In addition, compared with the conventional image processing detection method based on the RGB color value of pixels, the present invention can avoid the influence of lighting conditions and color differences, and is expected to achieve more reliable detection in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the method of the present invention;

[0039] Figure 2 It is a schematic diagram of solving the two-step scanning cross-section fitting information of the present invention;

[0040] Figure 3 It is a schematic diagram of the abnormal point identification mechanism of the present invention;

[0041] Figure 4 It is a schematic diagram of the marker segmented detection mechanism of the present invention;

[0042] Figure 5 This is an illustration of the clustering of abnormal points of the present invention;

[0043] Figure 6 This is a diagram illustrating the quantitative analysis of the diseased area of ​​the present invention;

[0044] Figure 7 This is an illustration of an example of mode B of the present invention.

[0045] Description of Figure Numbers:

[0046] 1-carrier, 101-label reading device, 2-scanning device, 201-laser source, 301-label I, 302-label II, 303-label III. DETAILED DESCRIPTION

[0047] like Figure 1 As shown, the present invention proposes a road surface disease line scanning detection method, which mainly includes a carrier 1 and a scanning device 2.

[0048] The vehicle 1 is a conventional four-wheeled motor vehicle with a reading device 101 fixed at its front end. When the vehicle 1 passes by, the reading device 101 can read the information of the identification array (identification I301, identification 11302, identification III303...) arranged equidistantly on the shoulder of the road, thereby realizing the segmentation of the detected road surface, which is convenient for disease detection and positioning.

[0049] The scanning device 2 is also installed at the front end of the vehicle. The laser source 201 installed on the scanning device 2 projects a fan-shaped laser vertically downward to form a laser scanning surface. The scanning device 2 obtains the y and z coordinates of the dense points distributed on the "I" laser line along the width direction of the road surface, and their x coordinates are obtained by combining the real-time speed of the vehicle and the timestamp of each frame of linear point cloud data (the scanning device coordinate system is defined as: the vehicle running direction is x, the road width direction is v, and the vehicle vertical direction is z). In this way, the linear array three-dimensional point cloud data {P1{(x1, y 1，1 , z 1，1 ), (x1, y 1，2 , z 1，2 )…,(x1,y 1,n ,z l，n )}, P2{(x2, y 2，1 , z 2，1 ), (x2, y 2，2 , z 2，2 )…,(x2,y 2，n , z 2，n )}, …P i {(x i ,y i，1 , z i，1 ), (x i ,y i，2 , z i，2 )…,(x i ,y i，n , z i，n The vehicle speed can be obtained by detecting the wheel linear speed or rotation speed through a dedicated speed sensor, or can be directly obtained in real time by communicating with the instrument provided by the vehicle 1.

[0050] Based on the line scanning data acquisition mode, a two-step scanning section fitting information acquisition method is designed.

[0051] Step 1: If Figure 2As shown in (a), the least squares method zy straight line fitting is performed on all coordinate points in the linear array point cloud obtained by the current scan to obtain the fitting straight line

[0052] Step 2: Calculate the distance from each point to the fitting line If a point p i，j arrive If the distance is greater than the threshold T, it means that the point is an obvious pothole or bulge, then the point and several points in its neighborhood ( Figure 2 All points in the rectangular box shown in (a) are removed from the line array, such as Figure 2 As shown in (b), the remaining points are refitted with the zy straight line to obtain the fitting straight line that can represent the current scanning section. Ay+Bz+C=0. This straight line is the representation of the entire road surface in the point cloud coordinate system after removing the obvious bumps and bumps. Ay+Bz+C=0 represents the fitting straight line The equation Av+Bz+C=0 is always satisfied, where A, B, C represent the coefficients of the straight line equation, and z, y represent the vertical and horizontal coordinates in the road surface point cloud data.

[0053] Further, after obtaining the scan section fitting line corresponding to the current frame Afterwards, a road surface abnormal point recognition mechanism is proposed based on the characteristics of the acquired linear point cloud. i {(x i ,y i，1 , z i，1 ), (x i ,y i，2 , z i，2 )…,(x i ,y i，n , z i，n )} as an example. Figure 3 As shown, calculate all coordinate points p of the linear point cloud i，1 (x i ,y i，1 , z i，1 )~p i，n (x i ,y i，n , z i，n ) to the scanned section fitting straight line The distance, for example, p i，j (x i ,y i，j , z i,j )arrive The deviation distance is

[0054]

[0055] If d i，j ≥T 3σ , then p i，j is an outlier point. Threshold T 3σ According to the Laida criterion, the non-damaged road surface of the intended inspection section is sampled, and the sample standard deviation σ is solved after calculating the deviation distance of each sampling point according to formula (1). According to the statistical principle, in the case of no disease, d i，j <T 3σ The probability is 99.7%, that is, when d i，j ≥T 3σ When p i，j The probability of being misidentified as an abnormal point is only 0.3%. Based on this threshold method, the linear point cloud P of the current frame can be identified. i All abnormal points.

[0056] Furthermore, the present invention proposes a segmented detection mechanism that divides the detection section into several detection units and realizes accurate positioning of the road surface disease area. Figure 4 As shown, the signs I, II, III, etc. are arranged at equal intervals of Δx on the shoulder (i.e. Figure 1 The identification can be RFID electronic tags, bar codes, QR codes, color codes, etc. A identification reading device 101 is fixed on the carrier 1, and the signal receiving direction of the device is coplanar with the laser scanning surface. When 101 is facing the identification, the serial number corresponding to the identification can be read. The current reading time corresponds to the starting point of the current detection unit, and the next identification reading time is the end point of the detection unit. Assume that the reading time of a frame of linear point cloud data is at the time of reading identification I and reading identification II (t I and t II ), and the frame data is the mth frame after the identification I is read, and the reading time is t m The abnormality can be accurately located based on the real-time speed information:

[0057]

[0058] Where v m Indicates the real-time speed of vehicle 1 when acquiring the mth frame data, x m_I represents the x-distance from the outlier point to the marker I, v i It indicates the real-time speed of vehicle 1 when the i-th frame of linear point cloud data is obtained starting from reading identification 1.

[0059] Furthermore, based on the above segmentation mechanism combined with the above abnormal point identification mechanism (see Figure 3 ), all abnormal points of each detection unit can be identified, Figure 5(a) shows all the abnormal points in the detection units divided by markers I and II. Next, all the abnormal points in each detection unit are clustered, that is, the abnormal points belonging to different disease areas are classified into different sets.

[0060] The clustering process is as follows:

[0061] 1. Randomly select an outlier point p from the current detection unit and use this point as the seed point to construct the set C k , all the unvisited outliers in the area with radius r and centered on p are classified into the set C k , mark point p as visited; (such as Figure 5 As shown in (b), all outliers in the neighborhood centered on point p are classified into set C1).

[0062] 2. Traverse all the outliers in the seed point area described in step 1 and repeat step 1 until no new outliers are included in C k , so the set C k Construction completed; (such as Figure 5 As shown in (c), the set C1 is constructed).

[0063] 3. Randomly select another outlier point from the remaining unvisited points as a new seed point, construct a new set, and repeat steps 1 and 2 until all points in the detection unit are visited, that is, all outliers are classified into the corresponding set according to the neighbor relationship. Figure 5 As shown in (d), all abnormal points in the current detection unit are classified into sets (C1, C2, C3, ...), where each abnormal point set represents a diseased area.

[0064] Furthermore, quantitative analysis is performed on each clustering set to obtain the length, width and maximum fluctuation of each disease area. Figure 6 As shown in Figure 1, principal component analysis is performed on the aforementioned sets (C1, C2, C3, ...), thereby obtaining the first principal component direction e1 and the second principal component direction e2. Each point in the set is projected onto the two principal component directions. The distance between the first and last projection points of e1 is the length of the diseased area L, and the distance between the first and last projection points of e2 is the width of the diseased area W. The maximum deviation distance determined by formula (1) for each point in the set is the maximum fluctuation of the diseased area at that location.

[0065] Furthermore, based on the aforementioned quantitative analysis of the defects, the detection of abnormal and varied road defects is achieved through Mode A and Mode B:

[0066] Mode A: Perform the aforementioned clustering and quantitative analysis on the detection unit to determine the number of defects (the number of sets obtained by the aforementioned clustering process) and the size of the defect area (length, width, and maximum fluctuation), and use the threshold to determine whether the current detection unit is abnormal. If the number of defects in a certain detection unit exceeds the maximum allowable value, or the length, width, or maximum fluctuation of a certain defect area exceeds the allowable value, it means that the current detection unit has abnormal road surface defects, and the detection unit needs to be inspected, and the defect location is located in combination with the distance from the aforementioned abnormal point to the mark, and targeted repairs are carried out.

[0067] Mode B: To accurately and effectively judge abnormal changes in road surface defects, the changes in road surface defects before and after are determined by comparing with the pre-scanned benchmark. Specifically, when the road surface is completed or the road surface condition is in compliance, the vehicle 1 pre-scans the entire test section and uses the aforementioned defect area quantitative analysis and processing method to determine the size of each defect area in the test unit. In subsequent actual tests, each test unit is scanned again and processed in the same way. Figure 7 As shown, the detection unit divided by identification I and identification II is also used as an example to calculate the corresponding set C of each disease during actual detection and pre-scanning. k The centroid position (i.e. C k The average value of the three-dimensional coordinates of all coordinate points in the image). The same diseased area can be found by the proximity of the centroid position. The length, width, and maximum fluctuation of the same diseased area before and after are compared. If a certain value changes greatly before and after, it means that the corresponding disease has expanded greatly. For example, Figure 7 Compared with the pre-scan, the length L and width W of the diseased area C2 in the re-scan are significantly larger, and timely inspection and repair are required to prevent the disease from further deteriorating and causing hidden dangers to driving safety. In addition, the number of diseases in the previous and subsequent scans can also be compared through the aforementioned clustering process. If the number changes, it means that a new diseased area has been generated. Timely repair will also effectively control the disease at a smaller level and reduce the cost of repair and maintenance.

[0068] The above pavement defect detection line scanning method can selectively or simultaneously work in mode A and mode B: mode A is used to identify abnormal pavement defects, while mode B is used to detect abnormal pavement defects before and after.

[0069] The present invention discloses a line scanning pavement disease detection method based on three-dimensional point cloud, and its main technical effects and advantages are as follows:

[0070] Acquisition of 3D point cloud data: By installing a scanning device at the front end of the vehicle, the road surface point cloud data is acquired, including the y and z coordinates of the coordinate points, and the x coordinate is obtained by combining the vehicle speed and timestamp. This method can acquire 3D information of the road surface with accuracy, real-time and economy, providing a data basis for disease detection.

[0071] Two-step scanning section fitting: The point cloud data is fitted with a straight line using the least squares method, and obvious concave and convex points are removed in two steps to obtain a more accurate road surface fitting straight line. This method can reduce the impact of accidental factors on the test results and improve the accuracy of the test.

[0072] Abnormal point identification: By calculating the distance from each point in the point cloud to the fitting line and comparing it with the threshold, abnormal points can be intelligently identified. This method can effectively identify the diseased area of ​​the road surface.

[0073] Marking segmented detection mechanism: By reading the markings on the shoulder, the road surface is divided into multiple detection units to achieve segmented detection. This method can accurately locate the location of the diseased area, facilitating subsequent repairs and maintenance.

[0074] Quantitative analysis of the diseased area: cluster the abnormal points to obtain a set of different diseased areas, and then quantify the size of the diseased area through principal component analysis. This method can provide detailed information on the diseased area and support maintenance decisions.

[0075] Disease detection mode: Combines the threshold discrimination method and the benchmark template comparison method to detect abnormal and abnormal pavement diseases. This method can comprehensively evaluate the health of the pavement and monitor the development trend of the disease.

[0076] In summary, the present invention provides an efficient and accurate pavement disease detection method, which can timely discover and evaluate pavement diseases, provide important reference for road maintenance, thereby increasing road life, reducing maintenance costs, and ensuring driving safety.

Claims

1. A road surface disease line scanning detection method, characterized in that: include: Vehicle 1: A motor vehicle, with a sign reading device (101) fixedly mounted on its front end for reading signs arranged at equal intervals on the road shoulder; Scanning device 2: installed at the front end of the carrier (1), the laser source (201) thereon projects laser light vertically downward to form a laser scanning surface; The scanning device (2) acquires the y and z coordinates of the dense points distributed on the "I" laser line along the width direction of the road surface, and the x coordinate is obtained by combining the real-time speed of the vehicle (1) and the timestamp of each frame of the linear point cloud data, thereby acquiring linear three-dimensional point cloud data representing road surface information in a line scanning manner; The detection steps are: (1) performing least squares linear fitting on the three-dimensional point cloud data of the linear array to obtain a fitting straight line for the scanned section, and identifying abnormal points representing the diseased area according to the deviation distance between each coordinate point on the linear array and the fitting straight line; (2) reading the identification through the reading device (101) to realize segmented detection, clustering and quantitative analysis of the abnormal points in each detection unit obtained by the division, determining the size of the diseased area, and determining the number of diseases according to the number of sets obtained by clustering processing; (3) Detect pavement damage anomalies and changes through the threshold method and the benchmark template comparison method.

2. The pavement defect line scanning detection method according to claim 1, characterized in that: In step (1), the road surface fitting straight line is solved in two steps: The first step is to use all the coordinate points of each frame of the linear point cloud as a set to perform zv straight line fitting based on the least squares method to obtain the fitting line The second step is to calculate the coordinate points to the fitting line If the distance is greater than a certain threshold, the point and its neighboring points are filtered out, and the remaining points are refitted with a zy straight line to obtain a fitting straight line representing the current scanning section. Determine the representation of the overall road surface in the point cloud coordinate system after removing the obvious bumpy accidental factors, where: Ay+Bz+C=0 represents the fitting straight line The equation Ay+Bz+C=0 is always satisfied, where A, B, C represent the coefficients of the straight line equation, and z and y represent the vertical and horizontal coordinates in the road surface point cloud data.

3. The pavement defect line scanning detection method according to claim 2, characterized in that: In step (1), a straight line is fitted based on the deviation of the coordinate points from the scanned section. Identify abnormal points on the road surface by size: When a coordinate point p in the linear point cloud i,j (x i ,y i，j , z i,j ) to the scan section fitting line When the distance exceeds the deviation threshold, the coordinate point is considered to be a road surface disease abnormal point: p i，j (x i ,y i,j , z i，j )arrive The deviation distance is: If d i，j ≥T 3σ , then p i，j is an abnormal point, according to the deviation threshold T 3σ Identify the current frame line point cloud P i All abnormal points.

4. The road surface disease line scanning detection method according to claim 3, characterized in that: Deviation threshold T 3σ Determined by the Raida criterion: Sample the road surface without disease in the section to be tested, calculate the distance from the coordinate point to the fitting straight line of the scan section and calculate the standard deviation σ, and the deviation threshold T 3σ It is 3 times the standard deviation σ.

5. The pavement defect line scanning detection method according to claim 1, characterized in that: In step (2), a marker segmented detection mechanism is used to determine the location of the abnormal point: by reading the serial number corresponding to the marker through the marker reading device (101) fixed on the vehicle (1), it can be determined between which two markers the current detection position is located, and then combined with the abnormal frame and its corresponding real-time speed information, the distance from the abnormal position to the marker can be accurately determined. Specifically, the markers are arranged at equal intervals of Δx on the shoulder of the road. When the reading device (101) is facing the marker, the serial number corresponding to the marker is read, and the specific location where the abnormality occurs is found according to the serial number; Anomaly detection at time t m , and the time t m At the time t of reading the identification I (301) and reading the identification II (302) I and t II If tx is between the time t of reading the mark II (302) and reading the mark III (303), then it can be known that the anomaly must occur between the mark I (301) and the mark II (302). II and t III If the abnormal frame appears between the markers s and s+1, then it can be known that the abnormality must appear between the markers II (302) and III (303), and so on; the abnormal frame appears between the markers s and s+1, where s = I, II, III, ..., from t s The calculation starts when the mark s is read at time t. If the mth frame linear array data is abnormal, the abnormal identification time t m Corresponding to the mth frame of linear point cloud data after the identification s is read, the abnormal position can be accurately located according to the real-time speed information: Where u m represents the real-time speed of vehicle (1) when acquiring the mth frame data, t1 and v1 represent the time of the first frame from reading the tag s and the real-time speed of vehicle 1, respectively, m_s represents the x-distance from the outlier point to the marker s, v i It indicates the real-time speed of the vehicle 1 when the i-th frame of linear point cloud data is obtained starting from the reading of the identifier s.

6. The pavement defect line scanning detection method according to any one of claims 1, 3 or 5, characterized in that: In step (2), the abnormal points in each detection unit divided by the mark are clustered, and the abnormal points belonging to different disease areas are classified into different sets, and the sets are quantitatively analyzed to determine the length, width and maximum fluctuation of each disease area in the detection unit.

7. The pavement defect line scanning detection method according to claim 6, characterized in that: The clustering process is as follows: a1. Randomly select an outlier point p from the current detection unit and use this point as the seed point to construct the set C k , all the unvisited outliers in the area with radius r and centered on p are classified into the set C k , mark point p as visited; a2. Traverse all the outliers in the seed point area described in step 1 and repeat step 1 until no new outliers are included in C k , so the set C k Build completed; a3. Randomly select another outlier point from the remaining unvisited points as a new seed point, construct a new set, and repeat steps a1 and a2 until all points in the detection unit are visited. That is, all outliers are classified into corresponding sets according to the proximity relationship. All outliers in the current detection unit are respectively classified into sets {C1, C2, C3...}, where each outlier point set represents a diseased area.

8. The road surface disease line scanning detection method according to claim 7, characterized in that: A quantitative analysis is performed on each set obtained by clustering to obtain the length, width and maximum fluctuation of each diseased area. Specifically: principal component analysis is performed on the aforementioned sets {C1, C2, C2...} to obtain the first principal component direction e1 and the second principal component direction e2. Each point in the set is projected onto the two principal component directions. The distance between the first and last projection points of e1 is the length L of the diseased area, and the distance between the first and last projection points of e2 is the width W of the diseased area. The deviation distance d determined by each point in the set is i，j The maximum value is the maximum fluctuation of the diseased area.

9. The road surface disease line scanning detection method according to any one of claims 1, 5, or 8, characterized in that: In step (3), the A and B modes are combined to detect abnormalities and changes in pavement damage; Mode A: Threshold judgment, that is, when the number of defects or the size of the defect area in the detection unit exceeds the normal allowable value, it is determined that the current detection unit has abnormal road surface defects, and the detection unit needs to be inspected, and the defect position is located based on the distance from the abnormal point to the mark for targeted repair. The size of the defect area includes length, width, and maximum undulation; Mode B: Benchmark template comparison. Specifically, first complete a preliminary scan of all inspection units in the entire inspection section. According to the number of defects in the inspection unit and the size of each defect area, perform the same processing on each inspection unit again during the subsequent actual inspection scan. Pair the same defect areas before and after the scan through the centroid position relationship, and compare the changes in length, width, maximum undulation and number of defects of the same defect area before and after. In this way, the changes of road surface defects on a time scale can be judged to facilitate corresponding maintenance decisions.

Images