Highway pavement quality online detection method and device

Abstract

The invention relates to the technical field of image processing, in particular to a highway pavement quality online detection method and device, and the method comprises the steps: obtaining point cloud data of a highway pavement at each moment in a preset time period; determining the change degree of each point cloud data at each moment in a predetermined time period; determining the shielding possibility of the current point cloud data at each moment in a preset time period; when the shielding possibility is greater than a first threshold value, determining that the current point cloud data is shielded, and when the shielding possibility is not greater than the first threshold value, determining the defect probability of the current point cloud data according to the pavement continuity of the current point cloud data and the shielding possibility of the current point cloud data at each moment within a predetermined time period; and detecting the quality of the position of the highway pavement corresponding to the current point cloud data according to the defect probability. According to the invention, the accuracy of the obtained point cloud data of the highway pavement is improved, and the detection precision of the highway pavement quality is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an online detection method and device for highway pavement quality. Background Art

[0002] Highway pavement quality refers to the physical condition of the road surface and its ability to meet traffic needs. It directly affects driving safety, comfort and efficiency. Good pavement quality can not only extend the service life of the road, but also reduce vehicle maintenance costs and improve transportation efficiency. With the increase in the number of vehicles and changes in transportation needs, the damage and deterioration of highway pavements have become more serious, which not only affects the smoothness of traffic, but may also cause traffic accidents. Uneven pavement, cracks, potholes and other problems may cause vehicles to lose control and increase the risk of traffic accidents. Therefore, it is necessary to monitor the quality of highway pavements and maintain them in a timely manner.

[0003] In some scenarios, the monitoring of road pavement quality mainly relies on manual observation, which is slow and prone to missed detection. In addition, the detection results are highly subjective, making it difficult to achieve large-scale, real-time, and efficient detection. Therefore, the road pavement can be monitored online by acquiring point cloud data of the road pavement in real time. However, for the road pavement, there may be obstacles and objects such as moving vehicles on the road surface, and the acquisition of point cloud data will be affected by the objects on the road pavement, resulting in inaccurate point cloud data, that is, the acquired point cloud data may not be the point cloud data of the road pavement itself. Therefore, the accuracy of the acquired point cloud data of the road pavement is low, which in turn leads to low detection accuracy of the road pavement quality. Summary of the invention

[0004] In order to solve the technical problem of low detection accuracy of highway pavement quality, the purpose of the present invention is to provide a method and device for online detection of highway pavement quality. The technical solution adopted is as follows: In a first aspect, an embodiment of the present invention provides an online detection method for highway pavement quality, comprising: acquiring point cloud data of a highway pavement at each moment within a predetermined time period; determining the degree of change of each point cloud data at each moment within the predetermined time period based on the height and grayscale value in the point cloud data at each moment within the predetermined time period; determining the relative change speed of the current point cloud data at each moment within the predetermined time period and the speed of a vehicle-mounted radar that collects the current point cloud data; determining the occlusion possibility of the current point cloud data at each moment within the predetermined time period based on the degree of change, relative change speed and speed of the current point cloud data at each moment within the predetermined time period; when the occlusion possibility is greater than a first threshold, determining that the current point cloud data is occluded, and re-collecting the point cloud data at the location where the current point cloud data is located, and when the occlusion possibility is not greater than the first threshold, determining the defect probability of the current point cloud data based on the road surface continuity of the current point cloud data and the occlusion possibility of the current point cloud data at each moment within the predetermined time period; and detecting the quality of the highway pavement location corresponding to the current point cloud data based on the defect probability.

[0005] Optionally, determining the degree of change of each point cloud data at each moment in a predetermined time period based on the height and grayscale value in the point cloud data at each moment in a predetermined time period includes: determining the grayscale expression value of the current point cloud data based on the first grayscale value mean of all point cloud data in the neighborhood of the current point cloud data and the second grayscale value mean of all point cloud data in the neighborhood of other point cloud data; determining the height difference parameter of the current point cloud data based on the first height of the current point cloud data and the second height of adjacent point cloud data adjacent to the current point cloud data; determining the third height of the point cloud data with the largest grayscale expression value; determining the road surface continuity of the current point cloud data based on the height difference parameter, grayscale expression value, third height and first height of the current point cloud data; determining the degree of change of the current point cloud data at each moment in the predetermined time period based on the road surface continuity of the current point cloud data at each moment in the predetermined time period, the first duration of the maximum height of the current point cloud data in the predetermined time period and the second duration of the minimum height of the current point cloud data in the predetermined time period.

[0006] Optionally, determining the grayscale representation value of the current point cloud data based on the first grayscale value mean of all point cloud data in the neighborhood of the current point cloud data and the second grayscale value mean of all point cloud data in the neighborhood of other point cloud data includes: calculating the absolute value of the first difference between the first grayscale value mean and the second grayscale value mean of other point cloud data, and superimposing the absolute values ​​of each first difference to obtain a first superimposed value; and performing inverse proportional normalization processing on the first superimposed value to obtain the grayscale representation value of the current point cloud data.

[0007] Optionally, determining the height difference parameters of the current point cloud data based on the first height of the current point cloud data and the second height of the adjacent point cloud data adjacent to the current point cloud data includes: calculating the second difference between the first height of the current point cloud data and the second height of the previous adjacent point cloud data adjacent to the current point cloud data, and the third difference between the first height of the current point cloud data and the second height of the next adjacent point cloud data adjacent to the current point cloud data; calculating the fourth difference between the second difference and the third difference, and performing inverse proportional normalization processing on the fourth difference to obtain the height difference parameters of the current point cloud data.

[0008] Optionally, determining the road surface continuity of the current point cloud data based on the height difference parameter, grayscale expression value, third height and first height of the current point cloud data includes: calculating the absolute value of the fifth difference between the third height and the first height, and the first sum of the absolute value of the fifth difference and a predetermined value; calculating the first ratio of the height difference parameter to the first sum; and determining the first product between the grayscale expression value and the first ratio as the road surface continuity of the current point cloud data.

[0009] Optionally, based on the road surface continuity of the current point cloud data at each moment within a predetermined time period, the first duration of the maximum height of the current point cloud data within the predetermined time period, and the second duration of the minimum height of the current point cloud data within the predetermined time period, determining the degree of change of the current point cloud data at each moment within the predetermined time period includes: calculating the average continuity of the road surface continuity of the current point cloud data at each moment within the predetermined time period, the second ratio between the average continuity and the road surface continuity of the current point cloud data at each moment within the predetermined time period, and the sixth difference between the predetermined value and the second ratio; inversely normalizing the sixth difference to obtain a normalized value; calculating the third ratio between the second duration and the first duration; and determining the second product between the normalized value and the third ratio as the degree of change of the current point cloud data at each moment within the predetermined time period.

[0010] Optionally, determining the occlusion possibility of the current point cloud data at each moment in the predetermined time period based on the change degree, relative change speed and speed of the current point cloud data at each moment in the predetermined time period includes: calculating the seventh difference between the relative change speed and the speed, and rounding the seventh difference up to an integer value; determining the third product between the change degree of the current point cloud data at each moment in the predetermined time period and the integer value as the occlusion possibility of the current point cloud data at each moment in the predetermined time period.

[0011] Optionally, determining the defect probability of current point cloud data based on the road surface continuity of the current point cloud data and the occlusion possibility of the current point cloud data at each moment within a predetermined time period includes: superimposing the occlusion possibility of the current point cloud data at each moment within the predetermined time period to obtain a second superposition value; calculating a second sum between the second superposition value and the road surface continuity of the current point cloud data; and performing inverse proportional normalization processing on the second sum to obtain the defect probability of the current point cloud data.

[0012] Optionally, detecting the quality of the position of the highway road surface corresponding to the current point cloud data according to the defect probability includes: when the defect probability is greater than a second threshold, determining that a road surface defect occurs at the position of the highway road surface corresponding to the current point cloud data.

[0013] In a second aspect, an embodiment of the present invention provides an online detection device for highway pavement quality, comprising: a processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; and the processor is used to execute the program stored in the memory to implement the steps of the online detection method for highway pavement quality mentioned in the first aspect.

[0014] The present invention has the following beneficial effects: firstly, the point cloud data of the road surface at each moment within a predetermined time period is acquired; then, the degree of change of each point cloud data at each moment within the predetermined time period is determined according to the height and grayscale value in the point cloud data at each moment within the predetermined time period; then, the relative change speed of the current point cloud data at each moment within the predetermined time period and the speed of the vehicle-mounted radar that collects the current point cloud data are determined; secondly, the occlusion possibility of the current point cloud data at each moment within the predetermined time period is determined according to the degree of change, relative change speed and speed of the current point cloud data at each moment within the predetermined time period; when the occlusion possibility is greater than a first threshold value, it is determined that the current point cloud data is occluded, and the point cloud data at the location of the current point cloud data is re-collected; when the occlusion possibility is not greater than the first threshold value, the defect probability of the current point cloud data is determined according to the road surface continuity of the current point cloud data and the occlusion possibility of the current point cloud data at each moment within the predetermined time period; finally, the quality of the position of the road surface corresponding to the current point cloud data is detected according to the defect probability.

[0015] In this way, the embodiment of the present invention obtains point cloud data of a road surface and analyzes the performance characteristics of the point cloud data on the road surface. According to the changes in the point cloud data at different times, the influence of vehicles passing by on the road surface under different traffic conditions on the point cloud data is analyzed to obtain the possibility that each point cloud data belongs to the road surface point cloud data, that is, the occlusion possibility of each point cloud data at each time is obtained. When the occlusion possibility is greater than the first threshold, it is determined that the current point cloud data is occluded, indicating that the point cloud data may not be the point cloud data of the road surface. When the occlusion possibility is not greater than the first threshold, it indicates that the point cloud data is the point cloud data of the road surface. Thus, the defect probability that a defect may occur at the position corresponding to the point cloud data is calculated using the effective point cloud data, and the road surface quality detection is performed based on the defect probability. In this way, the present invention improves the accuracy of the acquired point cloud data of the road surface, and then improves the detection accuracy of the road surface quality, and can accurately identify road surface defects and flatness problems, and eliminate the interference of other influencing factors on the road surface quality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or 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. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of a method for online detection of road pavement quality provided by an embodiment of the present invention; Figure 2 A schematic structural diagram of a highway pavement quality online detection device provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of an online detection device for highway pavement quality provided in another embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a method and device for online detection of road pavement quality proposed by the present invention, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of the on-line detection method and device for road pavement quality provided by the present invention is described in detail below in conjunction with the accompanying drawings.

[0021] Embodiment 1: See also Figure 1 , which shows a flow chart of a method for online detection of road pavement quality provided by an embodiment of the present invention, comprising: S101, obtaining point cloud data of a highway surface at each time within a predetermined time period.

[0022] Specifically, the embodiment of the present invention can use a vehicle-mounted laser radar to obtain point cloud data on the road surface. Among them, point cloud data (Point Cloud Data) is a collection composed of a large number of three-dimensional coordinate points, each point represents a measured position on the surface of an object, and each point contains x, y, z coordinate information, grayscale value and other information, which defines the position and color characteristics of the point in three-dimensional space. Laser radar is a common device for obtaining point cloud data. It can work under different lighting and weather conditions, is suitable for long-distance scanning, and can cover a large area. By emitting a laser beam and measuring the time required for it to reflect back, the point cloud data at each moment is obtained, thereby generating three-dimensional point cloud data, generating a road height model, recording the precise height information of different points, and obtaining the grayscale value of each point cloud data, and three-dimensional modeling of all point cloud data.

[0023] S102, determining the degree of change of each point cloud data at each moment in the predetermined time period according to the height and grayscale value in the point cloud data at each moment in the predetermined time period.

[0024] Specifically, since highway pavement is usually paved with asphalt, its surface color is close to gray and the color is relatively uniform. The large area of ​​gray tones on the highway pavement has a consistent color and texture. This uniformity of the asphalt surface forms a clear contrast with the roadside buildings, green belts, traffic signs, or other road objects. These objects on the highway pavement usually have different colors, textures or materials. Therefore, when collecting point cloud data, it will cause obvious differences from the point cloud data on the asphalt pavement. The uniform surface of asphalt often makes the grayscale values ​​of the point cloud data closer and more stable in the area, making the point cloud data of the highway pavement visually present a more uniform distribution. Therefore, it is possible to determine whether the point cloud data is valid point cloud data for the highway pavement by the degree of change of each point cloud data at different times.

[0025] Further, when determining the degree of change, as an optional embodiment of the present invention, the grayscale expression value of the current point cloud data is first determined based on the first grayscale value mean of all point cloud data in the neighborhood of the current point cloud data and the second grayscale value mean of all point cloud data in the neighborhood of other point cloud data; then the height difference parameter of the current point cloud data is determined based on the first height of the current point cloud data and the second height of the adjacent point cloud data adjacent to the current point cloud data; and the third height of the point cloud data with the largest grayscale expression value is determined; the road surface continuity of the current point cloud data is determined based on the height difference parameter, grayscale expression value, third height and first height of the current point cloud data; finally, based on the road surface continuity of the current point cloud data at each moment within the predetermined time period, the first duration of the maximum height of the current point cloud data within the predetermined time period and the second duration of the minimum height of the current point cloud data within the predetermined time period, the degree of change of the current point cloud data at each moment within the predetermined time period is determined.

[0026] Specifically, the neighborhood of the point cloud data may be an area where eight point cloud data are located around the point cloud data. Of course, according to actual conditions, the neighborhood range of the point cloud data may also be in other forms, which are not limited in the embodiments of the present invention. Because of the possible influence of sunlight, the direction of the highway extension is used as the direction of analysis. At the current moment, the grayscale expression value of the i-th point cloud data is calculated in the following manner: first, the absolute value of the first difference between the first grayscale value mean and the second grayscale value mean of other point cloud data is calculated, and the absolute values ​​of each first difference are superimposed to obtain a first superposition value; then the first superposition value is inversely normalized to obtain the grayscale expression value of the current point cloud data.

[0027] The embodiment of the present invention uses the following formula to calculate the grayscale representation value of the current point cloud data: ; In the above formula, Represents the grayscale representation value of the i-th point cloud data. Represents the mean of the grayscale values ​​of all point cloud data in the neighborhood of the i-th point cloud data, that is, the first grayscale value mean. Represents the mean of the second grayscale values ​​of all point cloud data in the neighborhood of the i+jth point cloud data. Indicates the number of other point cloud data. It represents the difference in grayscale value between the surrounding neighborhood of the i-th point cloud data and the neighborhood of other point cloud data in the extension direction of the highway. The smaller the difference, the more likely it is that the point cloud data has the grayscale feature of the road surface.

[0028] Furthermore, since the surface of the asphalt pavement is relatively flat, the spatial distribution of the point cloud data is usually relatively regular. The point cloud data scanned by the laser radar usually shows a small height change. Unlike the asphalt pavement, there are usually steps, road signs, curbs or other structures on the roadside, and the height of these objects is significantly higher than the surrounding road surface. In the laser radar scanning, when these steps or objects are scanned, the height value of their point cloud data usually shows a large mutation or contrast. For example, the height of the curb is usually higher than the asphalt pavement, while the edge of the step or road facility may show a sharp height change, generating obvious discrete point cloud data. For the point cloud data on the asphalt pavement, it usually shows a relatively continuous and regular height change. Since the surface of the asphalt is smooth and flat, the point cloud data scanned by the laser radar has a small height difference when distributed on the road surface, and the height of each point cloud data shows a certain continuity with the change of the surrounding point cloud data. Therefore, the embodiment of the present invention first obtains the continuity of each point cloud data on the road surface according to the grayscale and height expression.

[0029] Furthermore, when obtaining the height difference parameters between point cloud data, the embodiment of the present invention, as an optional embodiment of the present invention, first calculates the second difference between the first height of the current point cloud data and the second height of the previous adjacent point cloud data adjacent to the current point cloud data, and the third difference between the first height of the current point cloud data and the second height of the next adjacent point cloud data adjacent to the current point cloud data; then calculates the fourth difference between the second difference and the third difference, and performs inverse proportional normalization processing on the fourth difference to obtain the height difference parameters of the current point cloud data.

[0030] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the height difference parameter of the current point cloud data: ; In the above formula, Represents the height difference parameter of the i-th point cloud data. Indicates the first height of the i-th point cloud data. It represents the second height of the (i+1)th point cloud data adjacent to the (i)th point cloud data, that is, the second height of the next adjacent point cloud data adjacent to the (i)th point cloud data. It represents the second height of the i-1th point cloud data adjacent to the i-th point cloud data, that is, the second height of the previous adjacent point cloud data adjacent to the i-th point cloud data. (-) indicates the inverse normalization function, which is used to Perform inverse normalization. It indicates the difference between adjacent point cloud data. The smaller the difference, the higher the continuity of the road surface.

[0031] Furthermore, when calculating the road surface continuity, as an optional embodiment of the present invention, the absolute value of the fifth difference between the third height and the first height, and the first sum of the absolute value of the fifth difference and a predetermined value are first calculated; then the first ratio of the height difference parameter to the first sum is calculated; finally, the first product between the grayscale representation value and the first ratio is determined to be the road surface continuity of the current point cloud data.

[0032] Specifically, the predetermined value may be 1. The embodiment of the present invention specifically uses the following formula to calculate the road surface continuity of the current point cloud data: ; In the above formula, Indicates the road surface continuity of the i-th point cloud data. Represents the grayscale representation value of the i-th point cloud data. Represents the height difference parameter of the i-th point cloud data. Indicates the first height of the i-th point cloud data. Indicates the third height of the point cloud data with the maximum grayscale value. It represents the height difference between the i-th point cloud data and the point cloud data with the largest grayscale expression value. Problems such as road surface defects are based on the road surface. In most point cloud data, the road surface accounts for the vast majority, and the grayscale expression values ​​of most point cloud data are almost the same because they are based on a plane. Therefore, the smaller the difference, the greater the road surface continuity of the i-th point cloud data.

[0033] Furthermore, when a vehicle is traveling on the road, its physical volume and position may block or interfere with a portion of the road surface, especially defects on the road surface, such as potholes, cracks, and damage. These blockages may cause the defects in the relevant area to be unable to be fully or accurately captured during the data collection process, affecting the integrity and quality of the point cloud data. Therefore, when performing road defect detection and analysis, once a vehicle is identified on the road surface, it may be necessary to suspend the collection process or re-collect data to ensure that the defects in the area blocked by the vehicle can be clearly recorded. In addition, the presence of the vehicle may also cause distortion of the scanned data, and the signal reflected by the vehicle surface may be confused with the reflected signal of the ground or other objects, thereby affecting the accuracy of the point cloud. The point cloud data affected by the road surface may be missing or misidentified as point cloud data on the vehicle. If the point cloud data changes at different times, it is likely that the impact of the passing or passing of the moving vehicle affects the collection of the point cloud data, and the road continuity will also be affected to a certain extent. Therefore, the embodiment of the present invention can determine that the point cloud data is affected by analyzing the degree of change of the point cloud data that may be blocked on the road surface.

[0034] Furthermore, when determining the degree of change of the current point cloud data at each moment within a predetermined time period, as an optional embodiment of the present invention, firstly, the average continuity of the road surface continuity of the current point cloud data at each moment within the predetermined time period, the second ratio between the average continuity and the road surface continuity of the current point cloud data at each moment within the predetermined time period, and the sixth difference between the predetermined value and the second ratio are calculated; then, the sixth difference is inversely normalized to obtain a normalized value, and the third ratio between the second duration and the first duration is calculated; finally, the second product between the normalized value and the third ratio is determined to be the degree of change of the current point cloud data at each moment within the predetermined time period.

[0035] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the degree of change: ; In the above formula, Indicates the degree of change of the i-th point cloud data at the t-th time within the predetermined period. Indicates the first duration of the maximum height of the i-th point cloud data within the predetermined time period at the t-th time. The second duration representing the minimum height of the i-th point cloud data within the predetermined time period at the t-th time. Indicates the road surface continuity of the i-th point cloud data at the t-th time within a predetermined period. It represents the mean value of the road surface continuity of the i-th point cloud data at the t-th time within the predetermined period, that is, the average continuity. It indicates the duration ratio of the minimum height to the maximum height within the predetermined period. Because the car is driving on the road, the height of the point cloud data that may cause errors will be higher than the height of the road. Under normal circumstances, the vehicle will pass quickly within the predetermined period, so the duration is short. Therefore, the larger the ratio, the shorter the duration, the more likely it is that the point cloud data changes caused by the passing of the vehicle, and the greater the degree of change. Indicates the change of road continuity. If a vehicle passes by, the road continuity will also change. The greater the difference with other continuity within the predetermined time period, the greater the degree of change of the i-th point cloud data at this time. (-) indicates the inverse normalization function, which is used to Perform inverse proportional normalization.

[0036] S103, determining the relative change speed of the current point cloud data at each moment in a predetermined time period and the speed of the vehicle-mounted radar that collects the current point cloud data.

[0037] Specifically, on the road, vehicles are common dynamic objects, which will have a certain impact on the collection of point cloud data. Since laser radar or other sensors usually obtain three-dimensional data of the surrounding environment by emitting laser beams and receiving reflected signals, the movement of the moving vehicles will cause the collected point cloud data to change, resulting in uneven distribution of the density of the point cloud data, changes in the position of the point cloud, and even abnormal points that are inconsistent with the surrounding environment. When a vehicle passes by, the reflection intensity of the point cloud data may increase or decrease, or the point cloud data from the vehicle and its attached objects, while the point cloud data obtained from the defects on the road surface will not change accordingly. As the on-board radar moves, the defects on the road surface are relatively moving in opposite directions to the on-board radar, so the relative speed of the defect point cloud data on the road surface is the same as the speed of the on-board radar, but other moving vehicles may be moving in the same direction or in opposite directions and have a certain speed, so the speed will not always be the same as the on-board radar. Therefore, the embodiment of the present invention calculates the possibility of occlusion at each moment by obtaining the relative change speed of the current point cloud data at each moment within a predetermined time period and the speed of the vehicle-mounted radar that collects the current point cloud data. The relative change speed refers to the speed of the vehicle causing the occlusion relative to the speed of the ground.

[0038] S104, determining the occlusion possibility of the current point cloud data at each moment in the predetermined time period according to the change degree, relative change speed and speed of the current point cloud data at each moment in the predetermined time period.

[0039] Specifically, when determining the possibility of occlusion of the current point cloud data at each moment within a predetermined time period, as an optional embodiment of the present invention, firstly, the seventh difference between the relative change speed and the speed is calculated, and the seventh difference is rounded up to obtain an integer value; then, the third product between the degree of change of the current point cloud data at each moment within the predetermined time period and the integer value is determined as the possibility of occlusion of the current point cloud data at each moment within the predetermined time period.

[0040] The embodiment of the present invention uses the following formula to calculate the occlusion possibility of the current point cloud data at each moment in a predetermined time period: ; In the above formula, Indicates the occlusion possibility of the i-th point cloud data at the t-th time within the predetermined period. Indicates the degree of change of the i-th point cloud data at the t-th time within the predetermined period. Indicates the relative change rate of the i-th point cloud data at the t-th time within the predetermined period. Indicates the speed of the vehicle-mounted radar. The greater the degree of change, the greater the possibility of occlusion of the i-th point cloud data at this time. Indicates the upward rounding of the difference between the relative change speed and the radar speed. The larger the difference, the greater the possibility that it is not a defect on the road surface.

[0041] S105, when the occlusion possibility is greater than the first threshold, determine that the current point cloud data is occluded, and re-collect the point cloud data at the location where the current point cloud data is located. When the occlusion possibility is not greater than the first threshold, determine the defect probability of the current point cloud data based on the road surface continuity of the current point cloud data and the occlusion possibility of the current point cloud data at each moment within a predetermined time period.

[0042] Specifically, road surface defects usually appear as irregularities or local breaks in point cloud data. If the road surface continuity of the same point cloud data is small and the occlusion probability at multiple times is low, this indicates that the point cloud data is discontinuous in space and lacks errors or incompleteness caused by perspective occlusion. Therefore, this type of point cloud data is likely to be caused by defects in the road surface. In order to ensure the accuracy and reliability of detection, these possible defect points need to be collected again to eliminate misjudgments caused by external factors such as occlusion.

[0043] Furthermore, within the time range that can be captured, the occlusion probability of the same point cloud data at each moment is counted, and a first threshold is set. When the occlusion probability is greater than the first threshold, it means that the point cloud data collected at this time may be occluded, and the position needs to be collected again. For other normal moments, the point cloud data at this time can better reflect the actual condition of the road surface and is suitable for further road quality detection and analysis. Among them, the first threshold can be determined according to the actual situation, and the value in the embodiment of the present invention is 0.8.

[0044] Furthermore, when determining the defect probability of the current point cloud data, as an optional embodiment of the present invention, the occlusion possibilities of the current point cloud data at each moment within a predetermined time period are first superimposed to obtain a second superposition value; then a second sum between the second superposition value and the road surface continuity of the current point cloud data is calculated; finally, the second sum is inversely normalized to obtain the defect probability of the current point cloud data.

[0045] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the road surface continuity of the current point cloud data: ; In the above formula, Represents the defect probability of the i-th point cloud data at the t-th time within the predetermined period. Indicates the occlusion possibility of the i-th point cloud data at the t-th time within the predetermined period. Indicates the road surface continuity of the i-th point cloud data. T indicates the number of moments in the predetermined period. If the road surface continuity of the same point cloud data is small and the possibility of occlusion at multiple moments is small, it means that the point cloud data is more likely to belong to a road surface defect, and the defect probability is greater. (-) indicates the inverse normalization function, which is used to Perform inverse proportional normalization.

[0046] S106, detecting the quality of the position of the highway road surface corresponding to the current point cloud data according to the defect probability.

[0047] Specifically, after the defect probability is calculated in the embodiment of the present invention, when the defect probability is greater than a second threshold, it is determined that a road surface defect occurs at the position of the highway road surface corresponding to the current point cloud data.

[0048] Among them, the embodiment of the present invention sets a second threshold value. If the defect probability of the location corresponding to the collected point cloud data is greater than the second threshold value, it means that there is a high possibility of road surface defects at this location, and the point cloud data is marked. If there are also marked point cloud data in the surrounding or extended direction area of ​​the point cloud data, further on-site inspection is required at this location. During the inspection, focus should be placed on observing whether there are cracks, potholes, unevenness or other road quality problems that may affect driving safety and comfort. For main roads or key transportation hubs with large traffic volume and frequent vehicle traffic, for sections where defects often occur, in addition to timely repairs, regular monitoring and maintenance should also be considered.

[0049] Furthermore, the second threshold may be determined according to actual conditions, and in the embodiment of the present invention, the value is 0.85.

[0050] The embodiment of the present invention obtains point cloud data of a road surface and analyzes the performance characteristics of the point cloud data on the road surface. According to the changes in the point cloud data at different times, the influence of vehicles passing by on the road surface under different traffic conditions on the point cloud data is analyzed to obtain the possibility that each point cloud data belongs to the road surface point cloud data, that is, the occlusion possibility of each point cloud data at each time is obtained. When the occlusion possibility is greater than the first threshold, it is determined that the current point cloud data is occluded, indicating that the point cloud data may not be the point cloud data of the road surface. When the occlusion possibility is not greater than the first threshold, it indicates that the point cloud data is the point cloud data of the road surface. Thus, the defect probability that a defect may occur at the position corresponding to the point cloud data is calculated using the effective point cloud data, and the road surface quality detection is performed based on the defect probability. In this way, the present invention improves the accuracy of the acquired point cloud data of the road surface, and then improves the detection accuracy of the road surface quality, and can accurately identify road surface defects and flatness problems, and eliminate the interference of other influencing factors on the road surface quality detection.

[0051] Embodiment 2: Based on the above-mentioned embodiment provided on-line detection method of road pavement quality, based on the same technical concept, the embodiment of the present invention also provides an on-line detection device of road pavement quality. Figure 2 A schematic diagram of a structure of an online detection device for road surface quality provided by an embodiment of the present invention is shown in FIG. Figure 2 The highway pavement quality online detection device 200 includes: an acquisition module 201, which is used to acquire the point cloud data of the highway pavement at each time in a predetermined time period; a determination module 202, which is used to determine the degree of change of each point cloud data at each time in the predetermined time period according to the height and grayscale value in the point cloud data at each time in the predetermined time period; the determination module 202 is also used to determine the relative change speed of the current point cloud data at each time in the predetermined time period and the speed of the vehicle-mounted radar that collects the current point cloud data; the determination module 202 is also used to determine the degree of change of the current point cloud data at each time in the predetermined time period, the relative change The speed and speed determine the occlusion possibility of the current point cloud data at each moment in the predetermined time period; the determination module 202 is also used to determine that the current point cloud data is occluded when the occlusion possibility is greater than the first threshold, and re-collect the point cloud data at the location where the current point cloud data is located. When the occlusion possibility is not greater than the first threshold, the defect probability of the current point cloud data is determined according to the road surface continuity of the current point cloud data and the occlusion possibility of the current point cloud data at each moment in the predetermined time period; the detection module 203 is used to detect the quality of the position of the highway road surface corresponding to the current point cloud data according to the defect probability.

[0052] The embodiment of the present invention obtains point cloud data of a road surface and analyzes the performance characteristics of the point cloud data on the road surface. According to the changes in the point cloud data at different times, the influence of vehicles passing by on the road surface under different traffic conditions on the point cloud data is analyzed to obtain the possibility that each point cloud data belongs to the road surface point cloud data, that is, the occlusion possibility of each point cloud data at each time is obtained. When the occlusion possibility is greater than the first threshold, it is determined that the current point cloud data is occluded, indicating that the point cloud data may not be the point cloud data of the road surface. When the occlusion possibility is not greater than the first threshold, it indicates that the point cloud data is the point cloud data of the road surface. Thus, the defect probability that a defect may occur at the position corresponding to the point cloud data is calculated using the effective point cloud data, and the road surface quality detection is performed based on the defect probability. In this way, the present invention improves the accuracy of the acquired point cloud data of the road surface, and then improves the detection accuracy of the road surface quality, and can accurately identify road surface defects and flatness problems, and eliminate the interference of other influencing factors on the road surface quality detection.

[0053] Embodiment three: Corresponding to the online road pavement quality detection method provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides an online road pavement quality detection device, which is used to execute the above-mentioned online road pavement quality detection method. Figure 3 A schematic diagram of a structure of an online detection device for road surface quality according to another embodiment of the present invention is shown in FIG. Figure 3 The highway pavement quality online detection device may have relatively large differences due to different configurations or performances, and may include one or more processors 301 and memory 302, the memory 302 is used to store computer programs that can be run on the processor 301, and the processor 301 is used to execute the program stored in the memory 302 to achieve the above Figure 1 The memory 302 may be a temporary storage or a permanent storage. The application stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the online detection device for road surface quality.

[0054] Furthermore, the processor 301 can be configured to communicate with the memory 302, and execute a series of computer executable instructions in the memory 302 on the highway pavement quality online detection device. The highway pavement quality online detection device can also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input and output interfaces 305, and one or more keyboards 306.

[0055] Specifically in this embodiment, the highway pavement quality online detection device includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to achieve the above Figure 1 The various steps in the method embodiment are similar to those in the method embodiment, and have the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0056] It should be noted that the online road pavement quality detection device provided in the embodiment of the present invention and the online road pavement quality detection method provided in the embodiment of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned online road pavement quality detection method, and has the same or similar beneficial effects, and the repeated parts will not be repeated.

[0057] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for online detection of road pavement quality, characterized in that: The highway pavement quality online detection method comprises: Obtaining point cloud data of the road surface at each time within a predetermined time period; Determine the degree of change of each point cloud data at each moment in the predetermined time period according to the height and grayscale value in the point cloud data at each moment in the predetermined time period; Determine the relative change speed of the current point cloud data at each moment in a predetermined time period and the speed of the vehicle-mounted radar that collects the current point cloud data; Determining the occlusion possibility of the current point cloud data at each moment in the predetermined time period according to the change degree of the current point cloud data at each moment in the predetermined time period, the relative change speed and the speed; When the occlusion possibility is greater than a first threshold, determine that the current point cloud data is occluded, and re-collect the point cloud data at the location where the current point cloud data is located; when the occlusion possibility is not greater than the first threshold, determine the defect probability of the current point cloud data according to the road surface continuity of the current point cloud data and the occlusion possibility of the current point cloud data at each moment within a predetermined time period; The quality of the position of the highway road surface corresponding to the current point cloud data is detected according to the defect probability.

2. The method for online detection of road pavement quality according to claim 1, characterized in that: Determining the degree of change of each point cloud data at each moment in the predetermined time period according to the height and grayscale value in the point cloud data at each moment in the predetermined time period includes: Determine the grayscale representation value of the current point cloud data according to the first grayscale value average of all point cloud data in the neighborhood of the current point cloud data and the second grayscale value average of all point cloud data in the neighborhood of other point cloud data; Determine a height difference parameter of the current point cloud data according to a first height of the current point cloud data and a second height of adjacent point cloud data adjacent to the current point cloud data; Determine the third height of the point cloud data with the largest grayscale representation value; Determine the road surface continuity of the current point cloud data according to the height difference parameter of the current point cloud data, the grayscale representation value, the third height, and the first height; Based on the road surface continuity of the current point cloud data at each moment within the predetermined time period, the first duration of the maximum height of the current point cloud data within the predetermined time period, and the second duration of the minimum height of the current point cloud data within the predetermined time period, the degree of change of the current point cloud data at each moment within the predetermined time period is determined.

3. The method for online detection of road pavement quality according to claim 2, characterized in that: Determining the grayscale representation value of the current point cloud data according to the first grayscale value mean of all point cloud data in the neighborhood of the current point cloud data and the second grayscale value mean of all point cloud data in the neighborhood of other point cloud data includes: Calculating an absolute value of a first difference between the first grayscale value mean and a second grayscale value mean of other point cloud data, and superimposing the absolute values ​​of the first differences to obtain a first superimposed value; The first superposition value is subjected to inverse proportional normalization processing to obtain a grayscale representation value of the current point cloud data.

4. The method for online detection of road surface quality according to claim 2, characterized in that: The step of determining the height difference parameter of the current point cloud data according to the first height of the current point cloud data and the second height of adjacent point cloud data adjacent to the current point cloud data comprises: Calculating a second difference between a first height of the current point cloud data and a second height of a previous adjacent point cloud data adjacent to the current point cloud data, and a third difference between the first height of the current point cloud data and a second height of a next adjacent point cloud data adjacent to the current point cloud data; A fourth difference between the second difference and the third difference is calculated, and an inverse proportional normalization process is performed on the fourth difference to obtain a height difference parameter of the current point cloud data.

5. The method for online detection of road pavement quality according to claim 2, characterized in that: Determining the road surface continuity of the current point cloud data according to the height difference parameter of the current point cloud data, the grayscale representation value, the third height, and the first height includes: calculating an absolute value of a fifth difference between the third height and the first height, and a first sum of the absolute value of the fifth difference and a predetermined value; Calculating a first ratio of the height difference parameter to the first sum value; A first product between the grayscale representation value and the first ratio is determined as the road surface continuity of the current point cloud data.

6. The method for online detection of road surface quality according to claim 2, characterized in that: Determining the degree of change of the current point cloud data at each moment in the predetermined period based on the road surface continuity of the current point cloud data at each moment in the predetermined period, the first duration of the maximum height of the current point cloud data at the predetermined period, and the second duration of the minimum height of the current point cloud data at the predetermined period includes: Calculating an average continuity of road surface continuity of the current point cloud data at each moment within a predetermined period, a second ratio between the average continuity and the road surface continuity of the current point cloud data at each moment within the predetermined period, and a sixth difference between a predetermined value and the second ratio; Performing inverse proportional normalization on the sixth difference to obtain a normalized value; calculating a third ratio between the second duration and the first duration; A second product between the normalized value and the third ratio is determined as a change degree of the current point cloud data at each moment within a predetermined time period.

7. The method for online detection of road pavement quality according to claim 1, characterized in that: Determining the possibility of occlusion of the current point cloud data at each moment in the predetermined period of time according to the change degree of the current point cloud data at each moment in the predetermined period of time, the relative change speed and the speed includes: Calculating a seventh difference between the relative change speed and the speed, and rounding up the seventh difference to obtain an integer value; Determine a third product between the degree of change of the current point cloud data at each moment in the predetermined time period and the integer value as the occlusion possibility of the current point cloud data at each moment in the predetermined time period.

8. The method for online detection of road pavement quality according to claim 1, characterized in that: Determining the defect probability of the current point cloud data according to the road surface continuity of the current point cloud data and the occlusion possibility of the current point cloud data at each moment within a predetermined period of time includes: Superimposing the occlusion possibilities of the current point cloud data at each time within a predetermined time period to obtain a second superposition value; Calculating a second sum value between the second superposition value and the road surface continuity of the current point cloud data; The second sum is subjected to inverse proportional normalization processing to obtain the defect probability of the current point cloud data.

9. The method for online detection of road pavement quality according to claim 1, characterized in that: The detecting the quality of the position of the road surface corresponding to the current point cloud data according to the defect probability comprises: When the defect probability is greater than a second threshold, it is determined that a road surface defect occurs at the position of the highway road surface corresponding to the current point cloud data.

10. An on-line detection device for road surface quality, characterized in that: include: A processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; The processor is used to execute the program stored in the memory to implement the steps of the online detection method of highway pavement quality as described in any one of claims 1 to 9.

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