CN116612400B - Road management method and system based on road flatness - Google Patents
Road management method and system based on road flatness Download PDFInfo
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Abstract
The invention discloses a road management method and a road management system based on road flatness, and belongs to the technical field of road maintenance, wherein the method comprises the steps of dividing a road into different areas and acquiring image information of different angles and heights of the road in the different areas; image integration is carried out based on image information of roads in different areas at different angles and heights, and initial fluctuation images of road surfaces in different areas are obtained; determining road flatness change speeds of different areas according to a road flatness change prediction model, predicting and drawing change fluctuation images of road surfaces in different areas and in different time periods; predicting the pit depth and the pit distribution density of the highway according to the change fluctuation images of the highway pavement in different time periods in different areas; determining pit maintenance time of the highway based on pit depths of the highway in different time periods in each section of area; based on pit distribution density of roads in different time periods in each section of area, the regional speed limit of the roads is changed in real time.
Description
Technical Field
The invention relates to the technical field of road maintenance, in particular to a road management method and system based on road flatness.
Background
In urban highway maintenance, highway pits are one of the most common road surface defects, which are formed due to poor road construction quality or excessive rolling of illegal vehicles. The highway pit slot not only affects the attractiveness of the city, but also affects the running of vehicles and even increases the occurrence probability of traffic accidents.
In the prior art, aiming at the identification and maintenance of the defects of the pit and the groove of the highway, the response and repair of the pit and the groove road surface in a short period cannot be achieved mainly by means of the heat reflection of citizens or the highway inspection of related departments. And when vehicles running on the highway are more, the highway is not convenient for workers to patrol, and pits on the highway are easy to miss. If the missing pit is gradually enlarged along with the increase of vehicles, the running of the vehicles is easily affected, and potential safety hazards are caused.
Therefore, how to provide a road management method, which can accurately monitor the road surface of the road in real time, more accurately evaluate the degree of the pit slot of the road, more reasonably plan the road maintenance process, and avoid the potential safety hazard caused by untimely discovery and treatment is a technical problem to be solved by the technicians in the field.
Disclosure of Invention
Therefore, the invention provides a road management method and a road management system based on road flatness, which are used for solving the problem of potential safety hazards caused by untimely discovery and treatment due to the fact that the road surface of a road cannot be accurately monitored in real time in the prior art.
In order to achieve the above object, the present invention provides the following technical solutions:
according to a first aspect of the present invention, there is provided a road management method based on road flatness, comprising the steps of:
s1: dividing a highway into different areas, and acquiring image information of different angles and heights of the highway in the different areas through an unmanned aerial vehicle;
s2: image integration is carried out based on image information of different angles and heights of the highway in different areas, and initial fluctuation images of the road surface in different areas are obtained;
s3: determining road flatness change speeds of different areas according to a road flatness change prediction model, predicting and drawing change fluctuation images of the road pavement in different areas and at different time periods;
s4: predicting the pit depths and the pit distribution densities of the highways in different time periods in different areas according to the change fluctuation images of the highways in different time periods in different areas;
s5: determining pit maintenance time of each section of the highway based on pit depths of the highway in different time periods in each section of the area;
s6: and changing the regional speed limit in each section of the highway in real time based on the pit distribution density of the highway in different time periods in each section of the region.
Further, the establishing process of the road flatness variation prediction model in the step S3 specifically includes the following steps:
s301: continuously acquiring the traffic flow, the vehicle type and the maximum load of the vehicle of the highway in a unit time period;
s302: the unmanned aerial vehicle continuously acquires at least a first change fluctuation image, a second change fluctuation image and a third change fluctuation image of the highway in a unit time period;
s303: and comparing the first variation fluctuation image, the second variation fluctuation image and the third variation fluctuation image, and determining the road flatness variation speed which varies along with the vehicle information in the step S301 in a unit time period according to the amplitude variation.
Further, the formula of the road flatness change speed is as follows:
v＝f 1 (a)+(f 2 (b)) i ；
wherein v is the change speed of road flatness, f 1 (a) A first preset function of the change speed of the road flatness, a is the traffic flow of the road in a unit time period, f 2 (b) And b is a second preset function of the change speed of the road flatness, b is the type of the vehicle passing through the road in a unit time period, and i is the maximum load of the vehicle corresponding to the type of the vehicle.
Further, the pit depth of the highway is the peak value of the change fluctuation image of the highway pavement, and the pit distribution density of the highway is the peak distribution interval of the change fluctuation image of the highway pavement.
Further, the step S5 specifically includes the following steps:
s501: predicting and acquiring variation fluctuation images of the road surface of the highway in continuous time periods, and continuously obtaining pit depths of the highway in different time periods;
s502: continuously comparing the pit depth of the highway with a pit depth threshold;
s503: if the pit depth of the highway is greater than the pit depth threshold, transmitting an alarm signal to a remote terminal to prompt a worker to maintain the highway in the area.
Further, the step S6 specifically includes the following steps:
s601: determining the number of pits and the pitch of the pits of the highway based on the change fluctuation image of the highway pavement;
s602: summing the pit spacing on the highway pavement and averaging to obtain the pit distribution density of the highway;
s603: predicting and acquiring variation fluctuation images of the road surface of the highway in continuous time periods, and continuously obtaining pit distribution densities of the highway in different time periods;
s604: if the pit distribution density of the highway pavement is increased by one percentage, the regional speed limit in each section of region of the highway is correspondingly reduced by ten percentages.
Further, the formula of the pit distribution density of the highway is as follows:
wherein ρ is the pit distribution density, h of the highway i For the ith pit space, n is the number of pits, and the value range of i is an integer of more than or equal to 1 and less than or equal to n-1.
Further, based on the change fluctuation images of the road surface in different time periods in different areas, the crack width of the road is also predicted and obtained, and crack maintenance is performed.
Further, in the step S2, the unmanned aerial vehicle travels to four corners of the highway in different areas according to different altitude routes, and the image information of the four corners is spliced to obtain initial fluctuation images of the road surface in different areas.
According to a second aspect of the present invention, there is provided a road management system based on road flatness, for implementing any one of the road management methods based on road flatness described above, comprising:
the information acquisition unit is used for acquiring image information of different angles and heights of the highway in different areas;
the information processing unit is used for processing the image information of the roads at different angles and heights in different areas, carrying out image integration and obtaining initial fluctuation images of the road surfaces in different areas;
the image prediction drawing unit is used for determining the road flatness change speed of different areas according to the road flatness change prediction model, and predicting and drawing the change fluctuation images of the road surface in different areas and in different time periods;
the image processing unit is used for obtaining pit depths and pit distribution densities of the highways in different time periods in different areas according to the change fluctuation images of the highway pavement in different time periods in different areas;
and the feedback unit is used for determining pit maintenance time of each section of area of the highway and real-time changing the area speed limit in each section of area of the highway.
The invention has the following advantages:
according to the invention, the highway is divided into different areas, and the unmanned aerial vehicle is used for acquiring image information of different angles and heights of the highway in the different areas. And (3) carrying out image integration based on image information of different angles and heights of the roads in different areas, and acquiring initial fluctuation images of road surfaces in different areas. And determining the road flatness change speed of different areas according to the road flatness change prediction model, and predicting and drawing change fluctuation images of road surfaces in different areas and in different time periods.
And predicting the pit depths and the pit distribution densities of the highways in different time periods in different areas according to the change fluctuation images of the highways in different time periods in different areas. And determining the pit maintenance time of each section of the highway based on the pit depths of the highway in different time periods in each section of the area. Based on pit distribution density of roads in different time periods in each section of area, the area speed limit in each section of area of the roads is changed in real time.
The pit depth of the highway is the peak value of the change fluctuation image of the highway pavement, and the pit distribution density of the highway is the peak distribution interval of the change fluctuation image of the highway pavement. Through analysis of the change fluctuation image of the road surface, the pit change condition of the road in the area can be correspondingly obtained. And (5) maintaining the highway in the area by analyzing the change condition of the pit and the groove in the area.
The invention can accurately monitor the road surface of the highway in real time, more accurately evaluate the degree of the pit of the highway, more reasonably plan the highway maintenance process and avoid potential safety hazards caused by untimely discovery and treatment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
The structures, proportions, sizes, etc. shown in the present specification are shown only for the purposes of illustration and description, and are not intended to limit the scope of the invention, which is defined by the claims, so that any structural modifications, changes in proportions, or adjustments of sizes, which do not affect the efficacy or the achievement of the present invention, should fall within the ambit of the technical disclosure.
FIG. 1 is a flow chart of a road management method based on road flatness provided by the invention;
FIG. 2 is a flowchart showing a step S3 in the road management method according to the present invention;
FIG. 3 is a flowchart showing a step S5 in the road management method according to the present invention;
fig. 4 is a specific flowchart of step S6 in the road management method provided by the present invention;
fig. 5 is a connection block diagram of a road management system based on road flatness provided by the invention.
Detailed Description
Other advantages and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to a first aspect of the present invention, as shown in fig. 1, there is provided a road management method based on road flatness, comprising the steps of:
s1: dividing a highway into different areas, and acquiring image information of different angles and heights of the highway in the different areas through the unmanned aerial vehicle;
s2: image integration is carried out based on image information of roads in different areas at different angles and heights, and initial fluctuation images of road surfaces in different areas are obtained;
s3: determining road flatness change speeds of different areas according to a road flatness change prediction model, predicting and drawing change fluctuation images of road surfaces in different areas and in different time periods;
s4: predicting the pit depths and the pit distribution densities of the highways in different time periods in different areas according to the change fluctuation images of the highways in different time periods in different areas;
s5: determining pit maintenance time of each section of the highway based on pit depths of the highway in different time periods in each section of the area;
s6: based on pit distribution density of roads in different time periods in each section of area, the area speed limit in each section of area of the roads is changed in real time.
Generally, the highway is wound in a curved way, and is divided into different areas, so that workers can conveniently maintain the highway separately, and the work tasks of the workers are clearer. The number and depth of pits on the highway are different in different areas, and the maintenance comments are different.
When fewer vehicles exist on the highway, the unmanned aerial vehicle is controlled to respectively travel to four corners of the highway in different areas according to different height airlines, and image information of the four corners is spliced to obtain initial fluctuation images of the road surface in the different areas.
When vehicles on the highway gradually increase, vehicle information on the highway is acquired. Different traffic flows and vehicle types affect the extent of pit formation on a highway. And determining the road flatness change speed according to the vehicle information on the road, and using the road flatness change speed to predict and generate a change fluctuation image of the road surface in the next time period.
The pit depth of the highway is the peak value of the change fluctuation image of the highway pavement, and the pit distribution density of the highway is the peak distribution interval of the change fluctuation image of the highway pavement. Through analysis of the change fluctuation image of the road surface, the pit change condition of the road in the area can be correspondingly obtained. And (5) maintaining the highway in the area by analyzing the change condition of the pit and the groove in the area.
As shown in fig. 2, the process of establishing the road flatness variation prediction model in step S3 specifically includes the following steps:
s301: continuously acquiring the traffic flow, the vehicle type and the maximum load of the vehicle of the highway in a unit time period;
s302: the unmanned aerial vehicle continuously acquires at least a first change fluctuation image, a second change fluctuation image and a third change fluctuation image of the highway in a unit time period;
s303: and comparing the first change fluctuation image, the second change fluctuation image and the third change fluctuation image, and determining the change speed of the road flatness along with the change of the vehicle information in the step S301 in a unit time period according to the amplitude change.
Comparing the first change fluctuation image with the second change fluctuation image, and determining the first road flatness change speed in a unit time period according to the amplitude change of the first change fluctuation image; and comparing the second variation fluctuation image with the third variation fluctuation image, and determining the second road flatness variation speed in the unit time period according to the amplitude variation of the second variation fluctuation image. The first road flatness change speed and the second road flatness change speed are the same in time change, and the change parameters are the vehicle flow, the vehicle type and the maximum vehicle load of the unit time period, so that the road flatness change speed can be obtained to be related to the vehicle information. The road flatness change speed is continuously changed in different time periods.
The formula of the road flatness change speed is:
v＝f 1 (a)+(f 2 (b)) i ；
wherein v is the change speed of road flatness, f 1 (a) A first preset function of the change speed of the road flatness, a is the traffic flow of the road in a unit time period, f 2 (b) And b is a second preset function of the change speed of the road flatness, b is the type of the vehicle passing through the road in a unit time period, and i is the maximum load of the vehicle corresponding to the type of the vehicle.
For example, the road flatness change speed of the first area is found by observing the change of the vehicle information on the road in the first area. Predicting a change fluctuation image of the road surface of the first area according to the change speed of the road flatness of the first area; and obtaining the road flatness change speed of the second area by observing the vehicle information change on the road in the second area. And predicting a change fluctuation image of the road surface of the second area according to the change speed of the road flatness of the second area. And respectively analyzing the change fluctuation images of the road surfaces of different areas, and providing maintenance comments for the roads of different areas.
As shown in fig. 3, step S5 specifically includes the following steps:
s501: predicting and acquiring change fluctuation images of road surfaces of roads in continuous time periods, and continuously obtaining pit depths of roads in different time periods;
s502: continuously comparing the pit depth of the highway with a pit depth threshold value;
s503: if the pit depth of the highway is greater than the pit depth threshold, an alarm signal is transmitted to a remote terminal to prompt a worker to maintain the highway in the area.
The pit depth threshold value is obtained by screening big data, and is a pit depth value which is easy to cause traffic accidents. If the pit depth of the highway is greater than the pit depth threshold, this indicates that the highway in this area needs to be serviced at that time.
As shown in fig. 4, step S6 specifically includes the following steps:
s601: determining the number of pits and the pitch of the pits on the highway based on the change fluctuation image of the road surface;
s602: summing the pit spacing on the road surface and averaging to obtain the pit distribution density of the road;
s603: predicting and acquiring variation fluctuation images of road surfaces in continuous time periods, and continuously obtaining pit distribution densities of roads in different time periods;
s604: if the pit distribution density of the highway pavement is increased by one percentage, the regional speed limit in each section of region of the highway is correspondingly reduced by ten percentages.
The formula of the pit distribution density of the highway is as follows:
wherein ρ is pit distribution density of highway, h i For the ith pit space, n is the number of pits, and the value range of i is an integer of more than or equal to 1 and less than or equal to n-1.
The highway in each section of area has the regional speed limit, and the regional speed limit is further changed through the pit distribution density, so that the vehicle can stably run on the road. Because the fluctuation curve of the predicted road surface fluctuation image is different in shape, the distribution interval of wave crests on the fluctuation image is easy to change, and thus the pit distribution density is changed. The higher the pit distribution density, the more likely the road is to jolt, and the vehicle is required to run slowly. The regional speed limit of roads in different regions is changed in real time by analyzing the pit distribution density on the fluctuation image, so that the safe driving environment is improved.
And predicting the width of the crack of the highway based on the change fluctuation images of the road surface in different areas and different time periods, and carrying out crack maintenance. If the change of the road surface fluctuates to the place with the break on the image, the position is the crack position of the road, and the distance of the break is the width of the crack. And continuously comparing the crack width of the highway with a crack width threshold value, and if the crack width of the highway is larger than the crack width threshold value, transmitting an alarm signal to a remote terminal to prompt a worker to carry out crack maintenance on the highway in the area.
According to a second aspect of the present invention, there is provided a road management system based on road flatness, for implementing a road management method based on road flatness, as shown in fig. 5, comprising:
the information acquisition unit is used for acquiring image information of different angles and heights of roads in different areas;
the information processing unit is used for processing image information of roads in different areas at different angles and heights, integrating the images and acquiring initial fluctuation images of road surfaces in different areas;
the image prediction drawing unit is used for determining the road flatness change speed of different areas according to the road flatness change prediction model, and predicting and drawing the change fluctuation images of the road surfaces in different areas and in different time periods;
the image processing unit is used for obtaining pit depths and pit distribution densities of roads in different time periods in different areas according to the change fluctuation images of the road surfaces in different time periods in the different areas;
and the feedback unit is used for determining pit maintenance time of each section of area of the highway and real-time changing of the regional speed limit in each section of area of the highway.
Dividing the highway into different areas, acquiring image information of different angles and heights of the highway in the different areas through the unmanned aerial vehicle, and transmitting the image information to the information acquisition unit. And based on the image information of different angles and heights of the highways in different areas, the information processing unit performs image integration to acquire initial fluctuation images of the highways in different areas. And determining the road flatness change speed of different areas according to the road flatness change prediction model, and predicting and drawing the change fluctuation images of the road pavement in different time periods in different areas by the image prediction drawing unit.
And predicting the pit depths and the pit distribution densities of the highways in different time periods in different areas by the image processing unit according to the change fluctuation images of the highways in different time periods in different areas. Based on the pit depths of the roads in different time periods in each section of area, the feedback unit determines the pit maintenance time of each section of area of the roads. Based on pit distribution density of roads in different time periods in each section of area, the feedback unit changes the area speed limit in each section of area of the roads in real time.
While the invention has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (7)
1. The road management method based on the road flatness is characterized by comprising the following steps of:
s1: dividing a highway into different areas, and acquiring image information of different angles and heights of the highway in the different areas;
s2: image integration is carried out based on image information of different angles and heights of the highway in different areas, and initial fluctuation images of the road surface in different areas are obtained;
s3: determining road flatness change speeds of different areas according to a road flatness change prediction model, predicting and drawing change fluctuation images of the road pavement in different areas and at different time periods;
s4: predicting the pit depths and the pit distribution densities of the highways in different time periods in different areas according to the change fluctuation images of the highways in different time periods in different areas;
s5: determining pit maintenance time of each section of the highway based on pit depths of the highway in different time periods in each section of the area;
s6: based on pit distribution density of the highway in different time periods in each section of area, real-time change of area speed limit in each section of area of the highway;
the establishing process of the road flatness change prediction model in the S3 specifically comprises the following steps:
s301: continuously acquiring the traffic flow, the vehicle type and the maximum load of the vehicle of the highway in a unit time period;
s302: the unmanned aerial vehicle continuously acquires at least a first change fluctuation image, a second change fluctuation image and a third change fluctuation image of the highway in a unit time period;
s303: comparing the first change fluctuation image, the second change fluctuation image and the third change fluctuation image, and determining a first road flatness change speed and a second road flatness change speed which change along with the vehicle information in the step S301 in a unit time period according to the amplitude change;
s304: determining a corresponding relation between the road flatness change speed and the vehicle information change based on a first road flatness change speed and a second road flatness change speed which change along with the vehicle information, and determining a formula of the road flatness change speed;
the formula of the road flatness change speed is as follows:
v＝f 1 (a)+(f 2 (b)) i ；
wherein v is the change speed of road flatness, f 1 (a) A first preset function of the change speed of the road flatness, a is the traffic flow of the road in a unit time period, f 2 (b) B is a second preset function of the change speed of the road flatness, b is the type of the vehicle passing through the road in a unit time period, and i is the maximum load of the vehicle corresponding to the type of the vehicle;
the pit depth of the highway is the peak value of the change fluctuation image of the highway pavement, and the pit distribution density of the highway is the peak distribution interval of the change fluctuation image of the highway pavement.
2. The road management method based on road flatness of claim 1, wherein S5 specifically comprises the steps of:
s501: predicting and acquiring variation fluctuation images of the road surface of the highway in continuous time periods, and continuously obtaining pit depths of the highway in different time periods;
s502: continuously comparing the pit depth of the highway with a pit depth threshold;
s503: if the pit depth of the highway is greater than the pit depth threshold, transmitting an alarm signal to a remote terminal to prompt a worker to maintain the highway in the area.
3. The road management method based on road flatness of claim 1, wherein S6 specifically comprises the steps of:
s601: determining the number of pits and the pitch of the pits of the highway based on the change fluctuation image of the highway pavement;
s602: summing the pit spacing on the highway pavement and averaging to obtain the pit distribution density of the highway;
s603: predicting and acquiring variation fluctuation images of the road surface of the highway in continuous time periods, and continuously obtaining pit distribution densities of the highway in different time periods;
s604: if the pit distribution density of the highway pavement is increased by one percentage, the regional speed limit in each section of region of the highway is correspondingly reduced by ten percentages.
4. The road management method based on road flatness of claim 3, wherein the formula of pit distribution density of the road is:
wherein ρ is the pit distribution density, h of the highway i Is the ith pit space, n is the pitThe number of the grooves, i, is an integer of 1 to n-1.
5. The road management method based on road flatness of claim 1, wherein the crack width of the road is also predicted based on the varying fluctuation image of the road surface in different areas for different time periods, and crack maintenance is performed.
6. The road management method based on road flatness of claim 1, wherein the unmanned aerial vehicle in S2 runs to four corners of the road in different areas according to different altitude routes, and the image information of the four corners is spliced to obtain the initial fluctuation image of the road surface in different areas.
7. A road management system based on road flatness for implementing the road management method based on road flatness according to any one of claims 1-6, comprising:
the information acquisition unit is used for acquiring image information of different angles and heights of the highway in different areas;
the information processing unit is used for processing the image information of the roads at different angles and heights in different areas, carrying out image integration and obtaining initial fluctuation images of the road surfaces in different areas;
the image prediction drawing unit is used for determining the road flatness change speed of different areas according to the road flatness change prediction model, and predicting and drawing the change fluctuation images of the road surface in different areas and in different time periods;
the image processing unit is used for obtaining pit depths and pit distribution densities of the highways in different time periods in different areas according to the change fluctuation images of the highway pavement in different time periods in different areas;
and the feedback unit is used for determining pit maintenance time of each section of area of the highway and real-time changing the area speed limit in each section of area of the highway.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107092020A (en) * | 2017-04-19 | 2017-08-25 | 北京大学 | Merge the surface evenness monitoring method of unmanned plane LiDAR and high score image |
| JP2018028486A (en) * | 2016-08-18 | 2018-02-22 | 西日本高速道路エンジニアリング四国株式会社 | Method for quantitatively analyzing pothole generation risk in drainable pavement |
| TWI660607B (en) * | 2017-11-17 | 2019-05-21 | 中華電信股份有限公司 | Method and system for monitoring road pavement quality |
| KR102055585B1 (en) * | 2018-10-29 | 2019-12-13 | (주)케이웍스 | System for Measuring Pothole of Road and Method Thereof |
| CN113514837A (en) * | 2021-06-02 | 2021-10-19 | 东南大学 | Pavement track detection device and method |
| CN115272222A (en) * | 2022-07-25 | 2022-11-01 | 中科海微(北京)科技有限公司 | Method, device and equipment for processing road detection information and storage medium |
| CN115511788A (en) * | 2022-08-30 | 2022-12-23 | 清远市粤通机动车驾驶人培训有限公司 | Method for automatic detection and modeling of motor vehicle driver examination field |
| CN115993440A (en) * | 2022-11-17 | 2023-04-21 | 华中科技大学 | Evolution rule analysis method for pavement pit |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11373532B2 (en) * | 2019-02-01 | 2022-06-28 | Hitachi Astemo, Ltd. | Pothole detection system |
| CN112326686B (en) * | 2020-11-02 | 2024-02-02 | 坝道工程医院(平舆) | Unmanned aerial vehicle intelligent cruising pavement disease detection method, unmanned aerial vehicle and detection system |
-
2023
- 2023-05-30 CN CN202310618654.XA patent/CN116612400B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2018028486A (en) * | 2016-08-18 | 2018-02-22 | 西日本高速道路エンジニアリング四国株式会社 | Method for quantitatively analyzing pothole generation risk in drainable pavement |
| CN107092020A (en) * | 2017-04-19 | 2017-08-25 | 北京大学 | Merge the surface evenness monitoring method of unmanned plane LiDAR and high score image |
| TWI660607B (en) * | 2017-11-17 | 2019-05-21 | 中華電信股份有限公司 | Method and system for monitoring road pavement quality |
| KR102055585B1 (en) * | 2018-10-29 | 2019-12-13 | (주)케이웍스 | System for Measuring Pothole of Road and Method Thereof |
| CN113514837A (en) * | 2021-06-02 | 2021-10-19 | 东南大学 | Pavement track detection device and method |
| CN115272222A (en) * | 2022-07-25 | 2022-11-01 | 中科海微(北京)科技有限公司 | Method, device and equipment for processing road detection information and storage medium |
| CN115511788A (en) * | 2022-08-30 | 2022-12-23 | 清远市粤通机动车驾驶人培训有限公司 | Method for automatic detection and modeling of motor vehicle driver examination field |
| CN115993440A (en) * | 2022-11-17 | 2023-04-21 | 华中科技大学 | Evolution rule analysis method for pavement pit |
Non-Patent Citations (4)
| Title |
|---|
| .基于三维智能路面检测***识别沉陷病害的方法研究;陈梦月等;广东土木与建筑;第29卷(第5期);全文 * |
| A System for the Automatic Detection and Evaluation of the Runway Surface Cracks Obtained by Unmanned Aerial Vehicle Imagery Using Deep Convolutional Neural Networks;Maslan J等;Applied Sciences;全文 * |
| 山西省高速公路沥青路面平整度预测研;经叶松;中国硕士学位论文全文库 工程科技Ⅱ辑;全文 * |
| 考虑病害三维特征的沥青路面车辙异常检验方法;郎洪等;东南大学学报(自然科学版)(第03期);全文 * |
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