CN112067622A - Pavement crack identification method based on multi-source data fusion - Google Patents
Pavement crack identification method based on multi-source data fusion Download PDFInfo
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- CN112067622A CN112067622A CN202010941037.XA CN202010941037A CN112067622A CN 112067622 A CN112067622 A CN 112067622A CN 202010941037 A CN202010941037 A CN 202010941037A CN 112067622 A CN112067622 A CN 112067622A
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- 238000000034 method Methods 0.000 title claims abstract description 22
- 230000004927 fusion Effects 0.000 title claims abstract description 13
- 238000001514 detection method Methods 0.000 claims abstract description 52
- 238000004891 communication Methods 0.000 claims description 9
- 238000012795 verification Methods 0.000 abstract description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
Abstract
The invention relates to a multi-source data fusion-based pavement crack identification method, which comprises a detection system, wherein the detection system comprises a light source, a first camera and a second camera, the first light source is arranged at the front end of a detection vehicle, the first camera is arranged at the top of the front end of the detection vehicle, the second camera is arranged at the bottom of the front end of the detection vehicle, the detection vehicle is also provided with a GPS and a processor, and the processor is respectively connected with the first camera, the second camera and the GPS; the method of the invention simultaneously takes pictures of the same road surface through the two cameras, then compares the pictures to judge whether the shadow exists or not, further judges whether the road surface crack exists or not, and in addition, detects whether the crack exists at the shadow position judged by the trailer verification picture or not, thereby further determining the accuracy of the method of the invention.
Description
Technical Field
The invention relates to road surface detection, in particular to a road surface crack identification method based on multi-source data fusion.
Background
With the economic development, the quantity of automobiles is increasing, the traffic flow on the road is increasing day by day, vehicles passing through the road at high frequency cause a great deal of damage to the road surface, particularly when cracks appear on the road surface, when the vehicles pass through the cracks quickly, the tires of the vehicles are easily damaged, even the buffer structures of the vehicles are damaged, and further emergent traffic accidents are easily caused The accurate detection of the crack position of the road surface becomes a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a pavement crack identification method based on multi-source data fusion, which can quickly and accurately detect and identify cracks existing on a pavement and positions of the cracks.
The technical scheme adopted for realizing the aim of the invention is a pavement crack identification method based on multi-source data fusion, which comprises a detection system, wherein the detection system comprises a light source, a first camera and a second camera, the first light source is arranged at the front end of a detection vehicle, the first camera is arranged at the top of the front end of the detection vehicle, the second camera is arranged at the bottom of the front end of the detection vehicle, the detection vehicle is also provided with a GPS and a processor, and the processor is respectively connected with the first camera, the second camera and the GPS;
the method for identifying the pavement crack through the detection system comprises the following steps:
s1, shooting pictures of the detected vehicle in the driving process through the first camera and the second camera and transmitting the pictures to the processor;
s2, the processor compares the two cameras to take pictures of the same road surface at the same time, and if a long-strip-shaped shadow appears, the corresponding road surface is considered to have cracks. Specifically, the image may be gridded, that is, the image may be gridded by a plurality of horizontal lines and vertical lines, the number of the horizontal lines and the vertical lines may be selected according to the accuracy requirement, and the larger the number is, the smaller the grid into which the image is divided is, so that the higher the accuracy of the judgment is, and then whether the corresponding grid small blocks of the detected image and the standard image have shadows or not is compared, and if the long-strip shadows exist, the corresponding road surface at the position is considered to have cracks. And the position of the grid small block corresponding to the actual road surface is associated through the GPS, so that the position of the road surface crack is accurately determined.
Furthermore, the detection system also comprises a 5G communication module and a detection trailer, wherein the 5G communication module is connected with the processor, the detection trailer is rigidly connected with a detection vehicle, the detection trailer comprises a drag rod, the drag rod is connected with a plurality of rollers which are in close contact and can move up and down, the top end of each roller is connected with a spring, the spring is connected with a trigger switch, and the trigger switch is connected with the processor; the pavement crack identification method further includes:
and S3, transmitting the position information of the pavement crack determined in the step S2 to a remote control platform through a 5G communication module, transmitting the position information to a detection vehicle by the remote control platform, driving the detection trailer to pass through the crack position after the detection vehicle adjusts the direction, driving a spring to pull a trigger switch downwards when most of the crack positions in the detection trailer pass through the crack position, transmitting a pulling signal to a processor by the trigger switch, and determining that the crack does exist in the position by the processor, thereby ensuring the accuracy of the judgment in the step S2.
Furthermore, the first camera and the second camera are respectively connected to the detection vehicle through 360-degree rotating bases.
The method of the invention simultaneously takes pictures of the same road surface through the two cameras, then compares the pictures to judge whether the shadow exists or not, further judges whether the road surface crack exists or not, and in addition, detects whether the crack exists at the shadow position judged by the trailer verification picture or not, thereby further determining the accuracy of the method of the invention.
Drawings
Fig. 1 is a structural block diagram of a pavement crack identification system based on multi-source data fusion used in the invention.
FIG. 2 is a flow chart of the pavement crack identification method based on multi-source data fusion.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments.
As shown in fig. 1, the structure of the multi-source data fusion-based pavement crack recognition system used in the present invention includes a light source, a first camera and a second camera, the first light source is disposed at the front end of a detection vehicle, the first camera is disposed at the top of the front end of the detection vehicle, the second camera is disposed at the bottom of the front end of the detection vehicle, the detection vehicle is further provided with a 5G communication module, a GPS and a processor, and the processor is respectively connected with the first camera, the second camera, the 5G communication module and the GPS. Detect vehicle still rigid connection has the detection trailer, detects the trailer and includes the tow bar, and the tow bar is connected with a plurality of in close contact with and can reciprocate the gyro wheel, and every gyro wheel top is connected with the spring, and spring coupling has trigger switch, and trigger switch is connected with the treater.
The method for detecting the pavement cracks by the pavement crack identification system based on the multi-source data fusion comprises the following steps:
and S1, shooting pictures of the detected vehicle in the driving process through the first camera and the second camera and transmitting the pictures to the processor.
S2, the processor compares the two cameras to take pictures of the same road surface at the same time, and if a long-strip-shaped shadow appears, the corresponding road surface is considered to have cracks. Specifically, the image may be gridded, that is, the image may be gridded by a plurality of horizontal lines and vertical lines, the number of the horizontal lines and the vertical lines may be selected according to the accuracy requirement, and the larger the number is, the smaller the grid into which the image is divided is, so that the higher the accuracy of the judgment is, and then whether the corresponding grid small blocks of the detected image and the standard image have shadows or not is compared, and if the long-strip shadows exist, the corresponding road surface at the position is considered to have cracks. And the position of the grid small block corresponding to the actual road surface is associated through the GPS, so that the position of the road surface crack is accurately determined.
And S3, transmitting the position information of the pavement crack determined in the step S2 to a remote control platform through a 5G communication module, transmitting the position information to a detection vehicle by the remote control platform, driving the detection trailer to pass through the crack position after the detection vehicle adjusts the direction, driving a spring to pull a trigger switch downwards when most of the crack positions in the detection trailer pass through the crack position, transmitting a pulling signal to a processor by the trigger switch, and determining that the crack does exist in the position by the processor, thereby ensuring the accuracy of the judgment in the step S2.
As another preferred embodiment of the invention, the first camera and the second camera are respectively connected to the detection vehicle through a 360-degree rotating base, and pictures shot by the first camera and the second camera are correspondingly consistent with the position of the road surface by adjusting the 360-degree electric adjusting base.
Claims (3)
1. A pavement crack identification method based on multi-source data fusion is characterized by comprising the following steps: the vehicle detection system comprises a detection system, wherein the detection system comprises a light source, a first camera and a second camera, the first light source is arranged at the front end of a detection vehicle, the first camera is arranged at the top of the front end of the detection vehicle, the second camera is arranged at the bottom of the front end of the detection vehicle, the detection vehicle is also provided with a GPS and a processor, and the processor is respectively connected with the first camera, the second camera and the GPS;
the method for identifying the pavement crack through the detection system comprises the following steps:
s1, shooting pictures of the detected vehicle in the driving process through the first camera and the second camera and transmitting the pictures to the processor;
s2, the processor compares the two cameras to take pictures of the same road surface at the same time, and if a long-strip-shaped shadow appears, the corresponding road surface is considered to have cracks. Specifically, the image may be gridded, that is, the image may be gridded by a plurality of horizontal lines and vertical lines, the number of the horizontal lines and the vertical lines may be selected according to the accuracy requirement, and the larger the number is, the smaller the grid into which the image is divided is, so that the higher the accuracy of the judgment is, and then whether the corresponding grid small blocks of the detected image and the standard image have shadows or not is compared, and if the long-strip shadows exist, the corresponding road surface at the position is considered to have cracks. And the position of the grid small block corresponding to the actual road surface is associated through the GPS, so that the position of the road surface crack is accurately determined.
2. The multi-source data fusion-based pavement crack identification method according to claim 1, characterized in that: the detection system further comprises a 5G communication module and a detection trailer, the 5G communication module is connected with the processor, the detection trailer is rigidly connected with a detection vehicle, the detection trailer comprises a drag rod, the drag rod is connected with a plurality of rollers which are in close contact and can move up and down, the top end of each roller is connected with a spring, the spring is connected with a trigger switch, and the trigger switch is connected with the processor; the pavement crack identification method further includes:
and S3, transmitting the position information of the pavement crack determined in the step S2 to a remote control platform through a 5G communication module, transmitting the position information to a detection vehicle by the remote control platform, driving the detection trailer to pass through the crack position after the detection vehicle adjusts the direction, driving a spring to pull a trigger switch downwards when most of the crack positions in the detection trailer pass through the crack position, transmitting a pulling signal to a processor by the trigger switch, and determining that the crack does exist in the position by the processor, thereby ensuring the accuracy of the judgment in the step S2.
3. The multi-source data fusion-based pavement crack identification method according to claim 1, characterized in that: first camera and second camera are connected on detecting the vehicle through 360 degrees rotating base respectively.
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| CN202010941037.XA CN112067622A (en) | 2020-09-09 | 2020-09-09 | Pavement crack identification method based on multi-source data fusion |
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| CN202010941037.XA CN112067622A (en) | 2020-09-09 | 2020-09-09 | Pavement crack identification method based on multi-source data fusion |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4899296A (en) * | 1987-11-13 | 1990-02-06 | Khattak Anwar S | Pavement distress survey system |
| JP2000194983A (en) * | 1998-12-28 | 2000-07-14 | Nichireki Co Ltd | Road surface and roadside photographing vehicle |
| CN101701919A (en) * | 2009-11-20 | 2010-05-05 | 长安大学 | Pavement crack detection system based on image and detection method thereof |
| CN104864909A (en) * | 2015-05-08 | 2015-08-26 | 苏州科技学院 | Road surface pothole detection device based on vehicle-mounted binocular vision |
| CN106087677A (en) * | 2016-06-02 | 2016-11-09 | 上海华城工程建设管理有限公司 | Asphalt pavement crack type automatic identifying method |
| CN110220594A (en) * | 2019-07-24 | 2019-09-10 | 哈尔滨工业大学（深圳） | Mobile platform and the vibration detecting system acquired based on distributed synchronization |
-
2020
- 2020-09-09 CN CN202010941037.XA patent/CN112067622A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4899296A (en) * | 1987-11-13 | 1990-02-06 | Khattak Anwar S | Pavement distress survey system |
| JP2000194983A (en) * | 1998-12-28 | 2000-07-14 | Nichireki Co Ltd | Road surface and roadside photographing vehicle |
| CN101701919A (en) * | 2009-11-20 | 2010-05-05 | 长安大学 | Pavement crack detection system based on image and detection method thereof |
| CN104864909A (en) * | 2015-05-08 | 2015-08-26 | 苏州科技学院 | Road surface pothole detection device based on vehicle-mounted binocular vision |
| CN106087677A (en) * | 2016-06-02 | 2016-11-09 | 上海华城工程建设管理有限公司 | Asphalt pavement crack type automatic identifying method |
| CN110220594A (en) * | 2019-07-24 | 2019-09-10 | 哈尔滨工业大学（深圳） | Mobile platform and the vibration detecting system acquired based on distributed synchronization |
Non-Patent Citations (1)
| Title |
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| 肖力炀 等: "基于JTG 5210-2018标准的裂缝自动化检测方法", 《中国公路学会养护与管理分会第十届学术年会论文集》 * |
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Application publication date: 20201211 |