CN115511788A - Method for automatic detection and modeling of motor vehicle driver examination field - Google Patents
Method for automatic detection and modeling of motor vehicle driver examination field Download PDFInfo
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Abstract
The invention relates to the field of field detection modeling, in particular to a method for automatically detecting and modeling an examination field of a motor vehicle driver, which comprises the following steps: s1, acquiring a three-dimensional model of an actual examination room; s2, comparing the three-dimensional size data of the examination room with the theoretical size in the acceptance standard to obtain the detection result of the quantitative characteristic information of the examination room; s3, selecting road surface point data in the point cloud data, and judging the road surface evenness; and S4, outputting a test field quantitative characteristic information detection result graph and a road surface flatness judgment graph, and marking an unqualified detection result area in the corresponding graph. According to the invention, the three-dimensional model of the actual examination room is obtained by the unmanned aerial vehicle or other means, and then the examination room can be automatically detected by combining with the acceptance standard, so that the detection efficiency is high, the detection effect is good, in addition, the judgment result of the pavement evenness can be obtained, and the comprehensiveness of the automatic detection of the examination room is improved.
Description
Technical Field
The invention relates to the technical field of site detection modeling, in particular to a method for automatically detecting and modeling a motor vehicle driver examination site.
Background
The motor vehicle driver examination comprises theoretical examination and motor vehicle driver site driving skill examination (subject two and subject three), and examination sites qualified in acceptance are the fair and fair basis of subject two and subject three examinations, however, the manual site acceptance detection has the problems of large detection workload, high time cost and poor accuracy.
The Chinese patent with the authorization notice number CN111580128A discloses a method for automatic detection modeling of an examination field of a motor vehicle driver, which comprises the following steps: respectively acquiring image data and laser point cloud data of an examination field of a driver of a motor vehicle to be detected by using an unmanned aerial vehicle, and transmitting the image data and the laser point cloud data to an image processing cloud platform; modeling is carried out according to the image data and the laser point cloud data through the image processing cloud platform to obtain a three-dimensional model of the examination place, and a detection result is obtained according to examination place quantitative feature information in the three-dimensional model and corresponding examination place quantitative feature information in a pre-established examination place acceptance standard three-dimensional model. The system can accurately detect the examination site and facilities of the motor vehicle driver, solves the problems of large workload, high time cost and the like of manual site detection, improves the detection efficiency of the examination site, and ensures the accuracy, objectivity and justice of detection.
However, the above-mentioned known solutions have the following disadvantages: in the absence of the flatness analysis of the test road surface, although the importance priority of the flatness is set to be lower than that of the standard size of the test field, the flatness is also an important factor influencing the driving of the vehicle.
Disclosure of Invention
The invention aims to provide a method for automatic detection and modeling of a motor vehicle driver examination field, aiming at the problems of low manual detection efficiency, high manual cost and difficulty in ensuring detection precision of the driving examination field in the background technology.
The technical scheme of the invention is as follows: a method for automatically detecting and modeling an examination field of a driver of a motor vehicle comprises the following steps:
s1, acquiring a three-dimensional model of an actual examination room;
s2, comparing the three-dimensional size data of the examination room with the theoretical size in the acceptance standard to obtain the detection result of the quantitative characteristic information of the examination room;
s3, selecting road surface point data in the point cloud data, and judging the road surface evenness;
and S4, outputting a test field quantitative characteristic information detection result graph and a road flatness judgment graph, and marking out an unqualified detection result area in the corresponding graph.
Preferably, in S1, the three-dimensional model initial data of the actual examination room is obtained by an unmanned aerial vehicle carrying a camera device and a laser device, the camera device obtains graphic information, the laser device obtains point cloud data information, and the graphic information and the power data information are combined to obtain the three-dimensional model of the examination room.
Preferably, the three-dimensional data splicing combination is carried out on the multiple groups of point cloud data through a point cloud data splicing technology.
Preferably, in S1, the three-dimensional model of the actual examination room is a colored three-dimensional model.
Preferably, in S2, secondary data acquisition is carried out for positions where the examination room quantitative characteristic information detection results are unqualified, then secondary judgment is carried out, the positions are still unqualified, and unqualified judgment results are output.
Preferably, in S2, in the three-dimensional model of the actual examination room, the unqualified examination room quantitative feature information detection area is marked with red and specific unqualified data is noted, and the qualified examination room quantitative feature information detection area is normally displayed under the actual condition.
Preferably, in S3, the specific method for determining road flatness includes the following steps:
s31, intercepting road surface point cloud data in the point cloud data, establishing a two-dimensional coordinate by taking the extending direction of a road as an X axis and the vertical direction as a Z axis, obtaining a scatter diagram according to the road surface point cloud data, and obtaining a central curve a according to the point distribution condition in the scatter diagram, wherein the central curve a reflects the road surface flatness condition in the length direction of the road;
s32, obtaining deviation data of each point according to the central curve a, and judging whether the out-of-tolerance exists or not according to a preset threshold value;
s33, establishing a two-dimensional coordinate by taking the width direction of the road surface as a Y axis and the vertical direction as a Z axis, obtaining a scatter diagram according to the point cloud data of the road surface, and obtaining a central curve b according to the point distribution condition in the scatter diagram, wherein the central curve b reflects the road surface flatness condition in the width direction of the road;
s34, obtaining deviation data of each point according to the central curve b, and judging whether the out-of-tolerance exists or not according to a preset threshold value;
and S35, outputting a road surface flatness detection result according to the judgment results in the S32 and the S34.
Preferably, in S35, the area with the unqualified road flatness detection result is marked red in the three-dimensional model of the actual examination room.
Compared with the prior art, the invention has the following beneficial technical effects: the three-dimensional model of actual examination room is obtained through unmanned aerial vehicle or other means, and the inspection standard that combines to check again can carry out automated inspection to the examination room, and detection efficiency is high, and detection effect is good, combines three-dimensional model coordinate to obtain road flatness judged result in addition when examination room quantization testing, improves examination room automated inspection's comprehensiveness, makes the road surface requirement that drives the examination room and accord with actual.
Drawings
FIG. 1 is a flow chart of one embodiment of the present invention;
fig. 2 is a flowchart of a road flatness determination method.
Detailed Description
Example one
As shown in fig. 1, the method for automatically detecting and modeling the test field of the driver of the motor vehicle provided by the invention comprises the following steps:
s1, acquiring a three-dimensional model of an actual examination room;
s2, comparing the three-dimensional size data of the examination room with the theoretical size in the acceptance standard to obtain the detection result of the quantitative characteristic information of the examination room; performing secondary data acquisition aiming at the position of the examination room quantitative characteristic information detection result which is unqualified, then performing secondary judgment, wherein the secondary judgment is still unqualified, and outputting an unqualified judgment result; in a three-dimensional model of an actual examination room, marking red in an examination room quantitative characteristic information detection unqualified area and remarking specific unqualified data, wherein the examination room quantitative characteristic information detection qualified area is normally displayed according to actual conditions;
s3, selecting road surface point data in the point cloud data, and judging the road surface evenness;
and S4, outputting a test field quantitative characteristic information detection result graph and a road surface flatness judgment graph, and marking an unqualified detection result area in the corresponding graph.
In this embodiment, acquire the three-dimensional model of actual examination room through unmanned aerial vehicle or other means, combine to check and accept the standard again and can carry out automated inspection to the examination room, detection efficiency is high, and detection effect is good, combines the three-dimensional model coordinate to obtain the road flatness judged result when the examination room quantization in addition detects, improves examination room automated inspection's comprehensiveness, makes the driving examination room accord with actual driving's road surface requirement.
Example two
Compared with the first embodiment, in S1, the initial data of the three-dimensional model of the actual examination room is acquired by carrying a camera device and a laser device by an unmanned aerial vehicle, the camera device acquires graphic information, if a monitoring device covering the driving examination room can also acquire the graphic information by the monitoring device, the laser device acquires point cloud data information, the graphic information and the power supply data information are combined to obtain the three-dimensional model of the examination room, a plurality of groups of point cloud data are subjected to three-dimensional data splicing and combining by a point cloud data splicing technology, and the three-dimensional model of the actual examination room is a colored three-dimensional model, namely a combination of three-dimensional coordinates and colors, and is vivid and visual.
EXAMPLE III
As shown in fig. 2, compared with the first embodiment, in S3, the method for automatically detecting and modeling the test site of the driver of the motor vehicle provided by the present invention includes the following steps:
s31, intercepting road surface point cloud data in the point cloud data, establishing a two-dimensional coordinate by taking the extending direction of a road as an X axis and the vertical direction as a Z axis, obtaining a scatter diagram according to the road surface point cloud data, and obtaining a central curve a according to the point distribution condition in the scatter diagram, wherein the central curve a reflects the road surface flatness condition in the length direction of the road;
s32, obtaining deviation data of each point according to the central curve a, and judging whether the out-of-tolerance exists or not according to a preset threshold value;
s33, establishing a two-dimensional coordinate by taking the width direction of the road surface as a Y axis and the vertical direction as a Z axis, obtaining a scatter diagram according to the point cloud data of the road surface, and obtaining a central curve b according to the point distribution condition in the scatter diagram, wherein the central curve b reflects the road surface flatness condition in the width direction of the road;
s34, obtaining deviation data of each point according to the central curve b, and judging whether the out-of-tolerance exists or not according to a preset threshold;
and S35, outputting a road flatness detection result according to the judgment results in the S32 and the S34, and marking the area with the unqualified road flatness detection result in a three-dimensional model of the actual examination room with red marks.
In this embodiment, the deviation data of each point is obtained through the central curve a, whether the deviation data of each point exceeds the preset threshold value is judged, the deviation data of each point is obtained through the central curve b, and whether the deviation data exceeds the preset threshold value is judged, so that the flatness judgment condition of the whole road surface is obtained, the flatness data can be conveniently obtained while the examination room is quantitatively detected, the comprehensiveness of the automatic detection of the examination room is improved, and the driving examination room meets the road surface requirements of actual driving.
The embodiments of the present invention have been described in detail with reference to the drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (8)
1. A method for automatically detecting and modeling an examination site of a driver of a motor vehicle is characterized by comprising the following steps:
s1, acquiring a three-dimensional model of an actual examination room;
s2, comparing the three-dimensional size data of the examination room with the theoretical size in the acceptance standard to obtain the detection result of the quantitative characteristic information of the examination room;
s3, selecting road surface point data in the point cloud data, and judging the road surface evenness;
and S4, outputting a test field quantitative characteristic information detection result graph and a road surface flatness judgment graph, and marking an unqualified detection result area in the corresponding graph.
2. The method for automatic detection and modeling of the motor vehicle driver examination site according to claim 1, wherein in S1, the initial data of the three-dimensional model of the actual examination site is obtained by carrying a camera device and a laser device by an unmanned aerial vehicle, the camera device obtains graphic information, the laser device obtains point cloud data information, and the graphic information and the power supply data information are combined to obtain the three-dimensional model of the examination site.
3. The method for automatic detection and modeling of the examination site of the motor vehicle driver as claimed in claim 2, wherein the plurality of groups of point cloud data are combined by three-dimensional data stitching through a point cloud data stitching technique.
4. The method for automatic detection and modeling of an examination room of a motor vehicle driver as claimed in claim 1, wherein in S1, the three-dimensional model of the actual examination room is a colored three-dimensional model.
5. The method for automatic detection and modeling of the examination site of the driver of the motor vehicle as claimed in claim 1, wherein in S2, secondary data acquisition is performed for positions where the detection result of the quantitative characteristic information of the examination site is not qualified, then secondary judgment is performed, the secondary judgment is still not qualified, and a non-qualified judgment result is output.
6. The method for automatic detection and modeling of the examination room of the motor vehicle driver as claimed in claim 1, wherein in the step S2, in the three-dimensional model of the actual examination room, the unqualified examination room quantitative feature information detection area is marked with red and notes specific unqualified data, and the qualified examination room quantitative feature information detection area is displayed normally in the actual situation.
7. The method for automatic detection and modeling of the examination site of the driver of the motor vehicle as claimed in claim 1, wherein in S3, the specific method for judging the flatness of the road comprises the following steps:
s31, intercepting road surface point cloud data in the point cloud data, establishing a two-dimensional coordinate by taking the extending direction of a road as an X axis and the vertical direction as a Z axis, obtaining a scatter diagram according to the road surface point cloud data, and obtaining a central curve a according to the point distribution condition in the scatter diagram, wherein the central curve a reflects the road surface flatness condition in the length direction of the road;
s32, obtaining deviation data of each point according to the central curve a, and judging whether the out-of-tolerance exists or not according to a preset threshold value;
s33, establishing a two-dimensional coordinate by taking the width direction of the road surface as a Y axis and the vertical direction as a Z axis, obtaining a scatter diagram according to the point cloud data of the road surface, and obtaining a central curve b according to the point distribution condition in the scatter diagram, wherein the central curve b reflects the road surface flatness condition in the width direction of the road;
s34, obtaining deviation data of each point according to the central curve b, and judging whether the out-of-tolerance exists or not according to a preset threshold value;
and S35, outputting a road surface flatness detection result according to the judgment results in the S32 and the S34.
8. The method according to claim 7, wherein in step S35, the area with unqualified road flatness detection results is marked with red marks in a three-dimensional model of an actual examination room.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116612400A (en) * | 2023-05-30 | 2023-08-18 | 衡水金湖交通发展集团有限公司 | Road management method and system based on road flatness |
| CN116930758A (en) * | 2023-09-14 | 2023-10-24 | 山东星科智能科技股份有限公司 | Asynchronous motor comprehensive performance test system and test method |
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- 2022-08-30 CN CN202211046600.2A patent/CN115511788A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116612400A (en) * | 2023-05-30 | 2023-08-18 | 衡水金湖交通发展集团有限公司 | Road management method and system based on road flatness |
| CN116612400B (en) * | 2023-05-30 | 2024-03-19 | 衡水金湖交通发展集团有限公司 | Road management method and system based on road flatness |
| CN116930758A (en) * | 2023-09-14 | 2023-10-24 | 山东星科智能科技股份有限公司 | Asynchronous motor comprehensive performance test system and test method |
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