CN109636820B - Electronic map lane line correction method, device and computer readable storage medium - Google Patents
Electronic map lane line correction method, device and computer readable storage medium Download PDFInfo
- Publication number
- CN109636820B CN109636820B CN201811286291.XA CN201811286291A CN109636820B CN 109636820 B CN109636820 B CN 109636820B CN 201811286291 A CN201811286291 A CN 201811286291A CN 109636820 B CN109636820 B CN 109636820B
- Authority
- CN
- China
- Prior art keywords
- point
- line
- lane line
- preset area
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
Abstract
The embodiment of the invention provides a lane line correction method and device for an electronic map and a computer readable storage medium. The embodiment of the invention determines the reference line for marking the central line of the lane line through the area occupied by the lane line in the target image, the reference line may deviate from the true center line of the lane line, and further, the energy distribution of a preset area around each reference point on the reference line is used to determine the point with the highest energy in the preset area, since the point with the highest energy in the preset area is the point on the center line of the lane line, or, the point of highest energy in the preset area is a point close to the center line of the lane line, and therefore, each of the plurality of reference points is corrected to the point of highest energy in the preset area around the reference point, so that the reference line is closer to the center line of the lane line, when the electronic map is generated according to the position information of the reference line, the accuracy of the position of the lane line in the electronic map can be improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a method and equipment for correcting lane lines of an electronic map and a computer-readable storage medium.
Background
When generating electronic map at present, need gather the image information in lane to mark the lane line in this image information through the manual work, carry out machine learning through a large amount of lane lines of artifical mark out, be about to the neural network model of a large amount of lane lines of artifical mark out as the sample training, make the well-trained neural network model can discern the lane line.
However, the lane lines marked manually may not be accurate, that is, the samples used for training the neural network model may not be accurate, so that the trained neural network model cannot accurately identify the lane lines, and the positions of the lane lines in the electronic map are not accurate.
Disclosure of Invention
The embodiment of the invention provides a lane line correction method and device of an electronic map and a computer readable storage medium, which are used for improving the accuracy of the position of a lane line in the electronic map.
In a first aspect, an embodiment of the present invention provides a lane line correction method for an electronic map, including:
acquiring a target image including a lane line;
determining a reference line according to an area occupied by the lane line in the target image, wherein the reference line is used for identifying a central line of the lane line and comprises a plurality of reference points;
determining a point with highest energy in a preset area according to the energy distribution of the preset area around each reference point in the plurality of reference points;
and correcting each reference point in the plurality of reference points to the point with the highest energy in a preset area around the reference point.
In a second aspect, an embodiment of the present invention provides an electronic map lane line correcting apparatus, including:
the acquisition module is used for acquiring a target image comprising a lane line;
a first determining module, configured to determine a reference line according to an area occupied by the lane line in the target image, where the reference line is used to identify a center line of the lane line, and the reference line includes a plurality of reference points;
the second determining module is used for determining a point with highest energy in a preset area according to the energy distribution of the preset area around each reference point in the plurality of reference points;
and the correction module is used for correcting each reference point in the plurality of reference points to the point with the highest energy in the preset area around the reference point.
In a third aspect, an embodiment of the present invention provides an electronic map lane line correction apparatus, including:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to:
acquiring a target image including a lane line;
determining a reference line according to an area occupied by the lane line in the target image, wherein the reference line is used for identifying a central line of the lane line and comprises a plurality of reference points;
determining a point with highest energy in a preset area according to the energy distribution of the preset area around each reference point in the plurality of reference points;
and correcting each reference point in the plurality of reference points to the point with the highest energy in a preset area around the reference point.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the method of the first aspect.
The method, the device and the computer readable storage medium for correcting the lane line of the electronic map provided by the embodiment of the invention determine the reference line for identifying the center line of the lane line according to the area occupied by the lane line in the target image, wherein the reference line may deviate from the real center line of the lane line, and further determine the point with the highest energy in the preset area according to the energy distribution of the preset area around each reference point on the reference line, because the point with the highest energy in the preset area is the point on the center line of the lane line, or the point with the highest energy in the preset area is the point close to the center line of the lane line, each reference point in a plurality of reference points is corrected to the point with the highest energy in the preset area around the reference point, so that the reference line is closer to the center line of the lane line, when the electronic map is generated according to the position information of the reference line, the accuracy of the position of the lane line in the electronic map can be improved.
Drawings
Fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present invention;
FIG. 2 is a schematic diagram of an image provided by an embodiment of the present invention;
fig. 3 is a flowchart of a lane line correction method of an electronic map according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of another image provided by an embodiment of the present invention;
fig. 5 is a flowchart of a lane line correction method of an electronic map according to another embodiment of the present invention;
FIG. 6 is a schematic diagram of another image provided by an embodiment of the present invention;
fig. 7 is a flowchart of a lane line correction method of an electronic map according to another embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic map lane line correction apparatus according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic map lane line correction apparatus according to an embodiment of the present invention.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The lane line correction method of the electronic map can be applied to the application scene shown in figure 1. As shown in fig. 1, before generating an electronic map, such as a high-precision road map, it is necessary to collect relevant information of lanes, and one implementation manner is: a camera device, which may be a camera, and a detection device, which may be in particular a radar and/or laser detection device, are provided in the vehicle 11. In the driving process of the vehicle 11, the camera collects image information of the lane in real time, and meanwhile, the radar and/or the laser detection equipment detects three-dimensional point cloud of the lane in real time. For example, the image information of the lane collected by the camera includes the speed-limiting sign 12 beside the lane and the lane line 13 in the lane, and the radar and/or laser detection device can also detect the three-dimensional point cloud corresponding to the speed-limiting sign 12 and the lane line 13. When the device for generating a high-precision road map, such as a computer, a server, a terminal device, and the like, acquires the image information of the lane collected by the camera and the three-dimensional point cloud of the lane detected by the radar and/or laser detection device, the speed limit information of the lane can be determined according to the speed limit sign 12 in the image information collected by the camera, the position information of the lane line 13 can be determined according to the three-dimensional point cloud of the lane line 13 detected by the radar and/or laser detection device, and further, the high-precision road map corresponding to the lane can be generated according to the speed limit information of the lane and the position information of the lane line 13. Here, the present invention is only illustrative, and the high-precision road map corresponding to the lane may be generated based on not only the speed limit information of the lane and the position information of the lane line 13 but also more lane information.
In an actual lane, a lane line is an area having a certain width, for example, 15cm, in the lane, and the lane line may be a solid line area or a dotted line area, taking the dotted line area as an example, as shown in fig. 2, in an image 21 acquired by a camera, the lane line corresponds to one area 22, and when generating a high-precision road map, it is necessary to use position information of a center line of the lane line as position information of the lane line, that is, it is necessary to determine position information of a center line of the lane line, for example, position information of a center line 23 of the area 22. Because position information of lane lines in a large number of lanes needs to be determined when a high-precision road map is generated, in order to improve the generation efficiency, the center line of the lane lines in an image is labeled manually, the center line of the lane lines labeled manually is used as a sample, and a machine learning mode is adopted to train a neural network model, so that the trained neural network model can identify the center line of the lane lines. When image information of a large number of lane lines is acquired, the center line of the lane line is identified by the trained neural network model. However, the center line of the lane line marked manually may not be accurate, that is, the center line of the lane line marked manually may deviate from the true center line of the lane line, so that the trained neural network model is not accurate, the center line of the lane line identified by the neural network model deviates more from the true center line of the lane line, and finally the generated high-precision road map is not accurate enough. In order to solve the problem, an embodiment of the present application provides a lane line correction method for an electronic map, and the method is described below with reference to a specific embodiment.
The following describes the technical solutions of the present invention and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Fig. 3 is a flowchart of a lane line correction method of an electronic map according to an embodiment of the present invention. The embodiment of the invention provides a lane line correction method of an electronic map aiming at the technical problems in the prior art, which comprises the following specific steps:
Optionally, the acquiring a target image including a lane line includes: acquiring a three-dimensional point cloud of a lane including the lane line detected by a detection device; converting the three-dimensional point cloud into a two-dimensional point cloud; and determining the target image according to the two-dimensional point cloud. The detection device comprises at least one of the following: radar, laser detection equipment.
For example, an apparatus for generating a high-precision road map, such as a server, may obtain a three-dimensional point cloud of a lane line 13 detected by a detection device in a vehicle 11 as shown in fig. 1, for example, the vehicle 11 sends the three-dimensional point cloud of the lane line 13 detected by the detection device to the server in real time, the server further converts the three-dimensional point cloud into a two-dimensional point cloud, and fuses the two-dimensional point cloud into a base map, where the base map is recorded as a target image, and the lane line 13 occupies a certain area in the target image.
Optionally, the determining a reference line according to the area occupied by the lane line in the target image includes: and determining the reference line by adopting machine learning according to the area occupied by the lane line in the target image.
As shown in fig. 4, 41 represents the target image, and 42 represents the area occupied by the lane line in the target image 41, which is only schematically illustrated here and does not limit the shape of the area 42 occupied by the lane line in the target image 41, and in some embodiments, the area 42 occupied by the lane line may be irregular in shape. The center line of the lane line can be determined by machine learning from the area 42 occupied by the lane line in the target image 41, and the center line of the lane line determined by machine learning is referred to as a reference line, such as a reference line 43 shown in fig. 4. Since the center line of the lane line determined by machine learning is not the true center line of the lane line, the reference line 43 may deviate from the true center line of the lane line, that is, the center line of the area 42. It will be appreciated that the line is made up of an infinite number of points, and that similarly, the reference line 43 is made up of an infinite number of reference points, i.e., the points on the reference line 43 are the reference points. As shown in fig. 4, 44, 45, and 46 each represent an arbitrary reference point on the reference line 43.
Taking the reference point 44 as an example, a preset area is determined around the reference point 44, the size of the preset area may be preset, and optionally, the preset area at least includes a part of the upper edge and a part of the lower edge of the lane line, such as the preset area 47 shown in fig. 4, where the position of the reference point 44 in the preset area 47 is not limited, further, the preset area 47 is converted into an energy map, the brightness of different positions in the energy map is different, and a higher brightness indicates a higher energy, and optionally, the brightness of the area occupied by the lane line in the energy map is higher than that of other parts, that is, in the preset area 47, the brightness of the area 48 where the preset area 47 and the area 42 coincide is higher than that of the rest part of the preset area 47. In addition, the brightness is different at different positions inside the region 48, and alternatively, the brightness is highest at the position of the center line 49 of the region 48, and is lower at the portions inside the region 48 closer to the upper and lower edges of the region 42, that is, the brightness gradually decreases from the center line 49 of the region 48 in the direction indicated by the arrow. Therefore, the point on the center line 49 of the area 48 is the brightest point in the preset area 47, i.e., the point having the highest energy.
Similarly, the brightest point in the preset area around the other reference point on the reference line 43, such as the reference point 45 or the reference point 46, may be determined, and the detailed process is not described herein again.
And step 304, correcting each reference point in the plurality of reference points to a point with highest energy in a preset area around the reference point.
As shown in fig. 4, a point on the center line 49 of the area 48 is the brightest point in the preset area 47, and the brightest point in the preset area 47 may be a point on the center line of the lane line, or the brightest point in the preset area 47 may be a point close to the center line of the lane line. And the reference point 44 is a point on the reference line 43, the reference line 43 being used to identify the centerline of the lane line, but the reference line 43 being offset from the centerline of the lane line, that is, the reference point 44 being offset from the centerline of the lane line. After determining the brightest point in the preset area 47, the reference point 44 may be corrected to the brightest point in the preset area 47, that is, the reference point 44 may be corrected to any point on the center line 49 of the area 48 such that the reference point 44 falls on the center line of the lane line, or such that the reference point 44 is close to the center line of the lane line.
The embodiment of the invention determines the reference line for marking the central line of the lane line through the area occupied by the lane line in the target image, the reference line may deviate from the true center line of the lane line, and further, the energy distribution of a preset area around each reference point on the reference line is used to determine the point with the highest energy in the preset area, since the point with the highest energy in the preset area is the point on the center line of the lane line, or, the point of highest energy in the preset area is a point close to the center line of the lane line, and therefore, each of the plurality of reference points is corrected to the point of highest energy in the preset area around the reference point, so that the reference line is closer to the center line of the lane line, when the electronic map is generated according to the position information of the reference line, the accuracy of the position of the lane line in the electronic map can be improved.
Fig. 5 is a flowchart of a lane line correction method of an electronic map according to another embodiment of the present invention. On the basis of the above embodiment, the method for correcting the lane line of the electronic map specifically includes the following steps:
Step 501 is consistent with the implementation and principle of step 301, and is not described herein again.
Step 502 is consistent with the implementation and principle of step 302, and is not described herein again.
As shown in fig. 6, on the basis of fig. 4, when determining the preset region 47 around the reference point 44, the preset region 47 centered on the reference point 44 may be determined with the reference point 44 as the center, that is, the reference point 44 is the center of the preset region 47.
The method for determining the brightest point in the preset region 47 according to the energy distribution of the preset region 47 is the same as the method described in the above embodiment, and is not described herein again.
And 505, correcting each reference point in the plurality of reference points to the brightest point in a preset area around the reference point.
For example, reference point 44 is corrected to any point on centerline 49 of area 48 such that reference point 44 falls on the centerline of the lane line, or such that reference point 44 is close to the centerline of the lane line. Similarly, other reference points on the reference line, such as the reference point 45 or the reference point 46, are corrected to the center line of the lane line, or to a position close to the center line of the lane line.
The embodiment of the invention determines the reference line for marking the central line of the lane line through the area occupied by the lane line in the target image, the reference line may deviate from the true center line of the lane line, and further, the energy distribution of a preset area around each reference point on the reference line is used to determine the point with the highest energy in the preset area, since the point with the highest energy in the preset area is the point on the center line of the lane line, or, the point of highest energy in the preset area is a point close to the center line of the lane line, and therefore, each of the plurality of reference points is corrected to the point of highest energy in the preset area around the reference point, so that the reference line is closer to the center line of the lane line, when the electronic map is generated according to the position information of the reference line, the accuracy of the position of the lane line in the electronic map can be improved.
Fig. 7 is a flowchart of a lane line correction method of an electronic map according to another embodiment of the present invention. On the basis of the above embodiment, the method for correcting the lane line of the electronic map specifically includes the following steps:
The implementation manner and principle of step 701 are the same as those of step 301, and are not described herein again.
Step 702 is consistent with the implementation and principle of step 302, and is not described herein again.
And 703, determining the energy distribution of a preset area around the reference point by taking each reference point in the plurality of reference points as a center.
The implementation and principle of step 703 are the same as those of step 503, and are not described herein again.
As shown in fig. 6, the point on the center line 49 of the area 48 is the brightest point in the preset area 47, and further, the brightest point on the center line 49 of the area 48 closest to the reference point 44 can be determined based on the position information of the reference point 44 and the position information of the point on the center line 49 of the area 48, and it can be understood that the brightest point on the center line 49 of the area 48 closest to the reference point 44 is the intersection 60 passing through the reference point 44 and the center line 49 in the vertical direction.
For example, reference point 44 would be corrected to the brightest point 60 on centerline 49 of region 48 that is closest to reference point 44.
The embodiment of the invention determines the reference line for marking the central line of the lane line through the area occupied by the lane line in the target image, the reference line may deviate from the true center line of the lane line, and further, the energy distribution of a preset area around each reference point on the reference line is used to determine the point with the highest energy in the preset area, since the point with the highest energy in the preset area is the point on the center line of the lane line, or, the point of highest energy in the preset area is a point close to the center line of the lane line, and therefore, each of the plurality of reference points is corrected to the point of highest energy in the preset area around the reference point, so that the reference line is closer to the center line of the lane line, when the electronic map is generated according to the position information of the reference line, the accuracy of the position of the lane line in the electronic map can be improved.
Fig. 8 is a schematic structural diagram of an electronic map lane line correction apparatus according to an embodiment of the present invention. The electronic map lane line correction device provided in the embodiment of the present invention may execute the processing procedure provided in the embodiment of the electronic map lane line correction method, and as shown in fig. 8, the electronic map lane line correction device 80 includes: an acquisition module 81, a first determination module 82, a second determination module 83, and a correction module 84; the acquiring module 81 is configured to acquire a target image including a lane line; the first determining module 82 is configured to determine a reference line according to an area occupied by the lane line in the target image, where the reference line is used to identify a center line of the lane line, and the reference line includes a plurality of reference points; the second determining module 83 is configured to determine, according to the energy distribution of a preset region around each of the plurality of reference points, a point in the preset region where energy is highest; the correction module 84 is configured to correct each of the plurality of reference points to a point with the highest energy in a preset area around the reference point.
Optionally, the second determining module 83 is specifically configured to: determining the energy distribution of a preset area around each reference point in the plurality of reference points as a center; determining the brightest point in a preset area according to the energy distribution of the preset area around the reference point; the modification module 84 is specifically configured to: and correcting each reference point in the plurality of reference points to the brightest point in a preset area around the reference point.
Optionally, the second determining module 83 is specifically configured to: determining the brightest point closest to the reference point in the preset area according to the position information of the reference point and the energy distribution of the preset area around the reference point; the modification module 84 is specifically configured to: and correcting each reference point in the plurality of reference points to the brightest point closest to the reference point in a preset area around the reference point.
Optionally, the first determining module 82 is specifically configured to: and determining the reference line by adopting machine learning according to the area occupied by the lane line in the target image.
Optionally, the obtaining module 81 is specifically configured to: acquiring a three-dimensional point cloud of a lane including the lane line detected by a detection device; converting the three-dimensional point cloud into a two-dimensional point cloud; and determining the target image according to the two-dimensional point cloud.
Optionally, the detection device includes at least one of: radar, laser detection equipment.
The lane line correction device of the electronic map shown in the embodiment of fig. 8 can be used to implement the technical solution of the above method embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
Fig. 9 is a schematic structural diagram of an electronic map lane line correction apparatus according to an embodiment of the present invention. The electronic map lane line correction device provided in the embodiment of the present invention may execute the processing procedure provided in the embodiment of the electronic map lane line correction method, and as shown in fig. 9, the electronic map lane line correction device 90 includes: memory 91, processor 92, computer programs and communications interface 93; wherein the computer program is stored in the memory 91 and is configured to be executed by the processor 92 for: acquiring a target image including a lane line; determining a reference line according to an area occupied by the lane line in the target image, wherein the reference line is used for identifying a central line of the lane line and comprises a plurality of reference points; determining a point with highest energy in a preset area according to the energy distribution of the preset area around each reference point in the plurality of reference points; and correcting each reference point in the plurality of reference points to the point with the highest energy in a preset area around the reference point.
Optionally, when determining, according to the energy distribution of the preset region around each of the plurality of reference points, a point with the highest energy in the preset region, the processor 92 is specifically configured to: determining the energy distribution of a preset area around each reference point in the plurality of reference points as a center; determining the brightest point in a preset area according to the energy distribution of the preset area around the reference point; the processor 92, when correcting each of the plurality of reference points to a point with the highest energy in a preset area around the reference point, is specifically configured to: and correcting each reference point in the plurality of reference points to the brightest point in a preset area around the reference point.
Optionally, when determining the brightest point in the preset region according to the energy distribution of the preset region around the reference point, the processor 92 is specifically configured to: determining the brightest point closest to the reference point in the preset area according to the position information of the reference point and the energy distribution of the preset area around the reference point; the processor 92, when correcting each of the plurality of reference points to a brightest point in a preset area around the reference point, is specifically configured to: and correcting each reference point in the plurality of reference points to the brightest point closest to the reference point in a preset area around the reference point.
Optionally, when determining the reference line according to the area occupied by the lane line in the target image, the processor 92 is specifically configured to: and determining the reference line by adopting machine learning according to the area occupied by the lane line in the target image.
Optionally, when the processor 92 acquires the target image including the lane line, it is specifically configured to: acquiring a three-dimensional point cloud of a lane including the lane line detected by a detection device; converting the three-dimensional point cloud into a two-dimensional point cloud; and determining the target image according to the two-dimensional point cloud. In some embodiments, the processor 92 may receive the three-dimensional point cloud transmitted by the detection device through the communication interface 93.
Optionally, the detection device includes at least one of: radar, laser detection equipment.
The lane line correction device of the electronic map shown in the embodiment of fig. 9 can be used to implement the technical solution of the above method embodiment, and the implementation principle and technical effect are similar, which are not described herein again.
In addition, the present embodiment also provides a computer-readable storage medium on which a computer program is stored, the computer program being executed by a processor to implement the electronic map lane line correction method described in the above embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the device described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (13)
1. A lane line correction method of an electronic map is characterized by comprising the following steps:
acquiring a target image including a lane line;
determining a reference line according to an area occupied by the lane line in the target image, wherein the reference line is used for identifying a central line of the lane line and comprises a plurality of reference points;
determining a point with highest energy in a preset area according to energy distribution of the preset area around each reference point in the plurality of reference points, wherein the point with highest energy is a point on the central line of the lane line or a point close to the central line of the lane line;
and correcting each reference point in the plurality of reference points to the point with the highest energy in a preset area around the reference point.
2. The method of claim 1, wherein determining a point in a predetermined area around each of the plurality of reference points where energy is highest according to an energy distribution of the predetermined area comprises:
determining the energy distribution of a preset area around each reference point in the plurality of reference points as a center;
determining the brightest point in a preset area according to the energy distribution of the preset area around the reference point;
the correcting each reference point in the plurality of reference points to a point with the highest energy in a preset area around the reference point comprises:
and correcting each reference point in the plurality of reference points to the brightest point in a preset area around the reference point.
3. The method of claim 2, wherein the determining the brightest point in a predetermined area around the reference point according to the energy distribution of the predetermined area comprises:
determining the brightest point closest to the reference point in the preset area according to the position information of the reference point and the energy distribution of the preset area around the reference point;
the correcting each of the plurality of reference points to a brightest point in a preset area around the reference point includes:
and correcting each reference point in the plurality of reference points to the brightest point closest to the reference point in a preset area around the reference point.
4. The method according to any one of claims 1 to 3, wherein the determining a reference line according to the area occupied by the lane line in the target image comprises:
and determining the reference line by adopting machine learning according to the area occupied by the lane line in the target image.
5. The method of claim 1, wherein the obtaining the target image including the lane line comprises:
acquiring a three-dimensional point cloud of a lane including the lane line detected by a detection device;
converting the three-dimensional point cloud into a two-dimensional point cloud;
and determining the target image according to the two-dimensional point cloud.
6. The method of claim 5, wherein the detection device comprises at least one of:
radar, laser detection equipment.
7. An electronic map lane line correction apparatus, characterized by comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to:
acquiring a target image including a lane line;
determining a reference line according to an area occupied by the lane line in the target image, wherein the reference line is used for identifying a central line of the lane line and comprises a plurality of reference points;
determining a point with highest energy in a preset area according to energy distribution of the preset area around each reference point in the plurality of reference points, wherein the point with highest energy is a point on the central line of the lane line or a point close to the central line of the lane line;
and correcting each reference point in the plurality of reference points to the point with the highest energy in a preset area around the reference point.
8. The apparatus according to claim 7, wherein the processor, when determining a point in a predetermined area around each of the plurality of reference points where energy is highest according to an energy distribution of the predetermined area, is specifically configured to:
determining the energy distribution of a preset area around each reference point in the plurality of reference points as a center;
determining the brightest point in a preset area according to the energy distribution of the preset area around the reference point;
when the processor corrects each of the plurality of reference points to a point with the highest energy in a preset area around the reference point, the processor is specifically configured to:
and correcting each reference point in the plurality of reference points to the brightest point in a preset area around the reference point.
9. The apparatus according to claim 8, wherein the processor, when determining the brightest point in a preset area around the reference point according to the energy distribution of the preset area, is specifically configured to:
determining the brightest point closest to the reference point in the preset area according to the position information of the reference point and the energy distribution of the preset area around the reference point;
the processor, when correcting each of the plurality of reference points to a brightest point in a preset area around the reference point, is specifically configured to:
and correcting each reference point in the plurality of reference points to the brightest point closest to the reference point in a preset area around the reference point.
10. The apparatus according to any one of claims 7 to 9, wherein the processor, when determining the reference line based on the area occupied by the lane line in the target image, is specifically configured to:
and determining the reference line by adopting machine learning according to the area occupied by the lane line in the target image.
11. The apparatus according to claim 7, wherein the processor, when acquiring the target image including the lane line, is specifically configured to:
acquiring a three-dimensional point cloud of a lane including the lane line detected by a detection device;
converting the three-dimensional point cloud into a two-dimensional point cloud;
and determining the target image according to the two-dimensional point cloud.
12. The electronic map lane line correction apparatus of claim 11, wherein the detection apparatus comprises at least one of:
radar, laser detection equipment.
13. A computer-readable storage medium, having stored thereon a computer program for execution by a processor to perform the method of any one of claims 1-6.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110673855.0A CN113408407B (en) | 2018-10-31 | 2018-10-31 | Electronic map lane line correction method, electronic map lane line correction equipment and computer readable storage medium |
| CN201811286291.XA CN109636820B (en) | 2018-10-31 | 2018-10-31 | Electronic map lane line correction method, device and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811286291.XA CN109636820B (en) | 2018-10-31 | 2018-10-31 | Electronic map lane line correction method, device and computer readable storage medium |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110673855.0A Division CN113408407B (en) | 2018-10-31 | 2018-10-31 | Electronic map lane line correction method, electronic map lane line correction equipment and computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109636820A CN109636820A (en) | 2019-04-16 |
| CN109636820B true CN109636820B (en) | 2021-07-06 |
Family
ID=66067023
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811286291.XA Active CN109636820B (en) | 2018-10-31 | 2018-10-31 | Electronic map lane line correction method, device and computer readable storage medium |
| CN202110673855.0A Active CN113408407B (en) | 2018-10-31 | 2018-10-31 | Electronic map lane line correction method, electronic map lane line correction equipment and computer readable storage medium |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110673855.0A Active CN113408407B (en) | 2018-10-31 | 2018-10-31 | Electronic map lane line correction method, electronic map lane line correction equipment and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN109636820B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110120084A (en) * | 2019-05-23 | 2019-08-13 | 广东星舆科技有限公司 | A method of generating lane line and road surface |
| CN110530398B (en) * | 2019-08-30 | 2021-02-02 | 北京三快在线科技有限公司 | Method and device for detecting precision of electronic map |
| CN110539306B (en) * | 2019-09-06 | 2021-06-22 | 广东利元亨智能装备股份有限公司 | Workpiece bonding method and device |
| CN113160396B (en) * | 2020-01-23 | 2024-03-29 | 宝马股份公司 | Method for establishing map model, method for generating real-time map and map system |
| CN114705180B (en) * | 2022-06-06 | 2023-03-24 | 中汽创智科技有限公司 | Data correction method, device and equipment for high-precision map and storage medium |
| CN114724379A (en) * | 2022-06-08 | 2022-07-08 | 中汽创智科技有限公司 | Data processing method, device, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605977A (en) * | 2013-11-05 | 2014-02-26 | 奇瑞汽车股份有限公司 | Extracting method of lane line and device thereof |
| CN105528588A (en) * | 2015-12-31 | 2016-04-27 | 百度在线网络技术（北京）有限公司 | Lane line recognition method and device |
| CN106570446A (en) * | 2015-10-12 | 2017-04-19 | 腾讯科技（深圳）有限公司 | Lane line extraction method and device |
| CN107341453A (en) * | 2017-06-20 | 2017-11-10 | 北京建筑大学 | A kind of lane line extracting method and device |
| CN108470159A (en) * | 2018-03-09 | 2018-08-31 | 腾讯科技（深圳）有限公司 | Lane line data processing method, device, computer equipment and storage medium |
Family Cites Families (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4374211B2 (en) * | 2002-08-27 | 2009-12-02 | クラリオン株式会社 | Lane marker position detection method, lane marker position detection device, and lane departure warning device |
| JP2007104171A (en) * | 2005-10-03 | 2007-04-19 | Omron Corp | Front monitoring device |
| JP2007128141A (en) * | 2005-11-01 | 2007-05-24 | Hitachi Software Eng Co Ltd | System and method for determining road lane number in road image |
| CN102314599A (en) * | 2011-10-11 | 2012-01-11 | 东华大学 | Identification and deviation-detection method for lane |
| JP5733196B2 (en) * | 2011-12-22 | 2015-06-10 | アイシン・エィ・ダブリュ株式会社 | Lane guidance display system, method and program |
| JP5792678B2 (en) * | 2012-06-01 | 2015-10-14 | 株式会社日本自動車部品総合研究所 | Lane boundary detection device and program |
| CN103903019B (en) * | 2014-04-11 | 2017-12-15 | 北京工业大学 | A kind of automatic generation method of multilane track of vehicle space-time diagram |
| US9721471B2 (en) * | 2014-12-16 | 2017-08-01 | Here Global B.V. | Learning lanes from radar data |
| CN105260699B (en) * | 2015-09-10 | 2018-06-26 | 百度在线网络技术（北京）有限公司 | A kind of processing method and processing device of lane line data |
| CN105426861B (en) * | 2015-12-02 | 2019-05-21 | 百度在线网络技术（北京）有限公司 | Lane line determines method and device |
| US10102434B2 (en) * | 2015-12-22 | 2018-10-16 | Omnivision Technologies, Inc. | Lane detection system and method |
| CN105957182B (en) * | 2016-04-21 | 2018-08-03 | 深圳市元征科技股份有限公司 | A kind of method and device of rectilinear direction that correcting instruction vehicle traveling |
| TWI609807B (en) * | 2016-05-17 | 2018-01-01 | 緯創資通股份有限公司 | Image evaluation method and electronic apparatus thereof |
| CN106559658B (en) * | 2016-12-02 | 2017-08-04 | 郑州捷安高科股份有限公司 | Multi-channel projection fusion band color balance Control Scheme method |
| US10762776B2 (en) * | 2016-12-21 | 2020-09-01 | Here Global B.V. | Method, apparatus, and computer program product for determining vehicle lane speed patterns based on received probe data |
| CN106740841B (en) * | 2017-02-14 | 2018-07-10 | 驭势科技（北京）有限公司 | Method for detecting lane lines, device and mobile unit based on dynamic control |
| CN107066986A (en) * | 2017-04-21 | 2017-08-18 | 哈尔滨工业大学 | A kind of lane line based on monocular vision and preceding object object detecting method |
-
2018
- 2018-10-31 CN CN201811286291.XA patent/CN109636820B/en active Active
- 2018-10-31 CN CN202110673855.0A patent/CN113408407B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103605977A (en) * | 2013-11-05 | 2014-02-26 | 奇瑞汽车股份有限公司 | Extracting method of lane line and device thereof |
| CN106570446A (en) * | 2015-10-12 | 2017-04-19 | 腾讯科技（深圳）有限公司 | Lane line extraction method and device |
| CN105528588A (en) * | 2015-12-31 | 2016-04-27 | 百度在线网络技术（北京）有限公司 | Lane line recognition method and device |
| CN107341453A (en) * | 2017-06-20 | 2017-11-10 | 北京建筑大学 | A kind of lane line extracting method and device |
| CN108470159A (en) * | 2018-03-09 | 2018-08-31 | 腾讯科技（深圳）有限公司 | Lane line data processing method, device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113408407A (en) | 2021-09-17 |
| CN109636820A (en) | 2019-04-16 |
| CN113408407B (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109636820B (en) | Electronic map lane line correction method, device and computer readable storage medium | |
| CN108694882B (en) | Method, device and equipment for labeling map | |
| CN109584706B (en) | Electronic map lane line processing method, device and computer readable storage medium | |
| US20180018528A1 (en) | Detecting method and device of obstacles based on disparity map and automobile driving assistance system | |
| CN101877174B (en) | Vehicle speed measurement method, supervisory computer and vehicle speed measurement system | |
| CN111179358A (en) | Calibration method, device, equipment and storage medium | |
| EP2975555A1 (en) | Method and apparatus for displaying point of interest | |
| CN105184778A (en) | Detection method and apparatus | |
| CN104089628B (en) | Self-adaption geometric calibration method of light field camera | |
| CN112489126B (en) | Vehicle key point information detection method, vehicle control method and device and vehicle | |
| US20150117753A1 (en) | Computing device and method for debugging computerized numerical control machine | |
| WO2021051344A1 (en) | Method and apparatus for determining lane lines in high-precision map | |
| CN110774319B (en) | Robot and positioning method and device thereof | |
| CN110148099B (en) | Projection relation correction method and device, electronic equipment and computer readable medium | |
| CN112348737B (en) | Method for generating simulation image, electronic device and storage medium | |
| CN112836558A (en) | Mechanical arm tail end adjusting method, device, system, equipment and medium | |
| CN111444810A (en) | Traffic light information identification method, device, equipment and storage medium | |
| CN113255578B (en) | Traffic identification recognition method and device, electronic equipment and storage medium | |
| CN112784639A (en) | Intersection detection, neural network training and intelligent driving method, device and equipment | |
| CN110751040A (en) | Three-dimensional object detection method and device, electronic equipment and storage medium | |
| CN111598956A (en) | Calibration method, device and system | |
| CN110827340B (en) | Map updating method, device and storage medium | |
| CN116597246A (en) | Model training method, target detection method, electronic device and storage medium | |
| CN115019268A (en) | Lane line detection method and device, computer equipment and storage medium | |
| CN111401365B (en) | OCR image automatic generation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |