CN112800153B - Isolation belt information mining method, device, equipment and computer storage medium - Google Patents
Isolation belt information mining method, device, equipment and computer storage medium Download PDFInfo
- Publication number
- CN112800153B CN112800153B CN201911112083.2A CN201911112083A CN112800153B CN 112800153 B CN112800153 B CN 112800153B CN 201911112083 A CN201911112083 A CN 201911112083A CN 112800153 B CN112800153 B CN 112800153B
- Authority
- CN
- China
- Prior art keywords
- road
- travel track
- image
- detected
- 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
- 238000002955 isolation Methods 0.000 title claims abstract description 138
- 238000000034 method Methods 0.000 title claims abstract description 66
- 238000005065 mining Methods 0.000 title claims abstract description 26
- 238000005070 sampling Methods 0.000 claims abstract description 47
- 238000012549 training Methods 0.000 claims description 29
- 238000013145 classification model Methods 0.000 claims description 27
- 230000004888 barrier function Effects 0.000 claims description 20
- 230000015654 memory Effects 0.000 claims description 19
- 238000002372 labelling Methods 0.000 claims description 12
- 238000004891 communication Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000003993 interaction Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 230000002457 bidirectional effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000004883 computer application Methods 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000001151 other effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The application discloses a method, a device, equipment and a computer storage medium for mining isolation belt information, and relates to the field of big data. The specific implementation scheme is as follows: acquiring travel track data of a user on a road to be detected within a set time, wherein the travel track comprises a first type travel track and a second type travel track; determining an overlapping area of the first travel track and the second travel track; determining the ratio of the number of sampling points of the first type travel track to the total number of sampling points of the first type travel track in the overlapping area; and determining whether a physical isolation belt exists on the road based on the ratio. According to the application, whether the road contains the isolation belt or not is automatically excavated by utilizing the travel track data of the user on the road, and compared with a manual acquisition mode, the labor cost and the time cost are reduced.
Description
Technical Field
The application relates to the technical field of computer application, in particular to a method, a device, equipment and a computer storage medium for mining isolation belt information in the field of big data.
Background
Riding users in road travel are weak groups compared with driving users, and when accidents occur between motor vehicles and bicycles, the bicycles are often seriously damaged, and the riding users are injured in a high probability. Related researches show that the physical isolation belt on the road can greatly reduce the accident rate and improve the safety of riding users. Based on similar theory, the bidirectional lane isolation belt in the motor vehicle lane can also improve the safety of driving users.
Therefore, if the isolation belt information can be integrated in the path planning process, the trip safety of the user can be greatly improved. The premise of realizing the method is that whether the road contains the isolation belt or not needs to be known. In the prior art, the acquisition mode of road isolation belt information mainly relies on manual acquisition, and this mode can consume a large amount of manpower and time cost, and timeliness is poor.
Disclosure of Invention
In view of this, the present application provides a method, apparatus, device, and computer storage medium for mining information of an isolation belt, so as to reduce the labor and time costs for acquiring the information of the isolation belt.
In a first aspect, the present application provides a method for mining information of an isolation belt, the method comprising:
Acquiring travel track data of a user on a road to be detected within a set time, wherein the travel track comprises a first type travel track and a second type travel track;
determining an overlapping area of the first travel track and the second travel track;
determining the ratio of the number of sampling points of the first type travel track to the total number of sampling points of the first type travel track in the overlapping area;
and determining whether a physical isolation belt exists on the road based on the ratio.
According to a preferred embodiment of the application, the method further comprises:
and storing the information of whether the physical isolation zone exists on the road in a map database.
According to a preferred embodiment of the present application, the determining the overlapping area of the first type of travel track and the second type of travel track includes:
determining the distance range of a first travel track sampling point with a preset proportion from the road center;
determining the distance range of a second class travel track sampling point with a preset proportion from the road center;
and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
According to a preferred embodiment of the present application, determining whether a physical barrier exists on the road based on the ratio comprises:
If the ratio is greater than or equal to a preset first duty ratio threshold, determining that no physical isolation belt exists on the road;
if the ratio is smaller than or equal to a preset second duty ratio threshold value, determining that a physical isolation zone exists on the road;
wherein the second duty cycle threshold is less than the first duty cycle threshold.
According to a preferred embodiment of the application, the method further comprises:
and if the ratio is larger than the second duty ratio threshold and smaller than the first duty ratio threshold, determining whether a physical isolation zone exists on the road by utilizing the image data of the road to be detected.
According to a preferred embodiment of the application, the method further comprises:
if the total number of the sampling points of the first travel track or the total number of the sampling points of the second travel track is smaller than a preset number threshold, determining whether a physical isolation zone exists on the road by utilizing the image data of the road to be detected.
According to a preferred embodiment of the present application, determining whether a physical barrier exists on the road using the image data of the road to be detected includes:
inputting the image of the road to be detected into an image classification model obtained by training in advance;
And determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected.
According to a preferred embodiment of the present application, the image classification model is pre-trained by:
acquiring a training sample, wherein the training sample comprises a road image and a manual labeling result of whether the road image contains a physical isolation belt or not;
and taking the road image as input of a depth residual error network, taking a manual labeling result corresponding to the road image as an output target of the depth residual error network, and training the depth residual error network to obtain an image classification model.
According to a preferred embodiment of the present application, determining whether a physical isolation zone exists on the road according to the classification result of the image of the road to be detected by the image classification model includes:
acquiring classification results of the image classification model on a plurality of images of the road to be detected;
if the classification result is that the number of the images with the physical isolation bands reaches a preset number threshold, or if the classification result is that the proportion of the number of the images with the physical isolation bands to the number of the images of the road to be detected exceeds a preset proportion threshold, determining that the physical isolation bands exist on the road to be detected.
According to a preferred embodiment of the present application, the first travel track and the second travel track are respectively a user riding track and a driving track in the same direction; or alternatively, the process may be performed,
the first travel track and the second travel track are driving tracks in opposite directions respectively.
In a second aspect, the present application also provides an apparatus for mining information on an isolation belt, the apparatus comprising:
the track acquisition unit is used for acquiring distribution data of user travel tracks on a road to be detected within a set time, wherein the user travel tracks comprise a first travel track and a second travel track;
the area determining unit is used for determining an overlapping area of the first travel track and the second travel track;
the first excavating unit is used for determining the ratio of the number of sampling points of the first type of travel tracks to the total number of sampling points of the first type of travel tracks in the overlapping area; and determining whether a physical isolation belt exists on the road based on the ratio.
According to a preferred embodiment of the application, the device further comprises:
and the information storage unit is used for storing the information of whether the physical isolation belt exists on the road in the map database.
According to a preferred embodiment of the present application, the area determining unit specifically performs:
Determining the distance range of a first travel track sampling point with a preset proportion from the road center;
determining the distance range of a second class travel track sampling point with a preset proportion from the road center;
and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
According to a preferred embodiment of the present application, the first digging unit specifically performs:
if the ratio is greater than or equal to a preset first duty ratio threshold, determining that no physical isolation belt exists on the road;
if the ratio is smaller than or equal to a preset second duty ratio threshold value, determining that a physical isolation zone exists on the road;
wherein the second duty cycle threshold is less than the first duty cycle threshold.
According to a preferred embodiment of the application, the device further comprises:
and the second digging unit is used for determining whether a physical isolation zone exists on the road by utilizing the image data of the road to be detected if the ratio is larger than a second duty ratio threshold value and smaller than a first duty ratio threshold value.
According to a preferred embodiment of the application, the device further comprises:
and the second mining unit is used for determining whether a physical isolation zone exists on the road by utilizing the image data of the road to be detected if the total number of the first travel track sampling points or the total number of the second travel track sampling points is smaller than a preset number threshold.
According to a preferred embodiment of the present application, the second mining unit, when determining whether a physical isolation zone exists on the road using the image data of the road to be detected, specifically performs:
inputting the image of the road to be detected into an image classification model obtained by training in advance;
and determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected.
According to a preferred embodiment of the application, the device further comprises:
the model training unit is used for acquiring a training sample, wherein the training sample comprises a road image and a manual labeling result of whether the road image contains a physical isolation belt or not; and taking the road image as input of a depth residual error network, taking a manual labeling result corresponding to the road image as an output target of the depth residual error network, and training the depth residual error network to obtain an image classification model.
In a third aspect, the present application also provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.
In a fourth aspect, the application also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method as described above.
In a fifth aspect, the application also provides a computer program product comprising a computer program which, when executed by a processor, implements a method according to any of the preceding claims.
According to the technical scheme, the technical scheme provided by the application has the following advantages:
1) According to the application, whether the road contains the isolation belt or not is automatically excavated by utilizing the travel track data of the user on the road, and compared with a manual acquisition mode, the labor cost and the time cost are reduced.
2) The application can excavate the isolation belt information by using the travel track data in the set time at intervals, thereby effectively ensuring the timeliness of acquiring the road isolation belt information.
3) The method of the application is used for storing the information of whether the physical isolation zone exists on the road in the map database, so that a data base can be provided for map services, and the method can be used for path planning to improve the travel safety of users.
4) Under the condition that the way of mining travel track data cannot be accurately determined, the way of identifying and classifying road images can be combined to assist in determining whether a physical isolation zone exists on a road.
Other effects of the above alternative will be described below in connection with specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present application and are not to be construed as limiting the application. Wherein:
FIG. 1 is an exemplary system architecture diagram of a method or apparatus for mining information of an isolation strip according to an embodiment of the present application;
FIG. 2 is a flow chart of a method according to a first embodiment of the present application;
FIG. 3 is a flow chart of a method according to a second embodiment of the present application;
fig. 4 is a schematic diagram of an excavating device for isolation belt information according to an embodiment of the present application;
fig. 5 is a block diagram of an electronic device for implementing a method of route planning according to an embodiment of the application.
Detailed Description
Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 illustrates an exemplary system architecture to which the isolation zone information mining method or apparatus of the embodiment of the present application may be applied.
As shown in fig. 1, the system architecture may include terminal devices 101 and 102, a network 103, and a server 104. The network 103 is the medium used to provide communication links between the terminal devices 101, 102 and the server 104. The network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with server 104 through network 103 using terminal devices 101 and 102. Various applications, such as a map-type application, a voice interaction application, a web browser application, a communication-type application, and the like, may be installed on the terminal devices 101 and 102.
The terminal devices 101 and 102 may be various electronic devices. Including but not limited to smart phones, tablet computers, smart speakers, smart televisions, and the like. The route planning apparatus provided by the present application may be set and run in the terminal device 101 or 102, or may be set and run in the server 104. Which may be implemented as multiple software or software modules (e.g., to provide distributed services), or as a single software or software module, without limitation.
The server 104 may be a single server or a server group composed of a plurality of servers. The method for mining the isolation belt information in the embodiment of the application can be executed by the server 104, and the mining device for the isolation belt information in the server 104 can use the travel track data of the user to mine the isolation belt information of the road and store the isolation belt information of the road into the map database. The server 104 may provide services to the terminal device 101 or 102 based on data in the map database. It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The method and the system have the core idea that whether the road contains the isolation belt is mined by utilizing the travel track data of the user on the road. The method provided in the application is described in detail below with reference to examples.
Embodiment 1,
Fig. 2 is a flowchart of a method according to a first embodiment of the present application, and as shown in fig. 2, the method may include the following steps:
in 201, travel track data of a user on a road to be detected within a set time is acquired.
In the application, the user behavior log can be obtained from the database, and the travel track data of the user can be obtained from the user behavior log. Because each sampling point of the travel track is related to a specific position, for example, the longitude and latitude of each sampling point exist, the travel track data of the user can be mapped to each road.
The travel tracks used in the embodiment of the application can comprise a first travel track and a second travel track, and can also comprise travel tracks of other types. In the application, two travel tracks of different categories are distinguished by a first travel track and a second travel track, wherein the first travel track and the second travel track are just distinguished in terms of names, and do not contain limiting meanings such as quantity, sequence and the like. The travel track used may include, but is not limited to, the following two cases:
First case: the first travel track and the second travel track are tracks of travel categories corresponding to adjacent lanes in the same direction respectively.
For example, a driving track and a riding track respectively corresponding to a motor vehicle lane and a non-motor vehicle lane in the same direction. This may determine whether a physical barrier exists between the motor vehicle lane and the non-motor vehicle lane.
The physical isolation belt refers to an isolation belt used for isolating different lanes on a road, and can be in a green belt form, a guardrail form and the like. Which may be referred to by name as a barrier tape, barrier fence, barrier guardrail, etc.
Second case: the first travel track and the second travel track are tracks of travel categories corresponding to adjacent lanes in opposite directions respectively.
For example, driving tracks respectively corresponding to opposite-direction motor vehicle lanes. This can determine whether a physical barrier exists between two opposite motor vehicle lanes.
For another example, if a road does not allow the vehicle to travel, there may be a corresponding track of travel on opposite non-motor lanes. This can determine whether a physical barrier exists between the opposite non-motorized lanes.
In this step, each road to be detected whether the isolation belt information exists may be used as a road to be detected in the map database, respectively, so as to execute the procedure provided by the present application.
In 202, an overlap region of the first type of travel track and the second type of travel track is determined.
In the step, the distance range of the sampling points of the first travel track with the preset proportion from the road center is determined first. The road center can be used for determining a road center line as the road center according to the road shape, and also can be used for taking the median value, the mean value and the like of all the travel tracks on the road width as the road center according to the symmetry of all the travel tracks on the road.
And then determining the distance range of the second travel track sampling point with a preset proportion from the road center.
And taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
For example, if 90% of the driving track sampling points fall between 3 and 5 meters from the road center and 90% of the driving track sampling points fall between 4 and 8 meters from the road center on the right side of the road, then the range of 4 to 5 meters from the road center on the right side of the road is determined as the overlapping area.
In 203, a ratio of the number of sampling points of the first type of travel track to the total number of sampling points of the first type of travel track in the overlapping area is determined.
Continuing the previous example, determining the total number of the riding track sampling points on the right side of the road within the range of 4-5 meters from the center of the road, and determining the total number of the riding track sampling points on the right side of the road total Then determining the ratio sample of the two ratio 。
In 204, it is determined whether a physical barrier is present on the road to be detected based on the ratio.
Specifically, if the ratio determined in step 203 is greater than or equal to the preset first duty cycle threshold, i.e. if sample ratio ≥threshold no-exist Determining that no physical isolation belt exists on the road to be detected; if the ratio is smaller than or equal to the preset second duty ratio threshold, i.e. if sample ratio ≤threshold exist And determining that a physical isolation belt exists on the road to be detected.
Wherein the second duty cycle threshold value exist Less than a first duty cycle threshold no-exist . Second duty cycle threshold exist And a first duty cycle threshold no-exist The method can take a tested value or a test value, and the like, and can be controlled and adjusted according to the requirement of accuracy. For example, a second duty cycle threshold no-exist 60% may be taken, the second duty cycle threshold exist 40% can be taken.
In addition to this, there are some cases, such as threshold exist <sample ratio <threshold no-exist Or, sample total <threshold total In this case, whether or not the physical isolation belt is included is doubtful, and the situation can be further determined in combination with other means. For example, further in connection with road image based recognition techniques, which will be described in detail in embodiment two.
At 205, information of whether a physical barrier exists on a road is stored in a map database.
After each road is excavated whether a physical isolation zone exists, the physical isolation zone can be stored in a map database. The map data maintained at the server side contains road information. The road information may include road location information, name, length, road grade, etc., and in the embodiment of the present application, the road information further includes a status including a physical isolation zone. The condition in which the physical separation tape is contained may be such as whether the physical separation tape is contained or not, and the type of the physical separation tape may be further contained. The type of the physical isolation belt can be a physical isolation belt between motor vehicles, a physical isolation belt between motor vehicles and non-motor vehicles, and the like.
Embodiment II,
Fig. 3 is a flowchart of a method according to a second embodiment of the present application, as shown in fig. 3, the method may include the following steps:
Step 301 is the same as step 201 in the first embodiment, and will not be described here.
In 302, judging whether the total number of sampling points of the first type travel track or the total number of sampling points of the second type travel track on the road to be detected is smaller than a preset number threshold, if not, executing 303; if so, 308 is performed.
In this step, if the total sampling number of a certain travel track is smaller, the detection result of the isolation belt obtained based on the track may be considered to be less accurate, and the method based on road image recognition may be further combined, that is, the step 308 may be executed.
In 303, an overlapping area of the first type of travel track and the second type of travel track on the road to be detected is determined.
In 304, a ratio of the number of sampling points of the first type of travel track to the total number of sampling points of the first type of travel track in the overlapping area is determined.
Steps 303 to 304 are the same as steps 202 to 203 in the first embodiment, and are not described here.
In 305, comparing the determined ratio with a first duty ratio threshold and a second duty ratio threshold, and if the determined ratio is greater than or equal to the first duty ratio threshold, executing 306 to determine that no physical isolation zone exists on the road; if less than or equal to the second duty cycle threshold, then executing 307 to determine that a physical isolation zone exists on the road; if the ratio is between the second duty cycle threshold and the first duty cycle threshold, then 308 is performed.
At 308, an image of the road to be detected is acquired.
An image of the road to be detected is acquired from a map database. The application is not limited to the image sources of the roads to be detected in the map database, and the image sources can be shot by a camera of a traffic system, can be shot and uploaded by a user, and can be acquired from equipment such as a vehicle recorder and the like.
In 309, the image of the road to be detected is input into a pre-trained Resnet (Residual Neural Network, depth residual error network), and whether a physical isolation zone exists on the road to be detected is determined according to the classification result of the image of the road to be detected by the Resnet network.
In the embodiment of the application, the Resnet is actually used for classifying the road image, and the classification result is that a physical isolation belt exists or does not exist. And (3) the Resnet calculates a probability value of whether the physical isolation belt exists or not for the road image, if the probability value exceeds a preset probability threshold value, the isolation belt is considered to exist, and otherwise, the isolation belt is considered to not exist.
The Resnet network may be pre-trained as follows:
and acquiring a training sample, wherein the training sample comprises a road image and a manual labeling result of whether the road image contains a physical isolation belt. For example, some road images including physical isolation strips are acquired as positive samples, and road images not including physical isolation strips are acquired as negative samples.
And taking the road image as input of a depth residual error network, taking a manual labeling result corresponding to the road image as an output target of the depth residual error network, and training the depth residual error network.
When determining whether a physical isolation zone exists on the road to be detected according to the classification result of the image of the road to be detected by the Resnet network, if only one image of the road to be detected exists, directly determining whether the physical isolation zone exists on the road to be detected according to the classification result of the image by the Resnet network.
If a plurality of images of the road to be detected exist, a classification result of the Resnet network on the images is obtained, if the classification result shows that the number of the images with the physical isolation zones reaches a preset number threshold, or if the classification result shows that the proportion of the number of the images with the physical isolation zones to the number of the images of the road to be detected exceeds a preset proportion threshold, the physical isolation zones of the road to be detected are determined. For example, if the classification result of all 3 images is that there is a physical isolation zone after classifying 5 images of one road, it is determined that the road has a physical isolation zone.
In this embodiment, the image classification model is described by taking a description of the image classification model obtained by training the Resnet network as an example, but other types of convolution application networks such as VGG (Visual Geometry Group Network ), alexnet and the like can be used for training the image classification model besides the Resnet network.
The method is preferable because the method based on the track overlapping degree has low cost and high timeliness. However, since the method based on road image recognition is high in accuracy but low in cost and timeliness, the method is used as an auxiliary mining method to perform image recognition only on roads which cannot be accurately recognized by the mining method based on the track overlapping degree.
The foregoing describes the method provided by the embodiment of the present application in detail, and the following describes the apparatus provided by the embodiment of the present application in detail.
Third embodiment,
Fig. 4 is a schematic diagram of an excavating device for isolation belt information according to an embodiment of the present application, as shown in fig. 4, the device may include: the trajectory acquisition unit 01, the region determination unit 02, and the first mining unit 03 may further include: an information storage unit 04, a second mining unit 05 and a model training unit 06. Wherein, the main functions of each constituent unit are as follows:
the track acquisition unit 01 is used for acquiring distribution data of user travel tracks on a road to be detected within a set time, wherein the user travel tracks comprise a first travel track and a second travel track.
The travel track used may include, but is not limited to, the following two cases:
First case: the first travel track and the second travel track are tracks of travel categories corresponding to adjacent lanes in the same direction respectively.
For example, a driving track and a riding track respectively corresponding to a motor vehicle lane and a non-motor vehicle lane in the same direction. This may determine whether a physical barrier exists between the motor vehicle lane and the non-motor vehicle lane.
The physical isolation belt refers to an isolation belt used for isolating different lanes on a road, and can be in a green belt form, a guardrail form and the like. Which may be referred to by name as a barrier tape, barrier fence, barrier guardrail, etc.
Second case: the first travel track and the second travel track are tracks of travel categories corresponding to adjacent lanes in opposite directions respectively.
For example, driving tracks respectively corresponding to opposite-direction motor vehicle lanes. This can determine whether a physical barrier exists between two opposite motor vehicle lanes.
For another example, if a road does not allow the vehicle to travel, there may be a corresponding track of travel on opposite non-motor lanes. This can determine whether a physical barrier exists between the opposite non-motorized lanes.
And the area determining unit 02 is used for determining the overlapping area of the first travel track and the second travel track.
Specifically, the area determining unit 02 may determine a distance range of the first type travel track sampling point of a preset proportion from the road center; determining the distance range of a second class travel track sampling point with a preset proportion from the road center; and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as an overlapping area.
The road center can be used for determining a road center line as the road center according to the road shape, and also can be used for taking the median value, the mean value and the like of all the travel tracks on the road width as the road center according to the symmetry of all the travel tracks on the road.
The first digging unit 03 is configured to determine a ratio of the number of sampling points of the first type of travel track to the total number of sampling points of the first type of travel track in the overlapping area; and determining whether a physical isolation belt exists on the road based on the ratio.
And the information storage unit 04 is used for storing the information of whether the physical isolation belt exists on the road in the map database.
Specifically, if the ratio is greater than or equal to a preset first duty ratio threshold, the first excavation unit 03 determines that no physical isolation zone exists on the road; if the ratio is smaller than or equal to a preset second duty ratio threshold value, the first digging unit 03 determines that a physical isolation zone exists on the road; wherein the second duty cycle threshold is less than the first duty cycle threshold.
If the ratio is greater than the second duty ratio threshold and less than the first duty ratio threshold, the second mining unit 05 determines whether a physical isolation zone exists on the road by using the image data of the road to be detected.
If the total number of the sampling points of the first travel track or the total number of the sampling points of the second travel track is smaller than a preset number threshold, the second mining unit 05 determines whether a physical isolation zone exists on the road by using the image data of the road to be detected.
The second mining unit 05 may input an image of the road to be detected into a pre-trained image classification model when determining whether a physical isolation zone exists on the road by using image data of the road to be detected; and determining whether a physical isolation belt exists on the road according to the classification result of the image of the road to be detected by the image classification model.
The model training unit 06 obtains a training sample, wherein the training sample comprises a road image and a manual labeling result of whether the road image contains a physical isolation belt or not; and taking the road image as the input of the Resnet network, taking the artificial labeling result corresponding to the road image as the output target of the Resnet network, and training the Resnet network to obtain the image classification model.
Resnet is actually a classification of road images, with or without physical isolation zones. And (3) the Resnet calculates a probability value of whether the physical isolation belt exists or not for the road image, if the probability value exceeds a preset probability threshold value, the isolation belt is considered to exist, and otherwise, the isolation belt is considered to not exist.
When determining whether a physical isolation zone exists on the road to be detected according to the classification result of the image of the road to be detected by the Resnet network, if only one image of the road to be detected exists, directly determining whether the physical isolation zone exists on the road to be detected according to the classification result of the image by the Resnet network.
If a plurality of images of the road to be detected exist, a classification result of the Resnet network on the images is obtained, if the classification result shows that the number of the images with the physical isolation zones reaches a preset number threshold, or if the classification result shows that the proportion of the number of the images with the physical isolation zones to the number of the images of the road to be detected exceeds a preset proportion threshold, the physical isolation zones of the road to be detected are determined. For example, if the classification result of all 3 images is that there is a physical isolation zone after classifying 5 images of one road, it is determined that the road has a physical isolation zone.
In this embodiment, the image classification model is described by taking a description of the image classification model obtained by training the Resnet network as an example, but other types of convolution application networks such as a VGG network, an Alexnet network and the like can be adopted to train the image classification model besides the Resnet network.
After the isolation belt information of each road is obtained by mining through the embodiments and is stored in the image database, the isolation belt information of each road can be applied to various application scenes, and only the following two types of isolation belt information are listed:
application scene 1,
The condition that the road contains the physical isolation belt is integrated into the path planning, for example, if the user wants to improve the safety factor in the path planning process, the road containing the physical isolation belt can be preferentially selected in the path from the starting point to the destination, or the factors of whether the road contains the physical isolation belt are integrated in the path sequencing process, so that the recommended sequencing of the path containing the physical isolation belt is improved, and the like.
Application scene 2,
When the map interface is displayed, the information of whether the road contains the isolation belt can be marked on the map interface. For example, when the user locates to a granularity of road level, a path containing the isolation zones may be displayed in a particular color or logo to assist the user in making travel decisions. For another example, when a path planned for the user is displayed on the map, a road including the isolation belt in the displayed path may be displayed with a special color or a logo or the like.
According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.
As shown in fig. 5, is a block diagram of an electronic device according to an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 5, the electronic device includes: one or more processors 501, memory 502, and interfaces for connecting components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 501 is illustrated in fig. 5.
Memory 502 is a non-transitory computer readable storage medium provided by the present application. Wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the methods provided by the present application. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the method provided by the present application.
The memory 502 serves as a non-transitory computer readable storage medium that may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules, corresponding to the methods in embodiments of the present application. The processor 501 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 502, that is, implements the isolation zone mining method in the above-described method embodiments.
Memory 502 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created from the use of the route planned electronic device, etc. In addition, memory 502 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 502 may optionally include memory located remotely from processor 501, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device may further include: an input device 503 and an output device 504. The processor 501, memory 502, input devices 503 and output devices 504 may be connected by a bus or otherwise, for example in fig. 5.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, pointer stick, one or more mouse buttons, trackball, joystick, and like input devices. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed embodiments are achieved, and are not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.
Claims (17)
1. The mining method of the isolation belt information is characterized by comprising the following steps of:
acquiring travel track data of a user on a road to be detected within a set time, wherein the travel track comprises a first type travel track and a second type travel track; the first travel track and the second travel track are respectively a user riding track and a driving track in the same direction; or the first travel track and the second travel track are driving tracks in opposite directions respectively;
determining an overlapping area of the first travel track and the second travel track;
determining the ratio of the number of sampling points of the first type travel track to the total number of sampling points of the first type travel track in the overlapping area;
determining whether a physical isolation belt exists on the road based on the ratio, wherein the physical isolation belt refers to an isolation belt used for isolating different lanes on the road; wherein,
The determining the overlapping area of the first travel track and the second travel track comprises the following steps:
determining the distance range of a first travel track sampling point with a preset proportion from the road center;
determining the distance range of a second class travel track sampling point with a preset proportion from the road center;
and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
2. The method according to claim 1, characterized in that the method further comprises:
and storing the information of whether the physical isolation zone exists on the road in a map database.
3. The method of claim 1, wherein determining whether a physical barrier is present on the roadway based on the ratio comprises:
if the ratio is greater than or equal to a preset first duty ratio threshold, determining that no physical isolation belt exists on the road;
if the ratio is smaller than or equal to a preset second duty ratio threshold value, determining that a physical isolation zone exists on the road;
wherein the second duty cycle threshold is less than the first duty cycle threshold.
4. A method according to claim 3, characterized in that the method further comprises:
And if the ratio is larger than the second duty ratio threshold and smaller than the first duty ratio threshold, determining whether a physical isolation zone exists on the road by utilizing the image data of the road to be detected.
5. The method according to claim 1, characterized in that the method further comprises:
if the total number of the sampling points of the first travel track or the total number of the sampling points of the second travel track is smaller than a preset number threshold, determining whether a physical isolation zone exists on the road by utilizing the image data of the road to be detected.
6. The method according to claim 4 or 5, wherein determining whether a physical barrier exists on the road using the image data of the road to be detected comprises:
inputting the image of the road to be detected into an image classification model obtained by training in advance;
and determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected.
7. The method of claim 6, wherein the image classification model is pre-trained by:
acquiring a training sample, wherein the training sample comprises a road image and a manual labeling result of whether the road image contains a physical isolation belt or not;
And taking the road image as input of a depth residual error network, taking a manual labeling result corresponding to the road image as an output target of the depth residual error network, and training the depth residual error network to obtain an image classification model.
8. The method of claim 6, wherein determining whether a physical barrier exists on the road based on the classification of the image of the road to be detected by the image classification model comprises:
acquiring classification results of the image classification model on a plurality of images of the road to be detected;
if the classification result is that the number of the images with the physical isolation bands reaches a preset number threshold, or if the classification result is that the proportion of the number of the images with the physical isolation bands to the number of the images of the road to be detected exceeds a preset proportion threshold, determining that the physical isolation bands exist on the road to be detected.
9. An apparatus for mining information on an isolation belt, comprising:
the track acquisition unit is used for acquiring distribution data of user travel tracks on a road to be detected within a set time, wherein the user travel tracks comprise a first travel track and a second travel track; the first travel track and the second travel track are respectively a user riding track and a driving track in the same direction; or the first travel track and the second travel track are driving tracks in opposite directions respectively;
The area determining unit is used for determining an overlapping area of the first travel track and the second travel track;
the first excavating unit is used for determining the ratio of the number of sampling points of the first type of travel tracks to the total number of sampling points of the first type of travel tracks in the overlapping area; determining whether a physical isolation belt exists on the road based on the ratio, wherein the physical isolation belt refers to an isolation belt used for isolating different lanes on the road; wherein,
the area determination unit specifically performs:
determining the distance range of a first travel track sampling point with a preset proportion from the road center;
determining the distance range of a second class travel track sampling point with a preset proportion from the road center;
and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
10. The apparatus of claim 9, wherein the apparatus further comprises:
and the information storage unit is used for storing the information of whether the physical isolation belt exists on the road in the map database.
11. The apparatus according to claim 9, wherein the first digging unit performs in particular:
if the ratio is greater than or equal to a preset first duty ratio threshold, determining that no physical isolation belt exists on the road;
If the ratio is smaller than or equal to a preset second duty ratio threshold value, determining that a physical isolation zone exists on the road;
wherein the second duty cycle threshold is less than the first duty cycle threshold.
12. The apparatus of claim 11, wherein the apparatus further comprises:
and the second digging unit is used for determining whether a physical isolation zone exists on the road by utilizing the image data of the road to be detected if the ratio is larger than a second duty ratio threshold value and smaller than a first duty ratio threshold value.
13. The apparatus of claim 9, wherein the apparatus further comprises:
and the second mining unit is used for determining whether a physical isolation zone exists on the road by utilizing the image data of the road to be detected if the total number of the first travel track sampling points or the total number of the second travel track sampling points is smaller than a preset number threshold.
14. The apparatus according to claim 12 or 13, wherein the second digging unit, when determining whether a physical barrier exists on the road using the image data of the road to be detected, specifically performs:
inputting the image of the road to be detected into an image classification model obtained by training in advance;
And determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected.
15. The apparatus of claim 14, wherein the apparatus further comprises:
the model training unit is used for acquiring a training sample, wherein the training sample comprises a road image and a manual labeling result of whether the road image contains a physical isolation belt or not; and taking the road image as input of a depth residual error network, taking a manual labeling result corresponding to the road image as an output target of the depth residual error network, and training the depth residual error network to obtain an image classification model.
16. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.
17. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911112083.2A CN112800153B (en) | 2019-11-14 | 2019-11-14 | Isolation belt information mining method, device, equipment and computer storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911112083.2A CN112800153B (en) | 2019-11-14 | 2019-11-14 | Isolation belt information mining method, device, equipment and computer storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112800153A CN112800153A (en) | 2021-05-14 |
| CN112800153B true CN112800153B (en) | 2023-10-31 |
Family
ID=75803625
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911112083.2A Active CN112800153B (en) | 2019-11-14 | 2019-11-14 | Isolation belt information mining method, device, equipment and computer storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112800153B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113353247B (en) * | 2021-07-05 | 2023-04-14 | 中国商用飞机有限责任公司 | Airplane anti-skid brake control method and system based on image recognition technology |
| CN113327103B (en) * | 2021-08-03 | 2021-10-26 | 深圳市知酷信息技术有限公司 | Intelligent campus epidemic situation on-line monitoring and early warning method, system and storage medium |
| CN116753976B (en) * | 2023-08-18 | 2023-12-22 | 广西大也智能数据有限公司 | Walking navigation method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007183432A (en) * | 2006-01-06 | 2007-07-19 | Toyota Motor Corp | Map creation device for automatic traveling and automatic traveling device |
| KR20170064093A (en) * | 2015-11-30 | 2017-06-09 | 현대엠엔소프트 주식회사 | Driving Route at The Intersection of Leaving and Re-search Method using Image Recognition System |
| CN108345822A (en) * | 2017-01-22 | 2018-07-31 | 腾讯科技（深圳）有限公司 | A kind of Processing Method of Point-clouds and device |
| CN110188152A (en) * | 2019-05-30 | 2019-08-30 | 北京百度网讯科技有限公司 | Electronic map road information processing method and device, electronic equipment, readable medium |
| CN110363771A (en) * | 2019-07-15 | 2019-10-22 | 武汉中海庭数据技术有限公司 | A kind of isolation guardrail form point extracting method and device based on three dimensional point cloud |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10628671B2 (en) * | 2017-11-01 | 2020-04-21 | Here Global B.V. | Road modeling from overhead imagery |
-
2019
- 2019-11-14 CN CN201911112083.2A patent/CN112800153B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007183432A (en) * | 2006-01-06 | 2007-07-19 | Toyota Motor Corp | Map creation device for automatic traveling and automatic traveling device |
| KR20170064093A (en) * | 2015-11-30 | 2017-06-09 | 현대엠엔소프트 주식회사 | Driving Route at The Intersection of Leaving and Re-search Method using Image Recognition System |
| CN108345822A (en) * | 2017-01-22 | 2018-07-31 | 腾讯科技（深圳）有限公司 | A kind of Processing Method of Point-clouds and device |
| CN110188152A (en) * | 2019-05-30 | 2019-08-30 | 北京百度网讯科技有限公司 | Electronic map road information processing method and device, electronic equipment, readable medium |
| CN110363771A (en) * | 2019-07-15 | 2019-10-22 | 武汉中海庭数据技术有限公司 | A kind of isolation guardrail form point extracting method and device based on three dimensional point cloud |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112800153A (en) | 2021-05-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3933345A2 (en) | Road event detection method, apparatus, device and storage medium | |
| CN112800153B (en) | Isolation belt information mining method, device, equipment and computer storage medium | |
| US11887376B2 (en) | Method and apparatus of estimating road condition, and method and apparatus of establishing road condition estimation model | |
| CN111951144B (en) | Method and device for determining violation road section and computer readable storage medium | |
| KR102544760B1 (en) | Traffic accident recognition method, device, electronic equipment and storage medium | |
| CN110675644B (en) | Method and device for identifying road traffic lights, electronic equipment and storage medium | |
| WO2011023333A2 (en) | Methods and apparatus for processing road data | |
| EP2923177A1 (en) | Method of determining trajectories through one or more junctions of a transportation network | |
| JP7168708B2 (en) | MAP DATA UPDATE METHOD, APPARATUS, DEVICE, AND READABLE STORAGE MEDIUM | |
| CN110765227A (en) | Road traffic network model construction method and device | |
| CN111311906B (en) | Intersection distance detection method and device, electronic equipment and storage medium | |
| JP7200207B2 (en) | Map generation method, map generation device, electronic device, non-transitory computer-readable storage medium and computer program | |
| CN110968718A (en) | Target detection model negative sample mining method and device and electronic equipment | |
| CN111950537A (en) | Zebra crossing information acquisition method, map updating method, device and system | |
| CN111291681B (en) | Method, device and equipment for detecting lane change information | |
| CN114443794A (en) | Data processing and map updating method, device, equipment and storage medium | |
| CN111442775B (en) | Road identification method and device, electronic equipment and readable storage medium | |
| CN111324616B (en) | Method, device and equipment for detecting lane change information | |
| CN114998863B (en) | Target road identification method, device, electronic equipment and storage medium | |
| CN114771576A (en) | Behavior data processing method, control method of automatic driving vehicle and automatic driving vehicle | |
| CN116311015A (en) | Road scene recognition method, device, server, storage medium and program product | |
| CN111009135B (en) | Method and device for determining vehicle running speed and computer equipment | |
| CN113124887A (en) | Route information processing method, device, equipment and storage medium | |
| CN113129627B (en) | Method and device for indicating passing of road intersection, electronic equipment and storage medium | |
| CN116424347A (en) | Data mining method, vehicle control method, device, equipment and storage medium |
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 |