CN112329631A - Method for carrying out traffic flow statistics on expressway by using unmanned aerial vehicle - Google Patents
Method for carrying out traffic flow statistics on expressway by using unmanned aerial vehicle Download PDFInfo
- Publication number
- CN112329631A CN112329631A CN202011225316.2A CN202011225316A CN112329631A CN 112329631 A CN112329631 A CN 112329631A CN 202011225316 A CN202011225316 A CN 202011225316A CN 112329631 A CN112329631 A CN 112329631A
- Authority
- CN
- China
- Prior art keywords
- image
- pixel points
- lane
- unmanned aerial
- aerial vehicle
- 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.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
Abstract
The invention discloses a method for carrying out traffic flow statistics on a highway by using an unmanned aerial vehicle, which is characterized by comprising the following steps of: s1, the unmanned aerial vehicle patrols the expressway and shoots a road surface live video; s2, extracting at least one frame of image of the live video; s3, performing semantic segmentation on the image in the step S2; s4, carrying out binarization processing on the image to obtain a binarized image; s5, transversely scanning the binary image, defining N transversely continuous pixel points as foreground pixel points as a lane line, deleting the foreground pixel points defined as the lane line, and forming the outline of the automobile by the residual foreground pixel points, wherein N is a lane width value; and S6, counting the number of the automobile profiles in the step S5. The invention can provide the interference factors of the lane lines when counting the traffic flow, so that the accuracy of the statistical result is higher.
Description
Technical Field
The invention relates to a method for carrying out traffic flow statistics on a highway by using an unmanned aerial vehicle.
Background
In recent years, with the continuous increase of the vehicle holding capacity, congestion often appears on roads, particularly on expressways, and the congestion is more serious when holidays are met, so that the traffic flow is tracked in real time, a driver is reminded to select a feasible scheme for traveling according to the traffic flow of each road, and the scheme is one of the most feasible schemes for solving the road congestion.
Along with the quick development of unmanned aerial vehicle, unmanned aerial vehicle is widely used in road conditions and detects, but unmanned aerial vehicle among the prior art has following problem at least: unmanned aerial vehicle need discern the image when the real-time traffic flow of analysis, produces lane line and vehicle among the prior art and all generally is considered as the prospect when discerning, influences the accuracy of traffic flow statistics.
Disclosure of Invention
The invention aims to provide an image recognition system which is high in vehicle recognition degree and convenient for vehicle flow statistics.
In order to solve the problems, the invention provides a method for carrying out traffic flow statistics on a highway by using an unmanned aerial vehicle, which is characterized by comprising the following steps of:
s1, the unmanned aerial vehicle patrols the expressway and shoots a road surface live video;
s2, extracting at least one frame of image of the live video;
s3, performing semantic segmentation on the image in the step S2, separating out lanes and a background, and deleting pixel points of the background from the image;
s4, carrying out binarization processing on the image to obtain a binarized image, wherein in the binarized image, the road surface is displayed as a background and is black, and the automobile outline and the lane line are displayed as a foreground and are white;
s5, transversely scanning the binary image, defining N transversely continuous pixel points as foreground pixel points as a lane line, deleting the foreground pixel points defined as the lane line, and forming the outline of the automobile by the residual foreground pixel points, wherein N is a lane width value;
and S6, counting the number of the automobile profiles in the step S5.
As a further improvement of the present invention, in the step S6, the pixels in the binarized image processed in the step S5 are sequentially scanned, if the scanned current pixel is a foreground pixel, other pixels in the neighborhood setting range are scanned, and if the number of foreground pixels in the neighborhood is greater than or equal to the set threshold, all the neighborhoods are filled into foreground pixels to obtain an independent and complete automobile contour, and the number of the automobile contour is counted.
As a further improvement, the method is characterized by further comprising the following steps
S8.1, transversely scanning the binary image along the outer side of the lane on the binary image in the step S4, and recording the coordinates (X) of the foreground pixel points scanned firstly if the binary image is transversely continuous with N foreground pixel pointsm;Yn）；
S8.2, performing curve fitting on a plurality of foreground pixel point coordinates (Xm; Yn) which are scanned in the step S8.1 and are continuous in the longitudinal direction through a least square method to obtain a quadratic parabolic curve equation of the emergency lane;
s8.3, drawing an emergency lane line in the image processed in the step S5 according to the quadratic parabolic equation, and giving a set gray value to the emergency lane line, wherein the gray value is between 0 and 255;
s8.4, transversely scanning the image outside the image obtained in the step S8.3, and determining whether the emergency lane is occupied according to the gray value of the image scanned firstly.
As a further improvement of the present invention, in step S6, the vehicle contour is filled by a flood algorithm.
As a further improvement of the present invention, the steps S1-S8 are performed in the drone, and the method further includes the step S9 of transmitting the result of the vehicle profile counted in the step S6 and/or whether the lane is occupied in the step S8.4 and/or the live video in the step S1 and/or the image in the step S2 to the ground station.
The method has the advantages that after a road surface live image is obtained, the image is subjected to semantic segmentation to distinguish a lane area and a background area, then the lane area is subjected to binarization processing, vehicles and lane lines serving as foregrounds in the image subjected to binarization processing are displayed as white, a lane part serving as a background is displayed as black, after the lane lines are removed, the contour lines of the vehicles are captured, and finally the number of the contour lines of the vehicles in the image is counted, so that the real-time road crowding degree is judged.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a flow chart of the present invention;
in the figure: 100-an image acquisition module; 200-an image processing module; 102-a video filming unit; 104-picture making unit; 202-semantic segmentation unit; 204-a binarization processing unit; 206-lane line rejecting unit; 208-a vehicle contour extraction unit; 210-an emergency lane line contour coordinate extraction unit; 212-emergency lane line fitting unit; 214-emergency lane scanning unit; 216-a statistical unit; 300-a communication module; 400-ground platform.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
As shown in fig. 1 and 2, the present invention includes an image acquisition module 100 for acquiring a live image of a road pavement and an image processing module 200 for processing the live image, wherein the image processing module 200 includes:
a semantic segmentation unit 202, which performs semantic segmentation on the live-action image to obtain a lane area and a background area, and eliminates the background area in the image;
a binarization processing unit 204, configured to perform binarization processing on the image without the background region to obtain a binarized image of the image, where a road surface is a background, and a vehicle and a lane line are foreground;
a lane line removing unit 206, configured to perform horizontal scanning on the binarized image, define a lane line where N horizontal continuous pixel points are foreground pixel points, and remove the lane line from the binarized image to obtain a first processed image, where N is defined as a width threshold of the lane line, and the lane line includes a driving lane line and an emergency lane line;
a vehicle contour extraction unit 208 that scans the first processed image and captures an individual vehicle contour to obtain a second processed image;
and a counting unit 216 for counting the number of independent car outlines in the second processed image.
As a further improvement of the present invention, the vehicle contour extraction unit 208 scans the first processed image, scans pixels in a set range of a neighborhood if a current point is a foreground pixel, fills all neighborhoods of the current point into foreground pixels to obtain an independent vehicle contour if the number of foreground pixels in the neighborhood is greater than or equal to a set threshold, and obtains a second processed image after scanning and filling.
As a further improvement of the invention, the method also comprises the following steps:
the emergency lane contour coordinate extracting unit 210 scans the binarized image transversely along the outside of the lane on the binarized image, and records the coordinates (X) of the foreground pixels scanned first if the scanned foreground pixels are N continuous foreground pixels transverselym;Yn）；
An emergency lane line fitting unit 212 for fitting coordinates (X) of longitudinally continuous foreground pixelsm;Yn) Performing curve fitting, drawing a lane line on the second processed image, and giving a set gray value to the lane line, wherein the gray value is between 0 and 255;
and the emergency lane scanning unit 214 is used for transversely scanning the image processed by the emergency lane line fitting unit from the outer side and determining whether the emergency lane is occupied according to the gray value of the image scanned firstly.
As a further improvement of the present invention, the emergency lane line fitting unit 212 uses the least square method to match the coordinates (X) of the foreground pixel pointsm;Yn) And performing curve fitting to obtain a quadratic parabolic curve equation of the traffic lane, and drawing a fitted lane line on the second processed image according to the obtained quadratic parabolic equation.
As a further development of the invention, the vehicle contour extraction unit 208 is filled by means of a flood filling algorithm.
The invention also provides an unmanned aerial vehicle, which comprises the image identification system for the road inspection unmanned aerial vehicle and is characterized by further comprising
The video shooting unit 102 is used for recording the patrolled road in real time, making a live video and storing the live video;
the picture making unit 104 is in communication connection with the video shooting module and the semantic segmentation unit 202, and is configured to call the video stored in the video shooting module, extract a live image of at least one frame according to a set time, and receive the call of the semantic segmentation unit 202 from the extracted live image.
As a further improvement on this subject, a communication module is further included for communicating with the ground station 400, said communication module transmitting the statistical data and the live video counted by said statistical unit 216 and the fourth processed image to the ground station 400.
The specific principle of the invention is as follows:
(1) the unmanned aerial vehicle takes off, the video shooting unit 102 shoots the road surface and records the video, and the video shooting unit 102 is an airborne camera;
(2) the picture making unit 104 extracts a live image of at least one frame in the live video;
(3) the semantic segmentation unit 202 processes the live image to obtain a lane area and a background area, and then eliminates the background area;
(4) the binarization processing unit 204 performs binarization processing on the image, wherein the road is a background and is displayed as black, and the lane line and the vehicle are foreground and are displayed as white;
(5) the lane line removing unit 206 performs horizontal scanning on the image, specifically defines N horizontal continuous pixel points, each of which is a foreground pixel point, as a lane line, and removes the lane line from the binarized image, where N is defined as a width threshold of the lane line, and the lane line includes a driving lane line and an emergency lane line;
(6) the vehicle contour extraction unit 208 scans the image to capture an independent vehicle contour, specifically, when scanning is performed, if a current point is a foreground pixel point, pixels within a set range of a neighborhood are scanned, if the number of foreground pixel points in the neighborhood is greater than or equal to a set threshold, the neighborhood is completely filled into foreground pixel points to obtain an independent vehicle contour, and the formed image is a second processed image;
(7) the statistical unit 216 performs statistics on the independent automobile profiles in the images, so as to obtain the real-time traffic flow in the current frame image;
(8) the statistical unit 216 communicates with the ground station 400 through a communication module, so that the ground station 400 knows the real-time traffic flow;
in addition, the invention can also acquire the occupied condition in the emergency lane, specifically:
(9) the binarization processing unit 204 communicates with the emergency lane line contour coordinate extraction unit 210, and the emergency lane line contour coordinate extraction unit 210 scans the binarized image transversely along the outer side of the lane on the binarized image (generally, the outer side shoulder of the highway lane is close to the emergency lane), and records the coordinates (X) of the foreground pixel points scanned firstly if the scanned foreground pixel points are N continuous foreground pixel points transverselym;Yn）；
(10) The emergency lane line fitting unit 212 performs the least square method on the coordinates (X) of the foreground pixel pointsm;Yn) Performing curve fitting to obtain a quadratic parabolic curve equation of the traffic lane, drawing a fitted lane line on the second processed image according to the obtained quadratic parabolic equation, and giving a certain gray value R to the lane line, wherein the gray value R is between 0 and 255;
(11) the emergency lane scanning unit 214 performs horizontal scanning on the image processed by the emergency lane line fitting unit from the outside, determines whether the emergency lane is occupied according to the gray value of the image scanned firstly, if the gray value of the pixel scanned firstly is R, it indicates that the emergency lane is unoccupied, and if the gray value of the pixel scanned firstly is 255 (white), it indicates that the emergency lane is occupied.
(12) The statistical number, the occupied condition of the emergency lane and the live image extracted by the picture making unit 104 are sent to the ground platform 400 through the communication module 300, and the ground platform 400 confirms vehicle information, such as license plate number information, and performs related punishment;
(13) the communication module 300 is also capable of transmitting the video produced by the video capture unit 102 to the ground station 400.
The technical principle of the present invention is described above in connection with specific embodiments. The description is made for the purpose of illustrating the principles of the invention and should not be construed in any way as limiting the scope of the invention. Based on the explanations herein, those skilled in the art will be able to conceive of other embodiments of the present invention without inventive effort, which would fall within the scope of the present invention.
Claims (5)
1. A method for carrying out traffic flow statistics on a highway by using an unmanned aerial vehicle is characterized by comprising the following steps:
s1, the unmanned aerial vehicle patrols the expressway and shoots a road surface live video;
s2, extracting at least one frame of image of the live video;
s3, performing semantic segmentation on the image in the step S2, separating out lanes and a background, and deleting pixel points of the background from the image;
s4, carrying out binarization processing on the image to obtain a binarized image, wherein in the binarized image, the road surface is displayed as a background and is black, and the automobile outline and the lane line are displayed as a foreground and are white;
s5, transversely scanning the binary image, defining N transversely continuous pixel points as foreground pixel points as a lane line, deleting the foreground pixel points defined as the lane line, and forming the outline of the automobile by the residual foreground pixel points, wherein N is a lane width value;
and S6, counting the number of the automobile profiles in the step S5.
2. The method as claimed in claim 1, wherein in step S6, the pixels in the binarized image processed in step S5 are sequentially scanned, if the current scanned pixel is a foreground pixel, other pixels in a neighborhood setting range are scanned, and if the number of foreground pixels in the neighborhood is greater than or equal to a set threshold, the neighborhood is completely filled into foreground pixels to obtain an independent and complete automobile contour, and the number of the automobile contour is counted.
3. The method for traffic flow statistics on expressway by unmanned aerial vehicle according to claim 1, further comprising the following steps
S8.1, transversely scanning the binary image along the outer side of the lane on the binary image in the step S4, and recording the coordinates (X) of the foreground pixel points scanned firstly if the binary image is transversely continuous with N foreground pixel pointsm;Yn）；
S8.2, carrying out coordinate (X) on a plurality of longitudinally continuous foreground pixel points by a least square methodm;Yn) Performing curve fitting to obtain a quadratic parabolic curve equation of the emergency lane;
s8.3, drawing an emergency lane line in the image processed in the step S5 according to the quadratic parabolic equation, and giving a set gray value to the emergency lane line, wherein the gray value is between 0 and 255;
s8.4, transversely scanning the image outside the image obtained in the step S8.3, and determining whether the emergency lane is occupied according to the gray value of the image scanned firstly.
4. The method of claim 2, wherein in step S6, the vehicle contour is filled by a flood algorithm.
5. The method of traffic flow statistics for expressway by using unmanned aerial vehicle as claimed in claim 2, wherein the steps S1-S8 are performed in unmanned aerial vehicle, further comprising step S9, transmitting the result of whether the vehicle profile counted in step S6 and/or the lane is occupied in step S8.4 and/or the live video in step S1 and/or the image in step S2 to ground platform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011225316.2A CN112329631A (en) | 2020-11-05 | 2020-11-05 | Method for carrying out traffic flow statistics on expressway by using unmanned aerial vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011225316.2A CN112329631A (en) | 2020-11-05 | 2020-11-05 | Method for carrying out traffic flow statistics on expressway by using unmanned aerial vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112329631A true CN112329631A (en) | 2021-02-05 |
Family
ID=74316142
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011225316.2A Pending CN112329631A (en) | 2020-11-05 | 2020-11-05 | Method for carrying out traffic flow statistics on expressway by using unmanned aerial vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112329631A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113033514A (en) * | 2021-05-24 | 2021-06-25 | 南京伯索网络科技有限公司 | Classroom student aggressiveness evaluation method based on network |
| CN113221798A (en) * | 2021-05-24 | 2021-08-06 | 南京伯索网络科技有限公司 | Classroom student aggressiveness evaluation system based on network |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09212791A (en) * | 1996-02-07 | 1997-08-15 | Babcock Hitachi Kk | Method and device for monitoring road surface |
| JPH10214395A (en) * | 1997-01-30 | 1998-08-11 | Nec Corp | Lane change detector |
| CN102201167A (en) * | 2010-04-07 | 2011-09-28 | 宫宁生 | Video-based driveway automatic identification method |
| CN102314599A (en) * | 2011-10-11 | 2012-01-11 | 东华大学 | Identification and deviation-detection method for lane |
| CN104794907A (en) * | 2015-05-05 | 2015-07-22 | 江苏大为科技股份有限公司 | Traffic volume detection method using lane splitting and combining |
| CN105005778A (en) * | 2015-08-14 | 2015-10-28 | 东南大学 | Expressway vehicle detection method based on visual background extraction |
| CN105427626A (en) * | 2015-12-19 | 2016-03-23 | 长安大学 | Vehicle flow statistics method based on video analysis |
| CN105740809A (en) * | 2016-01-28 | 2016-07-06 | 东南大学 | Expressway lane line detection method based on onboard camera |
| CN107248296A (en) * | 2017-07-13 | 2017-10-13 | 南京航空航天大学 | A kind of video traffic flow statistical method based on unmanned plane and temporal aspect |
| CN110502982A (en) * | 2019-07-11 | 2019-11-26 | 平安科技（深圳）有限公司 | The method, apparatus and computer equipment of barrier in a kind of detection highway |
| CN110659539A (en) * | 2018-06-28 | 2020-01-07 | 杭州海康威视数字技术股份有限公司 | Information processing method and device |
| CN111460921A (en) * | 2020-03-13 | 2020-07-28 | 华南理工大学 | Lane line detection method based on multitask semantic segmentation |
| CN111597913A (en) * | 2020-04-23 | 2020-08-28 | 浙江大学 | Lane line picture detection and segmentation method based on semantic segmentation model |
-
2020
- 2020-11-05 CN CN202011225316.2A patent/CN112329631A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09212791A (en) * | 1996-02-07 | 1997-08-15 | Babcock Hitachi Kk | Method and device for monitoring road surface |
| JPH10214395A (en) * | 1997-01-30 | 1998-08-11 | Nec Corp | Lane change detector |
| CN102201167A (en) * | 2010-04-07 | 2011-09-28 | 宫宁生 | Video-based driveway automatic identification method |
| CN102314599A (en) * | 2011-10-11 | 2012-01-11 | 东华大学 | Identification and deviation-detection method for lane |
| CN104794907A (en) * | 2015-05-05 | 2015-07-22 | 江苏大为科技股份有限公司 | Traffic volume detection method using lane splitting and combining |
| CN105005778A (en) * | 2015-08-14 | 2015-10-28 | 东南大学 | Expressway vehicle detection method based on visual background extraction |
| CN105427626A (en) * | 2015-12-19 | 2016-03-23 | 长安大学 | Vehicle flow statistics method based on video analysis |
| CN105740809A (en) * | 2016-01-28 | 2016-07-06 | 东南大学 | Expressway lane line detection method based on onboard camera |
| CN107248296A (en) * | 2017-07-13 | 2017-10-13 | 南京航空航天大学 | A kind of video traffic flow statistical method based on unmanned plane and temporal aspect |
| CN110659539A (en) * | 2018-06-28 | 2020-01-07 | 杭州海康威视数字技术股份有限公司 | Information processing method and device |
| CN110502982A (en) * | 2019-07-11 | 2019-11-26 | 平安科技（深圳）有限公司 | The method, apparatus and computer equipment of barrier in a kind of detection highway |
| CN111460921A (en) * | 2020-03-13 | 2020-07-28 | 华南理工大学 | Lane line detection method based on multitask semantic segmentation |
| CN111597913A (en) * | 2020-04-23 | 2020-08-28 | 浙江大学 | Lane line picture detection and segmentation method based on semantic segmentation model |
Non-Patent Citations (2)
| Title |
|---|
| 秦洁: "基于大数据的高速公路车流量分析系统研究", 《数码世界》 * |
| 赵凯迪: "基于无人机的车辆和车道检测系统的设计与实现", 《中国优秀博硕士学位论文全文数据库(硕士) 工程科技Ⅱ辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113033514A (en) * | 2021-05-24 | 2021-06-25 | 南京伯索网络科技有限公司 | Classroom student aggressiveness evaluation method based on network |
| CN113221798A (en) * | 2021-05-24 | 2021-08-06 | 南京伯索网络科技有限公司 | Classroom student aggressiveness evaluation system based on network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110659539B (en) | Information processing method and device and machine-readable storage medium | |
| CN109460709B (en) | RTG visual barrier detection method based on RGB and D information fusion | |
| CN112232286A (en) | Unmanned aerial vehicle image recognition system and unmanned aerial vehicle are patrolled and examined to road | |
| US9773176B2 (en) | Image processing apparatus and image processing method | |
| CN108038416B (en) | Lane line detection method and system | |
| CN105741559B (en) | A kind of illegal occupancy Emergency Vehicle Lane detection method based on track line model | |
| CN107273788B (en) | Imaging system for performing lane detection in a vehicle and vehicle imaging system | |
| CN106971155B (en) | Unmanned vehicle lane scene segmentation method based on height information | |
| CN107665327B (en) | Lane line detection method and device | |
| CN112289032B (en) | Automatic inspection method for unmanned aerial vehicle expressway | |
| CN110298300B (en) | Method for detecting vehicle illegal line pressing | |
| CN108198417B (en) | A kind of road cruising inspection system based on unmanned plane | |
| CN108416316B (en) | Detection method and system for black smoke vehicle | |
| CN111369541A (en) | Vehicle detection method for intelligent automobile under severe weather condition | |
| CN102054169B (en) | License plate positioning method | |
| CN112329631A (en) | Method for carrying out traffic flow statistics on expressway by using unmanned aerial vehicle | |
| CN110163109B (en) | Lane line marking method and device | |
| CN105095869A (en) | Pedestrian detection method and apparatus | |
| CN105740836A (en) | Illegal emergency lane occupancy detection method | |
| CN111783666A (en) | Rapid lane line detection method based on continuous video frame corner feature matching | |
| CN111046741A (en) | Method and device for identifying lane line | |
| CN106340031A (en) | Method and device for detecting moving object | |
| WO2022104798A1 (en) | 5g-based unmanned electronic traffic police duty system | |
| CN111914675A (en) | Vehicle axle number determining method and device | |
| CN112232284A (en) | Unmanned aerial vehicle system based on automatic inspection of highway |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210205 |
|
| WD01 | Invention patent application deemed withdrawn after publication |