WO2023279966A1

WO2023279966A1 - Multi-lane-line detection method and apparatus, and detection device

Info

Publication number: WO2023279966A1
Application number: PCT/CN2022/100542
Authority: WO
Inventors: 李�杰; 李森; 张尉; 马坤
Original assignee: 中移(上海)信息通信科技有限公司; 中移智行网络科技有限公司; 中国移动通信集团有限公司
Priority date: 2021-07-08
Filing date: 2022-06-22
Publication date: 2023-01-12
Also published as: CN113221861A; CN113221861B

Abstract

The present application belongs to the technical field of image processing. Provided are a multi-lane-line detection method and apparatus, and a detection device. The multi-lane-line detection method comprises: extracting a plurality of frames of images from a video of a road having a plurality of lane lines; performing binarization processing and edge detection on each frame of image, so as to obtain a plurality of straight line segments; according to position information corresponding to the plurality of straight line segments, screening out non-lane lines; performing first merging processing on a plurality of straight line segments obtained after same have been subjected to screening; performing second merging processing on straight line segments, which are obtained after same have been subjected to first merging processing, in all the frames of images; and determining lane lines according to straight line segments obtained after same have been subjected to second merging processing. In the present application, a plurality of frames of images are extracted from a video of a road to perform detection, and mutual compensation and verification between the plurality of frames of images are utilized, such that the situation of missing detection due to a vehicle blocking a lane line is avoided, and a non-lane line, such as a symbol or sign on a road, is also prevented from being mistakenly detected as a lane line, thereby increasing the accuracy rate of lane line detection.

Description

A multi-lane detection method, device and detection equipment

Cross References to Related Applications

This application is based on a Chinese patent application with application number 202110770354.4 and a filing date of July 08, 2021, and claims the priority of this Chinese patent application. The entire content of this Chinese patent application is hereby incorporated by reference into this application.

technical field

The present invention relates to the technical field of image processing, in particular to a multi-lane detection method, device and detection equipment.

Background technique

At present, how to accurately detect the lane line of the road is a problem that is often faced when conducting research on intelligent driving, traffic condition detection, and traffic scheduling. With the development of computer vision and artificial intelligence technology, the lane line detection algorithm has also made great progress, but there are still some problems, for example, all the lane lines cannot be detected at one time, and the vehicles on the road will detect the lane lines. Occlusion will affect the detection effect, and some identifiers and signs on the road will be mistakenly detected as lane lines.

Contents of the invention

In view of this, the present application provides a multi-lane line detection method, device and detection equipment, which are used to solve the problem that in the prior art, all the lane lines cannot be detected at one time, and the vehicles on the road will block the lane lines and thus affect the The detection effect, some identifiers and signs on the road will be falsely detected as lane lines.

In order to solve the above technical problems, in the first aspect, the present application provides a multi-lane line detection method, including:

Extract several frames of images from a video of a road with multiple lanes;

Perform binarization processing and edge detection on each frame of image to obtain multiple straight line segments in each frame of image;

Filter out non-lane lines according to the position information corresponding to multiple straight line segments in each frame of image;

Performing the first merging process on the straight line segments located on the same straight line among the multiple straight line segments obtained after screening in each frame of image;

Perform a second merge process on the straight line segments obtained after the first merge process in all frame images;

Determine the lane line according to the straight line segment obtained after the second merging process;

Determining the lane line according to the straight line segment obtained after the second merging process includes:

Among the straight line segments obtained after the second merging process, the straight line segments whose number of the combined straight line segments obtained after the first merging process is greater than a preset threshold are determined as lane lines.

Preferably, the filtering out non-lane lines according to the position information corresponding to a plurality of straight line segments in each frame image includes:

determining a first angle formed between the shooting direction of the video and the direction of the road;

According to the slopes of the multiple straight line segments in each frame of image and the first angle, the non-lane lines in the multiple straight line segments are screened out.

Preferably, the filtering out non-lane lines in the multiple straight line segments according to the slopes of the multiple straight line segments in each frame of image and the first angle includes:

If the slope of a line segment meets the first angle:

then retain the straight line segment;

If the slope of a line segment meets the first angle:

Then filter out the straight line segment;

Wherein, θ is the first angle, and α is the slope of the straight line segment.

Preferably, the first merging process and/or the second merging process includes:

If the distance between the straight line segments and the rotation angle relative to a reference axis satisfy the preset condition, the straight line segments satisfying the preset condition are merged into one straight line segment.

Preferably, for every two straight line segments, the preset condition is:

And |P ₁ -P ₂ |≤D;

Wherein, β is the preset angle, D is the preset distance, the expression of a straight line segment is y=a ₁ x+b ₁ , and the expression of another straight line segment is y=a ₂ x+b ₂ ,

are the rotation angles of the two straight line segments relative to the x-axis,

P ₁ and P ₂ are respectively the abscissas of the intersection points of the two straight line segments and the x-axis,

Preferably, after the step of determining the lane line according to the straight line segment obtained after the second merging process, it further includes:

Merge two straight line segments whose distance is smaller than the preset distance into one straight line segment as the final lane line.

In a second aspect, the present application also provides a multi-lane detection device, including:

The extraction module is configured to extract several frame images from the video of the road with multi-lane lines;

A processing module configured to perform binarization processing and edge detection on each frame of image to obtain a plurality of straight line segments in each frame of image;

The filtering module is configured to filter out non-lane lines according to the position information corresponding to a plurality of straight line segments in each frame of image;

The first merging module is configured to perform the first merging process on the straight line segments located on the same straight line among the multiple straight line segments obtained after screening in each frame of image;

The second merging module is configured to perform the second merging process on the straight line segments obtained after the first merging process in all frame images;

The determination module is configured to determine the lane line according to the straight line segment obtained after the second merging process;

The determination module includes:

The determination unit is configured to determine, among the straight line segments obtained after the second merging process, the number of the combined straight line segments obtained after the first merging process is greater than a preset threshold value as the lane line.

Preferably, the screening module includes:

an angle unit configured to determine a first angle formed between the shooting direction of the video and the direction of the road;

The filtering unit is configured to filter out non-lane lines in the multiple straight line segments in each frame of image according to the slopes of the multiple straight line segments and the first angle.

Preferably, the screening unit includes:

The first subunit is configured such that if the slope of a line segment meets the first angle:

then retain the straight line segment;

The second subunit is configured such that if the slope of a line segment meets the first angle:

Then filter out the straight line segment;

Preferably, for every two straight line segments, the preset condition is:

And |P ₁ -P ₂ |≤D;

Preferably, it also includes:

The third merging module is configured to merge two straight line segments whose distance is smaller than a preset distance into one straight line segment as a final lane line.

In a third aspect, the present application also provides a detection device, including a memory, a processor, and a computer program stored on the memory and operable on the processor; when the processor executes the computer program, the above-mentioned Any multi-lane line detection method.

In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in any one of the above multi-lane detection methods are implemented.

The beneficial effects of the above-mentioned technical scheme of the present application are as follows:

In the embodiment of the present application, multi-frame images in the road video are extracted to detect multi-lane lines, and the mutual complementation and mutual verification between multi-frame images is used to avoid the missed detection caused by vehicles blocking the lane lines. It prevents non-lane lines such as pavement symbol signs and text signs from being misdetected as lane lines, and improves the accuracy of lane line detection.

Description of drawings

FIG. 1 is a schematic flowchart of a multi-lane detection method provided in Embodiment 1 of the present application;

Fig. 2 is the result figure after the image in embodiment one of the present application is binarized;

Fig. 3 is the result figure after edge detection is performed on the image in Embodiment 1 of the present application;

FIG. 4 is a schematic structural diagram of a multi-lane detection device in Embodiment 2 of the present application;

FIG. 5 is a schematic structural diagram of a detection device in Embodiment 3 of the present application.

detailed description

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below in conjunction with the drawings of the embodiments of the present application. Apparently, the described embodiments are some of the embodiments of the present application, but not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the described embodiments of the present application belong to the protection scope of the present application.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart of a multi-lane detection method provided in Embodiment 1 of the present application. The method includes the following steps:

Step 11: Extract several frames of images from the video of the road with multiple lanes.

In the embodiment of the present application, a video of a certain road is captured first, and the road captured in the video has multiple lane lines, and then the continuous frames of the acquired video are analyzed, and several frames of images are extracted from the video; optionally, When extracting, several frames of images can be extracted at intervals from consecutive frames, and the size of the interval and the specific number of frames to be extracted can be adjusted according to the specific conditions of the road and traffic. In a single-frame road image, the vehicles driving on the road usually form certain occlusions to the lane lines, which makes the detection of the occluded lane lines more difficult. At the same time, if there are symbols, text signs, etc. In graphics, the lines of these non-lane lines and lane lines are difficult to be distinguished by algorithms, and are easily misdetected as lane lines. However, in the video of a certain road, the number of vehicles on the road at different times, the size of vehicles, The position of the vehicle usually changes, so the lane line that is blocked in a certain frame of image can usually be revealed in other frames. Therefore, this application extracts several frames of images from the road video, and the symbols on the road, signs, There is a high probability that non-lane line graphics such as text signs will be blocked by driving vehicles. In this way, non-lane lines can be screened out, thereby improving the accuracy of detection. For real lane lines, the probability of being blocked is relatively low. Small, the discrimination can be realized according to the number of straight line segments appearing in several frames of images. Using the method of mutual verification and complementarity between multiple frames of images can make the lane line detection results more complete and accurate, so that the lane lines existing on the road can be completely detected, and at the same time, the interference objects that do not belong to the lane line can be effectively eliminated.

Step 12: Perform binarization processing and edge detection on each frame of image to obtain multiple straight line segments in each frame of image.

Optionally, after extracting several frames of images, in order to improve the detection effect, each frame of images may be preprocessed first. The preprocessing may include: performing distortion correction on each frame of image according to the camera parameters of the camera that shoots the road video, so as to prevent the straight lines on the road from appearing non-linear in the image due to distortion; the preprocessing may also Including: According to the spatial position relationship of the camera's installation position, height, angle, etc. relative to the captured road, the approximate image range of the road in each frame of image is obtained through geometric calculation, and the image region of interest (region) is set according to this range. of interest, ROI), the follow-up process can only process the region of interest in the image, which can save computing time; the preprocessing can also include: converting each frame of image into grayscale through the color image to grayscale image formula Image, because the color image and grayscale image have little influence on the detection effect, and the multi-channel image becomes a single channel, which is beneficial to speed up image processing.

Please refer to FIG. 2 , which is a result of binarization processing on the image in Embodiment 1 of the present application. In the embodiment of the present application, since the lane line is generally a lighter color such as white or yellow, which is quite different from the dark road surface, in order to further highlight the lane line, each frame of image can be binarized, that is, binary Value-based segmentation, for example: given a threshold, the pixels in the image whose grayscale is higher than this threshold are assigned a value of 255, and the pixels lower than or equal to this threshold are assigned a value of 0. The key of binarization segmentation lies in the determination of the threshold value. There are many ways to calculate the threshold value, such as image gray level average method, etc. The result after binarization processing is shown in Figure 2. Optionally, after the binarization processing, the image may also be filtered to improve the effect of image edge detection in the next step.

Please refer to FIG. 3 , which is a result diagram of edge detection performed on the image in Embodiment 1 of the present application. In the embodiment of the present application, after the binarization process is performed on the image, edge detection is further performed on the image, and the result after the edge detection is shown in FIG. 3 . Since the edge obtained after edge detection is still displayed as a discrete pixel point, the straight line segment of the vector can be obtained by means of straight line fitting, so as to obtain multiple straight line segments in each frame of the image, and the obtained after fitting Each straight line segment can be represented by the coordinates (x ₁ , y ₁ ) and (x ₂ , y ₂ ) of the two endpoints, or a straight line expression in the form of y=ax+b can be obtained through calculation based on the two endpoints.

Step 13: Filter out non-lane lines according to the position information corresponding to multiple straight line segments in each frame of image.

Since some of the straight line segments fitted after edge detection are obviously not lane lines, for example, the lane line is consistent with the direction of the road, if the angle between the direction of the straight line segment and the direction of the road exceeds a certain value, the straight line segment is considered It is obviously not a lane line. In the embodiment of the present application, some straight line segments that are obviously not a lane line may be screened out according to the position information corresponding to multiple straight line segments in each frame of image.

In the embodiment of the present application, optionally, the filtering out the non-lane lines according to the position information corresponding to the multiple straight line segments in each frame image includes:

Specifically, the position information corresponding to the straight line segment can include the position/coordinate of the straight line segment in the image, and the slope of the straight line segment. By setting a slope threshold, it is possible to filter out some straight line segments that are obviously not lane lines. The determination of the slope threshold is related to The orientation relationship between the camera that shoots the video and the road is related. Therefore, the first angle formed between the shooting direction of the video and the direction of the road to be shot is determined, and part of the straight line segments can be screened out according to the slope of each straight line segment and the relationship between the first angle.

Optionally, the filtering out non-lane lines in the multiple straight line segments according to the slopes of the multiple straight line segments in each frame of image and the first angle includes:

If the slope of a line segment meets the first angle:

then retain the straight line segment;

If the slope of a line segment meets the first angle:

Then filter out the straight line segment;

In the embodiment of the present application, let θ be the first angle formed between the shooting direction of the video and the direction of the road being shot, and α be the slope of multiple straight line segments fitted in each frame of image, then The angle of the straight line segment is calculated by the arc tangent arctan, and then the angular relationship between the first angle θ and the straight line segment is limited by the tangent value, so that some straight line segments that are obviously not lane lines can be screened out.

Step 14: Perform the first merging process on the straight line segments located on the same straight line among the multiple straight line segments obtained after filtering in each frame of image.

For a frame of image, after filtering out some non-lane lines, since the lane lines of the road are divided into solid lines and dashed lines, the edge fitting of the solid line lane lines is a complete straight line segment, that is, a solid line The edge of a lane line corresponds to a straight line segment; while each small segment of a dashed lane line will be fitted with a straight line segment, that is, the edge of a dashed lane line will correspond to multiple straight line segments, in fact, these straight line segments are all on the same line Therefore, in the detection process, these straight line segments that are actually on the same straight line need to be merged.

Optionally, the first merging process includes:

That is to say, the basis for merging is that for every two straight line segments, if the rotation angles and distances of the two straight line segments are very close, it can be determined that the two straight line segments are on the same straight line, and the two straight line segments can be combined segments are merged.

Wherein, for every two straight line segments, the preset condition is:

And |P ₁ -P ₂ |≤D;

Specifically, since the straight line segment obtained after filtering each frame of image can be described by a straight line expression in the form of y=ax+b, therefore, for every two straight line segments, one of the straight line segments can be set The expression of is y=a ₁ x+b ₁ , the expression of another line segment is y=a ₂ x+b ₂ , and let

are the rotation angles of the two straight line segments relative to the x-axis, then

Among them, a ₁ , a ₂ , b ₁ , and b ₂ are all constants; and if P ₁ and P ₂ are respectively the abscissas of the intersection points of the straight line where the two straight line segments are located and the x-axis, then by setting y=0, we can find have to

Therefore, if

That is, the positive difference between the rotation angles of the two straight line segments relative to the x-axis is less than or equal to the preset angle β, and |P ₁ -P ₂ |≤D, that is, the abscissa of the intersection point of the straight line where the two straight line segments are located and the x-axis If the positive difference is less than the preset distance D, it means that the two straight line segments are determined to be on the same straight line, and the two straight line segments can be merged into a new straight line segment. Optionally, the endpoints of the merged straight line segment can be the two endpoints farthest from the endpoints (four endpoints in total) of the two straight line segments before the merge, and the above-mentioned merge process is repeated until the dotted lane line corresponds to All the straight line segments lying on the same straight line are connected.

In the embodiment of the present application, optionally, a vector-type storage container may be created to store the straight line segment processed from each frame of image.

Step 15: Perform the second merging process on the straight line segments obtained after the first merging process in all frame images.

From the foregoing analysis, it can be known that if the position of the camera that shoots the video does not change, that is, the road in the video is always in the same area, then the multiple straight line segments obtained after the above steps of several frames of images extracted from the video There must be many repetitions of the edges of the same lane line on multiple images. Therefore, it is necessary to perform a second merge process on these repeated straight line segments.

The second merging process includes:

Wherein, for every two straight line segments, the preset condition is:

And |P ₁ -P ₂ |≤D;

That is to say, the principles of the second merging process and the first merging process may be the same, and to avoid repetition, details are not repeated here.

Step 16: Determining the lane line according to the straight line segment obtained after the second merging process, said determining the lane line according to the straight line segment obtained after the second merging process includes:

In the embodiment of the present application, since the vehicles on the road will pass by the symbols and characters on the road, the number of repetitions of the straight line segments corresponding to these symbols and characters in all frame images is usually higher than that of the straight line corresponding to the lane line. Therefore, by counting the number of straight line segments obtained after the first merge process combined by each straight line segment obtained after the second merge process, it can be determined which straight line segments are lane line. For example, the set of straight line segments before the second merging process (that is, the straight line segments obtained after the first merging process) can be set as A, and the set of straight line segments after the second merging process is B. While performing the second merging process, it is also counted how many straight line segments in set A each straight line segment in set B is merged from. If the number is greater than the preset threshold value, the straight line segment is determined as the lane line. Therefore, by setting the preset threshold value, the straight line segment corresponding to the symbol mark and text mark on the road can be screened out, thereby improving the accuracy of detection. During specific implementation, the value of the preset threshold can be adjusted according to specific road conditions.

In some embodiments of the present application, after the step of determining the lane line according to the straight line segment obtained after the second merging process, it further includes:

Specifically, since the lane line on the road has a certain width, a lane line on the road will detect two straight line segments in the image, that is, the straight line segments corresponding to the edges on both sides of the length direction of the lane line, and the detected lane line The goal of the line is to provide accurate lane separation information for driving, traffic monitoring, traffic scheduling, etc. Therefore, it is more convenient for each actual lane line to be marked with a straight line segment in the detection results of the lane line. Therefore, Among the straight line segments determined as lane lines, if two straight line segments correspond to both side edges of the same lane line, the two straight line segments may be merged into one line. Optionally, straight line segments may be merged according to the actual width of the lanes on the road and/or the actual width of the lane lines. For example, two straight line segments whose distance is smaller than the preset distance are merged into one straight line segment as the final lane line. Optionally, the final lane line takes an intermediate value, that is, an equidistant line between the two straight line segments. Specifically, the straight line segments determined as lane lines can be sorted from left to right according to the intersection of their extension lines and the x-axis, and then cluster analysis is performed on the sorted straight line segments, that is, according to the relationship between the two straight line segments and The distance between the intersection points of the x-axis is used for cluster analysis. If the distance is less than the preset distance, it is judged that the two straight line segments belong to the two side edges of the same lane line. At this time, the two straight line segments can be merged into one, and if If the spacing is greater than the preset spacing, it is judged that the two straight line segments do not belong to the edges on both sides of the same lane line.

Finally, the straight line segment finally obtained after the above processing is output as the final detection result.

Therefore, in the embodiment of the present application, multi-frame images in the road video are extracted to detect multi-lane lines, and the mutual complementation and mutual verification between multi-frame images is used to avoid missed detection caused by vehicles blocking lane lines. It also prevents non-lane lines such as road surface symbols and text signs from being misdetected as lane lines, and improves the accuracy of lane line detection.

Please refer to FIG. 4. FIG. 4 is a schematic structural diagram of a multi-lane detection device provided in Embodiment 2 of the present application. The detection device 40 includes:

The extraction module 41 is configured to extract several frames of images from the video of the road with multi-lane lines;

The processing module 42 is configured to perform binarization processing and edge detection on each frame of image to obtain a plurality of straight line segments in each frame of image;

The filtering module 43 is configured to filter out non-lane lines according to the position information corresponding to a plurality of straight line segments in each frame of image;

The first merging module 44 is configured to perform the first merging process on the straight line segments located on the same straight line among the multiple straight line segments obtained after screening in each frame of image;

The second merging module 45 is configured to perform a second merging process on the straight line segments obtained after the first merging process in all frame images;

The determination module 46 is configured to determine the lane line according to the straight line segment obtained after the second merging process;

The determination module includes:

Preferably, the screening module includes:

Preferably, the screening unit includes:

then retain the straight line segment;

Then filter out the straight line segment;

Preferably, for every two straight line segments, the preset condition is:

And |P ₁ -P ₂ |≤D;

Preferably, it also includes:

The third merging module is configured to merge the two straight line segments whose distance is smaller than the preset distance into one straight line segment as the final lane line.

The embodiment of the present application is a product embodiment corresponding to the above-mentioned method embodiment one, so details are not repeated here, please refer to the above-mentioned embodiment one for details.

Please refer to FIG. 5. FIG. 5 is a schematic structural diagram of a detection device provided in Embodiment 3 of the present application. The detection device 50 includes a processor 51, a memory 52 and a The computer program that runs on; When described processor 51 executes described computer program, realize following steps:

Extract several frames of images from a video of a road with multiple lanes;

In the embodiment of the present application, when the processor 51 executes the computer program, the following steps may also be implemented:

Said filtering non-lane lines according to the position information corresponding to a plurality of straight line segments in each frame image includes:

If the slope of a line segment meets the first angle:

then retain the straight line segment;

If the slope of a line segment meets the first angle:

Then filter out the straight line segment;

Preferably, for every two straight line segments, the preset condition is:

And |P ₁ -P ₂ |≤D;

The specific working process of the embodiment of the present application is consistent with that in the first method embodiment above, so it will not be repeated here. For details, please refer to the description of the method steps in the first embodiment above.

Embodiment 4 of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the method for detecting multi-lane markings in Embodiment 1 above are implemented. For details, please refer to the description of the method steps in the above corresponding embodiments.

The above-mentioned computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage may be realized by any method or technology. Information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Flash memory or other memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cartridge, tape magnetic disk storage or other magnetic storage device or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

It should be noted that: "first", "second", etc. are used to distinguish similar objects, and not necessarily used to describe a specific order or sequence.

In addition, the technical solutions described in the embodiments of the present application may be combined arbitrarily if there is no conflict.

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Anyone skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present invention. Should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims

A multi-lane line detection method, comprising:

Extract several frames of images from a video of a road with multiple lanes;

Perform binarization processing and edge detection on each frame of image to obtain multiple straight line segments in each frame of image;

Filter out non-lane lines according to the position information corresponding to multiple straight line segments in each frame of image;

Performing the first merging process on the straight line segments located on the same straight line among the multiple straight line segments obtained after screening in each frame of image;

Perform a second merge process on the straight line segments obtained after the first merge process in all frame images;

Determine the lane line according to the straight line segment obtained after the second merging process;

Determining the lane line according to the straight line segment obtained after the second merging process includes:

Among the straight line segments obtained after the second merging process, the straight line segments whose number of the combined straight line segments obtained after the first merging process is greater than a preset threshold are determined as lane lines.
The method for detecting multi-lane lines according to claim 1, wherein said filtering out non-lane lines according to position information corresponding to a plurality of straight line segments in each frame image comprises:

determining a first angle formed between the shooting direction of the video and the direction of the road;

According to the slopes of the multiple straight line segments in each frame of image and the first angle, the non-lane lines in the multiple straight line segments are screened out.
The method for detecting multi-lane lines according to claim 2, wherein, according to the slopes of the multiple straight line segments in each frame image and the first angle, the non-lane lines in the multiple straight line segments are screened out include:

If the slope of a line segment meets the first angle:
then retain the straight line segment;

If the slope of a line segment meets the first angle:
Then filter out the straight line segment;

Wherein, θ is the first angle, and α is the slope of the straight line segment.
The multi-lane line detection method according to claim 1, wherein the first merging process and/or the second merging process comprises:

If the distance between the straight line segments and the rotation angle relative to a reference axis satisfy the preset condition, the straight line segments satisfying the preset condition are merged into one straight line segment.
The multi-lane line detection method according to claim 4, wherein, for every two straight line segments, the preset condition is:

@R Yichi @三D;

Wherein, β is the preset angle, D is the preset distance, the expression of a straight line segment is y=a 1 x+b 1 , and the expression of another straight line segment is y=a 2 x+b 2 ,
are the rotation angles of the two straight line segments relative to the x-axis,
P 1 and P 2 are respectively the abscissas of the intersection points of the two straight line segments and the x-axis,
The multi-lane line detection method according to claim 1, wherein, after the step of determining the lane line according to the straight line segment obtained after the second merging process, further comprising:

Merge two straight line segments whose distance is smaller than the preset distance into one straight line segment as the final lane line.
A multi-lane detection device, comprising:

The extraction module is configured to extract several frame images from the video of the road with multi-lane lines;

A processing module configured to perform binarization processing and edge detection on each frame of image to obtain a plurality of straight line segments in each frame of image;

The filtering module is configured to filter out non-lane lines according to the position information corresponding to a plurality of straight line segments in each frame of image;

The first merging module is configured to perform the first merging process on the straight line segments located on the same straight line among the multiple straight line segments obtained after screening in each frame of image;

The second merging module is configured to perform a second merging process on the straight line segments obtained after the first merging process in all frame images;

The determination module is configured to determine the lane line according to the straight line segment obtained after the second merging process;

The determination module includes:

The determination unit is configured to determine, among the straight line segments obtained after the second merging process, the number of the combined straight line segments obtained after the first merging process is greater than a preset threshold value as the lane line.
The multi-lane detection device according to claim 7, wherein the screening module comprises:

an angle unit configured to determine a first angle formed between the shooting direction of the video and the direction of the road;

The filtering unit is configured to filter out non-lane lines in the multiple straight line segments in each frame of image according to the slopes of the multiple straight line segments and the first angle.
The multi-lane detection device according to claim 8, wherein the screening unit comprises:

The first subunit is configured such that if the slope of a line segment meets the first angle:
then retain the straight line segment;

The second subunit is configured such that if the slope of a line segment meets the first angle:
Then filter out the straight line segment;

Wherein, θ is the first angle, and α is the slope of the straight line segment.
The multi-lane line detection device according to claim 7, wherein the first merging process and/or the second merging process comprises:

If the distance between the straight line segments and the rotation angle relative to a reference axis satisfy the preset condition, the straight line segments satisfying the preset condition are merged into one straight line segment.
The multi-lane line detection device according to claim 10, wherein, for every two straight line segments, the preset condition is:

|P 1 -P 2 |≤D;

Wherein, β is the preset angle, D is the preset distance, the expression of a straight line segment is y=a 1 x+b 1 , and the expression of another straight line segment is y=a 2 x+b 2 ,
are the rotation angles of the two straight line segments relative to the x-axis,
P 1 and P 2 are respectively the abscissas of the intersection points of the two straight line segments and the x-axis,
The multi-lane detection device according to claim 7, further comprising:

The third merging module is configured to merge two straight line segments whose distance is smaller than a preset distance into one straight line segment as a final lane line.
A detection device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor; when the processor executes the computer program, any one of claims 1 to 6 is realized. The multi-lane line detection method described in the item.
A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the multi-lane line detection method according to any one of claims 1 to 6 are realized.