A lane line detection method, apparatus, electronic device and storage medium

Abstract

This invention discloses a lane line detection method, apparatus, electronic device, and storage medium. The method includes acquiring a lane image; performing thresholding processing on the lane image to obtain a binarized image; performing line scanning on the binarized image using a preset imaging model to obtain a line scanning result; wherein the imaging model is a model generated based on lane line features; and performing line detection processing on the line scanning result based on a preset Hough transform to obtain the lane line. This technical solution utilizes an imaging model and Hough transform to detect lane lines, which can improve the lane line detection effect and enhance the robustness of the detection.

Description

Technical Field

[0001] This invention relates to the field of lane line detection technology, and in particular to a lane line detection method, apparatus, electronic device, and storage medium. Background Technology

[0002] Lane detection is a fundamental aspect of vehicle driving environment monitoring and has wide applications in traffic statistics analysis and autonomous driving.

[0003] Lane detection refers to acquiring images of the road surface ahead using a CCD / CMOS camera placed in front of the vehicle, and then obtaining road marking information through a series of image processing techniques.

[0004] Using image processing technology to detect lane lines results in low accuracy. Summary of the Invention

[0005] This invention provides a lane line detection method, device, electronic device, and storage medium. By utilizing an imaging model and Hough transform to detect lane lines, it can improve the lane line detection effect and enhance the robustness of the detection.

[0006] According to one aspect of the present invention, a lane line detection method is provided, the method comprising:

[0007] Acquire lane images;

[0008] The lane image is thresholded to obtain a binarized image;

[0009] The binarized image is line-scanned using a preset imaging model to obtain the line-scanning result; wherein, the imaging model is a model generated based on lane line features;

[0010] Based on the preset Hough transform, the line scan results are processed to detect straight lines, thus obtaining lane lines.

[0011] According to another aspect of the present invention, a lane line detection device is provided, the device comprising:

[0012] Lane image acquisition module, used to acquire lane images;

[0013] A thresholding module is used to perform thresholding processing on the lane image to obtain a binarized image;

[0014] The line scanning module is used to perform line scanning on the binarized image using a preset imaging model to obtain line scanning results; wherein, the imaging model is a model generated based on lane line features;

[0015] The lane line detection module is used to perform straight line processing on the line scanning results based on a preset Hough transform to obtain lane lines.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a lane line detection method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement a lane line detection method according to any embodiment of the present invention.

[0021] The technical solution of this invention involves acquiring a lane image, then thresholding the lane image to obtain a binarized image, and using a preset imaging model to perform line scanning on the binarized image to obtain a line scanning result. Finally, based on a preset Hough transform, the line scanning result is processed to detect straight lines, thus obtaining the lane line. This technical solution utilizes an imaging model and Hough transform to detect lane lines, which improves the lane line detection effect and enhances the robustness of the detection.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a lane line detection method provided in Embodiment 1 of the present invention;

[0025] Figure 2 This is a schematic diagram of a lane line detection process provided in Embodiment 1 of this application;

[0026] Figure 3 This is a flowchart of a lane line detection process provided in Embodiment 2 of the present invention;

[0027] Figure 4 This is a schematic diagram illustrating the working principle of the camera provided in Embodiment 2 of this application;

[0028] Figure 5 This is a schematic diagram of the structure of a lane line detection device according to Embodiment 3 of the present invention;

[0029] Figure 6 This is a schematic diagram of the structure of an electronic device that implements a lane line detection method according to an embodiment of the present invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a lane line detection method according to Embodiment 1 of the present invention. This embodiment is applicable to situations requiring lane line detection. The method can be executed by a lane line detection device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0034] S110, Obtain lane image.

[0035] Lane images can refer to images formed by photographing lane lines on a road surface.

[0036] In this embodiment, lane images can be acquired using acquisition devices installed on the road. For example, lane images can be acquired using a CCD (charge-coupled device) camera mounted on a road pole, or a camera mounted on a road pole. Lane images can also be acquired using a camera mounted in front of the vehicle. For example, lane images can be acquired using a CCD or CMOS camera mounted in front of the vehicle.

[0037] Optionally, acquire lane images, including:

[0038] Image acquisition equipment is used to acquire images of the road surface to obtain lane images.

[0039] Image acquisition devices can include cameras, webcams, etc.

[0040] In this plan, Figure 2 This is a schematic diagram of a lane line detection process provided in Embodiment 1 of this application, as shown below. Figure 2 As shown, a camera installed in front of the vehicle can be used to collect lane lines on the road surface to obtain lane images.

[0041] By acquiring lane line images, lane line detection can be performed based on these images. This provides more information about road use, thereby improving road traffic.

[0042] S120. Threshold the lane image to obtain a binarized image.

[0043] In this scheme, thresholding refers to removing pixels in the image whose pixel values ​​are higher or lower than a certain value.

[0044] In this embodiment, as Figure 2 As shown, thresholding can be used to segment lane images and extract the desired portions of the lane images.

[0045] Specifically, unique weights ρ(R,G,B) are added to different colors in the lane image to obtain a binarized image with less redundant information and optimized color weights. Preferably, white and yellow, which are close to the lane line colors, can be given greater weights, i.e., different weights are assigned to different colors in the lane image. Thresholding of the lane image can be performed using the following formula:

[0046] G(R,G,B)=ρ(R,G,B)×H(R,G,B);

[0047] Where G(R,G,B) represents the binarized image and H(R,G,B) represents the lane image.

[0048] S130. Perform line scanning on the binarized image using a preset imaging model to obtain line scanning results; wherein, the imaging model is a model generated based on lane line features.

[0049] In this scheme, lane line width decreases with increasing distance in the perspective image; lane lines are wider in the near-field and narrower in the far-field. The width between adjacent lane lines also follows this pattern: wider in the near-field and narrower in the far-field. Therefore, a model generated based on lane line features can be used to process the binarized image, thereby removing non-lane road markings and some vehicle occlusions. Specific weighting operators are added during scanning to make lane line edge detection more complete.

[0050] Optionally, the binarized image is line-scanned using a preset imaging model to obtain the line-scan result, including:

[0051] The binarized image is scanned using a pre-determined lane width, adjacent lane width, and lane linearity features to obtain the line scan result.

[0052] Among them, lane line linearity features can refer to features such as lane line color, shape, and texture.

[0053] In this embodiment, the line scan results are obtained by selecting constraint blocks based on lane line width, adjacent lane line width, and lane line linearity characteristics. For example... Figure 2 As shown, the block spacing and block width of constraint blocks can be constructed based on lane line width, adjacent lane line width, and lane line linear features, thereby processing the binarized image based on the block spacing and block width.

[0054] Optionally, the binarized image is line-scanned based on a predetermined lane width, adjacent lane widths, and lane linear features to obtain a line-scanning result, including:

[0055] Constraints are constructed based on the predetermined lane width, adjacent lane width, and lane linearity characteristics.

[0056] The binarized image is scanned by line from top to bottom and left to right. The center point of the pixels that meet the constraints is retained to obtain the line scan result.

[0057] Specifically, the line scan results are determined by selecting constraint blocks based on lane width, adjacent lane width, and lane linearity characteristics. After filtering, the line scan results are obtained, and the characteristics of the constraint blocks are statistically analyzed, including their length, the width between adjacent blocks, and their positions. Lane marking widths are generally between 0.15 and 0.2 meters, while the distance between adjacent lane lines is generally between 2.5 and 4 meters. Therefore, the constraint block width constraints and the distance constraints between adjacent constraint blocks for the v-th line scan are as follows:

[0058] P l ∈(aΔu,bΔu),ΔX=0.15m;

[0059] L interval ∈(cΔu,dΔu),ΔX=3.5m;

[0060] Where Δμ is the distance between the v-th scan lines, which can be calculated using the camera imaging geometry model, and a, b, c, and d are control coefficients.

[0061] Specifically, the scanning is performed in the order of top to bottom and left to right. Constraint blocks that meet the constraints are retained by taking their center point, while constraint blocks that do not meet the constraints are removed.

[0062] This method can remove non-lane markings and some vehicle obstructions, providing a good foundation for the next step of probabilistic Hough transform.

[0063] S140. Based on the preset Hough transform, the line scan result is processed to detect straight lines to obtain lane lines.

[0064] Among them, the PHT (Proportional-Preference Transform) is a very important method for detecting the shape of discontinuities. It achieves the fitting of straight lines and curves by transforming the image coordinate space to the parameter space.

[0065] In this plan, such as Figure 2 As shown, the Hough transform is used to detect straight lines. It can specify the minimum line segment connection length and the maximum interval value of discontinuous line segments, which is beneficial for the detection of dashed lane lines and has better robustness for the detection of straight lines in the same lane.

[0066] The technical solution of this invention involves acquiring a lane image, then thresholding the lane image to obtain a binarized image. A preset imaging model is then used to perform line scanning on the binarized image to obtain the line scanning result. Finally, a preset Hough transform is used to detect straight lines on the line scanning result to obtain the lane line. By implementing this technical solution, utilizing the imaging model and Hough transform to detect lane lines, the lane line detection effect and robustness can be improved.

[0067] Example 2

[0068] Figure 3 This is a flowchart of a lane line detection process provided in Embodiment 2 of the present invention. The relationship between this embodiment and the above embodiments is a detailed description of the lane line detection process. Figure 3 As shown, the method includes:

[0069] S310, Obtain lane image.

[0070] S320. The lane image is processed by a hat transformation to obtain a processed lane image.

[0071] In this plan, such as Figure 2 As shown, after acquiring lane images through image acquisition equipment, the lane images are preprocessed using the top hat transformation in mathematical morphology, which can reduce the impact of changes in external lighting and complex road backgrounds on lane line detection.

[0072] In this embodiment, the distortion in the image caused by the camera's main optical axis not being perpendicular to the road surface can also be eliminated by using a geometric model, and the depth information lost during the projection process can also be estimated.

[0073] Specifically, the coordinates of the road surface in three-dimensional space are mapped to the coordinates of the images acquired by the monocular camera, so that the relative position of the road surface points in three-dimensional space can be determined by the relative position on the image. Figure 4 This is a schematic diagram illustrating the working principle of the camera provided in Embodiment 2 of this application, as shown below. Figure 4 As shown in (a), (b), and (c), assume W = {(X,Y,Z)∈E} 3 Let W be the world coordinate system. In W, the camera's coordinates are C(d,0,h), where h is the height of the camera lens relative to the ground. During installation, the camera's optical axis is parallel to the road surface, with an angle γ between it and the lane lines. α is the camera's maximum horizontal viewing angle, and β is the camera's maximum vertical viewing angle. The mapping model between any point I(μ,v) on the road surface and the road surface coordinates W(X,Y,0) is established as follows:

[0074]

[0075]

[0076]

[0077]

[0078] Where H I and W I These are the horizontal and vertical resolutions of the camera; the horizontal and vertical viewing angles can be obtained through calibration.

[0079] S330. Threshold the lane image to obtain a binarized image.

[0080] S340. Perform line scanning on the binarized image using a preset imaging model to obtain line scanning results; wherein, the imaging model is a model generated based on lane line features.

[0081] Furthermore, line scanning is performed on the binarized image. The line scanning results are then constrained by lane width, adjacent lane width, and lane linearity features, and filtered to obtain the final line scanning result. In perspective images, lane width decreases with increasing distance; lane widths are wider in the near field of view and narrower in the far field of view. The width between lane lines also follows this pattern: adjacent lane widths are wider in the near field of view and narrower in the far field of view. Combining this with the camera imaging geometry model, the distance Δu between the v-th scan lines can be derived as the distance ΔX in the world coordinate system.

[0082]

[0083] S350. Based on the preset Hough transform, the line scan result is processed to detect straight lines to obtain lane lines.

[0084] S360. Perform clustering processing on the lane lines to obtain the lane line slope.

[0085] In this scheme, after the Hough transform is used to process the line scan results into straight lines, some noisy lines will interfere with the recognition results, so cluster fitting is required.

[0086] Specifically, lane lines can be clustered. Since straight lines on the same lane line have similar slopes, when the slope difference between two straight lines is within a certain range, these two lines are considered to be on the same lane line and grouped into one category. After obtaining the slope of each category of lane lines, the midpoint of the line's pixel is calculated as a point, and finally, a lane line is fitted.

[0087] S370. Based on the slope of the lane line, the lane lines are filtered to obtain the target lane lines.

[0088] Specifically, the following formula can be used to filter lane lines;

[0089]

[0090] Where D represents the difference in slope between two straight lines, and Th(s) is the set threshold. When the difference in slope between two straight lines is within a certain range, they are considered to be on the same lane and grouped into one category; if the difference is too large, they are not on the same lane. The number of pixels of the same category of straight lines is counted. If the following formula is satisfied, it is considered a lane line; otherwise, it is considered noise. Then, after obtaining the slope of each category of lane lines, the midpoint of the line pixels is calculated as a point and fitted to form the final lane line.

[0091]

[0092] Where Th(l) is the set threshold.

[0093] The technical solution of this invention involves acquiring a lane image, performing a height-hat transform on the lane image to obtain a processed lane image, then thresholding the lane image to obtain a binarized image, and using a preset imaging model to perform line scanning on the binarized image to obtain line scanning results. Based on a preset Hough transform, the line scanning results are then processed to detect straight lines, resulting in lane lines. After obtaining the lane lines, clustering processing is performed on the lane lines to obtain their slopes, and then the lane lines are filtered based on their slopes to obtain target lane lines. By implementing this technical solution, utilizing the imaging model and Hough transform to detect lane lines, the lane line detection effect and robustness can be improved.

[0094] Example 3

[0095] Figure 5 This is a schematic diagram of the structure of a lane line detection device according to Embodiment 3 of the present invention. Figure 5 As shown, the device includes:

[0096] Lane image acquisition module 510 is used to acquire lane images;

[0097] The thresholding processing module 520 is used to perform thresholding processing on the lane image to obtain a binarized image.

[0098] The line scanning module 530 is used to perform line scanning on the binarized image using a preset imaging model to obtain a line scanning result; wherein, the imaging model is a model generated based on lane line features;

[0099] The lane line detection module 540 is used to perform straight line processing on the line scanning results based on a preset Hough transform to obtain lane lines.

[0100] Optional, the line scan module 530 includes:

[0101] The line scanning unit is used to perform line scanning on the binarized image based on the predetermined lane line width, adjacent lane line width, and lane line linear features to obtain the line scanning result.

[0102] Optional, line scan unit, specifically used for:

[0103] Constraints are constructed based on the predetermined lane width, adjacent lane width, and lane linearity characteristics.

[0104] The binarized image is scanned by line from top to bottom and left to right. The center point of the pixels that meet the constraints is retained to obtain the line scan result.

[0105] Optionally, the lane image acquisition module 510 is specifically used for:

[0106] Image acquisition equipment is used to acquire images of the road surface to obtain lane images.

[0107] Optionally, the device further includes:

[0108] The hat transformation processing module is used to process the lane image through hat transformation to obtain the processed lane image.

[0109] Optionally, the device further includes:

[0110] A clustering processing module is used to cluster the lane lines to obtain the lane line slope;

[0111] The lane line filtering module is used to filter the lane lines based on the lane line slope to obtain the target lane lines.

[0112] The lane line detection device provided in this embodiment of the invention can execute a lane line detection method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0113] Example 4

[0114] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0115] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0116] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0117] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a lane detection method.

[0118] In some embodiments, a lane detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the lane detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a lane detection method by any other suitable means (e.g., by means of firmware).

[0119] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0120] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0121] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0122] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0123] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0124] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0125] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0126] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A lane line detection method, characterized in that, include: Acquire lane images; The lane image is thresholded to obtain a binarized image; The binarized image is line-scanned using a preset imaging model to obtain the line-scanning result; wherein, the imaging model is a model generated based on lane line features; The line scan results are processed to detect straight lines based on a preset Hough transform to obtain lane lines. The binarized image is line-scanned using a preset imaging model to obtain the line-scan result, including: The binarized image is scanned according to the predetermined lane line width, adjacent lane line width, and lane line linear features to obtain the line scan result; Specifically, the binarized image is line-scanned based on a predetermined lane width, adjacent lane widths, and lane linear features to obtain line-scanning results, including: Constraints are constructed based on the predetermined lane width, adjacent lane width, and lane linearity characteristics. The binarized image is scanned by line from top to bottom and left to right. The center point of the pixels that meet the constraints is retained to obtain the line scan result.

2. The method according to claim 1, characterized in that, Acquire lane images, including: Image acquisition equipment is used to acquire images of the road surface to obtain lane images.

3. The method according to claim 1, characterized in that, After acquiring the lane image, the method further includes: The lane image is processed by a hat transformation to obtain a processed lane image.

4. The method according to claim 1, characterized in that, After performing straight line detection processing on the line scan results based on a preset Hough transform to obtain lane lines, the method further includes: The lane lines are clustered to obtain the lane line slopes; The lane lines are filtered based on the lane line slope to obtain the target lane lines.

5. A lane line detection device, characterized in that, include: Lane image acquisition module, used to acquire lane images; A thresholding module is used to perform thresholding processing on the lane image to obtain a binarized image; The line scanning module is used to perform line scanning on the binarized image using a preset imaging model to obtain line scanning results; wherein, the imaging model is a model generated based on lane line features; The lane line detection module is used to perform straight line processing on the line scanning results based on a preset Hough transform to obtain lane lines; The line scanning module includes: The line scanning unit is used to perform line scanning on the binarized image based on the predetermined lane line width, adjacent lane line width, and lane line linear features to obtain the line scanning result; Specifically, the line scanning unit is used for: Constraints are constructed based on the predetermined lane width, adjacent lane width, and lane linearity characteristics. The binarized image is scanned by line from top to bottom and left to right. The center point of the pixels that meet the constraints is retained to obtain the line scan result.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a lane line detection method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute and implement the lane line detection method according to any one of claims 1-4.

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