Lane line recognition method, system, computer and readable storage medium

Abstract

The application provides a lane line identification method, system, computer and readable storage medium, the method comprises: collecting real scene image in front of the current vehicle in real time through a preset camera, and performing a gray processing on the real scene image, so as to identify the road area and the non-road area in the real scene image; the road area is extracted, and the road area is processed twice to identify the driving area and the lane line area; the lane line area is subjected to image segmentation processing to obtain a lane line binary graph corresponding to the lane line area, and the lane line pixel points contained in the lane line binary graph are extracted to fit the lane line existing in the real scene image according to the lane line pixel points. Through the above mode, the requirement of the hardware equipment is lower, and the lane line identification time is greatly shortened, so that the lane line identification efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a lane line recognition method, system, computer, and readable storage medium. Background Technology

[0002] With the advancement of technology and the rapid development of productivity, automobiles have become ubiquitous in people's daily lives, greatly improving travel efficiency and becoming an indispensable means of transportation, thus facilitating people's lives.

[0003] In this context, cars need to travel in designated lanes, which are defined by different types of lane markings. Therefore, existing technologies often incorporate intelligent driver assistance features into cars to identify the lane markings on which the vehicle is traveling.

[0004] Existing technologies for lane line recognition first acquire road images and then process these images using numerous neural network models and recognition algorithms to identify lane lines. However, this method requires sophisticated hardware and consumes considerable computation time, thus reducing the efficiency of lane line recognition and consequently diminishing the user's driving experience. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a lane line recognition method, system, computer, and readable storage medium to solve the problem that the existing recognition methods require high-end hardware and consume a lot of computing time, which reduces the efficiency of lane line recognition.

[0006] A first aspect of this invention provides a lane line recognition method, the method comprising:

[0007] The system uses a preset camera device to capture real-time images of the scene in front of the vehicle and performs grayscale processing on the real-scene images to identify road areas and non-road areas in the real-scene images.

[0008] The road region is extracted and then subjected to secondary grayscale processing to identify the driving area and lane line area within the road region.

[0009] The lane line region is segmented to obtain a lane line binary image corresponding to the lane line region, and the lane line pixels contained in the lane line binary image are extracted to fit the lane lines existing in the real scene image based on the lane line pixels.

[0010] The beneficial effects of this invention are as follows: A preset camera device acquires real-time images of the scene in front of the vehicle, and performs a first grayscale processing on the real-scene image to identify road and non-road areas. Further, the road area is extracted, and a second grayscale processing is performed on the road area to identify the driving area and lane line area. Finally, only image segmentation processing is performed on the lane line area to obtain a lane line binary image corresponding to the lane line area, and the lane line pixels contained in the lane line binary image are extracted to fit the lane lines present in the real-scene image based on the lane line pixels. By using the above method, given the acquisition of a real-scene image, only one and two grayscale processing steps are needed to easily and effectively identify the lane line area in the current real-scene image, and finally fit the required lane lines based on the lane line pixels in the lane line area. This reduces the requirements for hardware equipment, significantly shortens the lane line recognition time, improves the efficiency of lane line recognition, and enhances the user's driving experience.

[0011] Preferably, the step of performing grayscale processing on the real-scene image to identify road areas and non-road areas in the real-scene image includes:

[0012] When the real-scene image is acquired, the real-scene image is processed into grayscale based on the maximum value method to generate a corresponding first grayscale image, and all first pixels in the first grayscale image are detected. Each first pixel has a corresponding pixel value.

[0013] Determine whether the pixel value corresponding to each of the first pixels is greater than a first preset threshold.

[0014] If it is determined that there are several first pixel points whose corresponding pixel values ​​are greater than the first preset threshold, then the pixel regions corresponding to these several first pixel points are set as the road region.

[0015] Preferably, the step of performing secondary grayscale processing on the road area to identify the driving area and lane line area in the road area includes:

[0016] When the road area is obtained, the road area is subjected to secondary grayscale processing based on the weighted average method to generate a corresponding second grayscale image, and all second pixels in the second grayscale image are detected. Each second pixel has a corresponding pixel value.

[0017] Determine whether the pixel value corresponding to each second pixel point is greater than the second preset threshold;

[0018] If it is determined that there are several second pixel points whose corresponding pixel values ​​are greater than the second preset threshold, then the pixel regions corresponding to these several second pixel points are set as the lane line regions.

[0019] Preferably, the step of performing image segmentation processing on the lane line region to obtain a lane line binary image corresponding to the lane line region includes:

[0020] When the lane line region is acquired, the lane line region is converted into an image space with a brightness channel to obtain the corresponding lane line space image.

[0021] The first edge information of the lane line spatial image is obtained based on the vertical direction of the brightness channel, and the second edge information of the lane line spatial image is obtained based on the horizontal direction of the brightness channel.

[0022] The first edge information and the second edge information are smoothed to obtain lane line edge information corresponding to the lane line spatial image, and the lane line binary image is generated based on the lane line edge information.

[0023] Preferably, after the step of extracting the lane line pixels contained in the lane line binary image and fitting the lane lines existing in the real-world image based on the lane line pixels, the method further includes:

[0024] When the lane lines in the real-world image are identified, image enhancement and mosaic stitching are performed on the lane lines in sequence to restore the lane lines to both sides of the driving area.

[0025] The driving area and the lane lines are simultaneously converted into corresponding display signals, and the display signals are transmitted in real time to the display terminal inside the vehicle so as to display the driving area and the lane lines in real time on the display terminal.

[0026] A second aspect of this invention provides a lane line recognition system, the system comprising:

[0027] The acquisition module is used to acquire real-time images of the scene in front of the vehicle through a preset camera device, and to perform grayscale processing on the real-scene images to identify the road area and non-road area in the real-scene images.

[0028] An extraction module is used to extract the road area and perform secondary grayscale processing on the road area to identify the driving area and lane line area in the road area.

[0029] The processing module is used to perform image segmentation processing on the lane line region to obtain a lane line binary image corresponding to the lane line region, and extract the lane line pixels contained in the lane line binary image, so as to fit the lane lines existing in the real scene image based on the lane line pixels.

[0030] In the aforementioned lane line recognition system, the acquisition module is specifically used for:

[0031] When the real-scene image is acquired, the real-scene image is processed into grayscale based on the maximum value method to generate a corresponding first grayscale image, and all first pixels in the first grayscale image are detected. Each first pixel has a corresponding pixel value.

[0032] Determine whether the pixel value corresponding to each of the first pixels is greater than a first preset threshold.

[0033] If it is determined that there are several first pixel points whose corresponding pixel values ​​are greater than the first preset threshold, then the pixel regions corresponding to these several first pixel points are set as the road region.

[0034] In the aforementioned lane line recognition system, the extraction module is specifically used for:

[0035] When the road area is obtained, the road area is subjected to secondary grayscale processing based on the weighted average method to generate a corresponding second grayscale image, and all second pixels in the second grayscale image are detected. Each second pixel has a corresponding pixel value.

[0036] Determine whether the pixel value corresponding to each second pixel point is greater than the second preset threshold;

[0037] If it is determined that there are several second pixel points whose corresponding pixel values ​​are greater than the second preset threshold, then the pixel regions corresponding to these several second pixel points are set as the lane line regions.

[0038] In the aforementioned lane line recognition system, the processing module is specifically used for:

[0039] When the lane line region is acquired, the lane line region is converted into an image space with a brightness channel to obtain the corresponding lane line space image.

[0040] The first edge information of the lane line spatial image is obtained based on the vertical direction of the brightness channel, and the second edge information of the lane line spatial image is obtained based on the horizontal direction of the brightness channel.

[0041] The first edge information and the second edge information are smoothed to obtain lane line edge information corresponding to the lane line spatial image, and the lane line binary image is generated based on the lane line edge information.

[0042] In the aforementioned lane line recognition system, the lane line recognition system further includes a display module, which is specifically used for:

[0043] When the lane lines in the real-world image are identified, image enhancement and mosaic stitching are performed on the lane lines in sequence to restore the lane lines to both sides of the driving area.

[0044] The driving area and the lane lines are simultaneously converted into corresponding display signals, and the display signals are transmitted in real time to the display terminal inside the vehicle so as to display the driving area and the lane lines in real time on the display terminal.

[0045] A third aspect of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the lane line recognition method as described above.

[0046] A fourth aspect of the present invention provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lane line recognition method as described above.

[0047] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0048] Figure 1 A flowchart of the lane line recognition method provided in the first embodiment of the present invention;

[0049] Figure 2 This is a structural block diagram of the lane line recognition system provided in the sixth embodiment of the present invention.

[0050] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0051] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0052] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0054] Existing technologies for lane line recognition first acquire road images and then process these images using numerous neural network models and recognition algorithms to identify lane lines. However, this method requires sophisticated hardware and consumes considerable computation time, thus reducing the efficiency of lane line recognition and consequently diminishing the user's driving experience.

[0055] Please see Figure 1 The image shows a lane line recognition method provided in the first embodiment of the present invention. Under the premise of acquiring a real-scene image, the lane line recognition method provided in this embodiment can simply and effectively identify the lane line region in the current real-scene image by performing grayscale processing once and grayscale processing twice on the current real-scene image. Finally, the required lane line is fitted based on the lane line pixels in the lane line region. Therefore, the requirements for hardware devices are low, and the lane line recognition time is greatly shortened, thereby improving the efficiency of lane line recognition and enhancing the user's driving experience.

[0056] Specifically, the lane line recognition method provided in this embodiment includes the following steps:

[0057] Step S10: Real-time image of the scene in front of the vehicle is captured by a preset camera device, and the real-scene image is processed into grayscale to identify the road area and non-road area in the real-scene image.

[0058] Specifically, in this embodiment, it should first be noted that the lane line recognition method provided in this embodiment is specifically applied to vehicles equipped with vehicle-mounted camera devices. That is, this embodiment requires the camera device to collect real-time images of the scene in front of the current vehicle. Preferably, the aforementioned preset camera device can be a dashcam or a camera or other device capable of capturing images.

[0059] In addition, the lane line recognition method provided in this embodiment is based on a vehicle controller installed inside the vehicle. The vehicle controller can receive real-time images captured by the aforementioned preset camera device and immediately process the real-time images to ultimately identify the required lane lines in the real-time images.

[0060] Therefore, it should be noted that in this step, the real-time image of the current vehicle in front is first acquired by the aforementioned preset camera device. Specifically, the real-time image includes the road in front of the current vehicle and objects located on both sides of the road. Based on this, this step needs to first identify the road in the current real-time image. Only on this basis can the lane lines in the road be further identified.

[0061] Furthermore, this step will first perform grayscale processing on the real-scene image to initially identify the required road areas and non-road areas in the real-scene image.

[0062] Step S20: Extract the road area and perform secondary grayscale processing on the road area to identify the driving area and lane line area in the road area.

[0063] Furthermore, in this embodiment, it should be noted that after obtaining the road area through the above steps, this step requires further grayscale processing of the road area, that is, it is necessary to further identify the required driving area and lane line area in the road area.

[0064] Step S30: Perform image segmentation processing on the lane line region to obtain a lane line binary image corresponding to the lane line region, and extract the lane line pixels contained in the lane line binary image to fit the lane lines existing in the real scene image based on the lane line pixels.

[0065] Finally, it should be noted that after obtaining the lane line region through the above steps, this step will further perform image segmentation processing on the lane line region, that is, obtain the lane line binary image corresponding to the current lane line region. Based on this, this step can further extract the lane line pixels contained in the lane line binary image, and then fit the lane lines existing in the above real scene image based on the lane line pixels.

[0066] In operation, a preset camera device captures real-time images of the scene ahead of the vehicle. The images are then processed into grayscale to identify road and non-road areas. Next, the road area is extracted and processed into grayscale again to identify the driving area and lane lines. Finally, only the lane line area needs image segmentation to obtain a binary image of the lane lines. The lane line pixels within this binary image are then extracted to fit the lane lines in the real-world image. This method, by simply performing one and two grayscale processing steps on the real-world image, allows for simple and effective identification of lane line areas. Finally, the required lane lines are fitted based on the lane line pixels within these areas. This approach reduces hardware requirements, significantly shortens lane line recognition time, improves recognition efficiency, and enhances the user's driving experience.

[0067] It should be noted that the above implementation process is only to illustrate the feasibility of this application, but it does not mean that the lane line recognition method of this application has only the above-mentioned unique implementation process. On the contrary, as long as the lane line recognition method of this application can be implemented, it can be included in the feasible implementation scheme of this application.

[0068] In summary, the lane line recognition method provided by the above embodiments of the present invention, under the premise of acquiring a real-scene image, only needs to perform one grayscale processing and two grayscale processing on the current real-scene image to simply and effectively identify the lane line region in the current real-scene image, and finally fit the required lane line based on the lane line pixels in the lane line region. Therefore, the requirements for hardware devices are low, while significantly shortening the lane line recognition time, thereby improving the efficiency of lane line recognition and enhancing the user's driving experience.

[0069] The second embodiment of the present invention also provides a lane line recognition method. The lane line recognition method provided in this embodiment differs from the lane line recognition method provided in the first embodiment above in that:

[0070] Furthermore, in this embodiment, it should be noted that the step of performing grayscale processing on the real-scene image to identify road areas and non-road areas in the real-scene image includes:

[0071] When the real-scene image is acquired, the real-scene image is processed into grayscale based on the maximum value method to generate a corresponding first grayscale image, and all first pixels in the first grayscale image are detected. Each first pixel has a corresponding pixel value.

[0072] Determine whether the pixel value corresponding to each of the first pixels is greater than a first preset threshold.

[0073] If it is determined that there are several first pixel points whose corresponding pixel values ​​are greater than the first preset threshold, then the pixel regions corresponding to these several first pixel points are set as the road region.

[0074] Specifically, in this embodiment, it should be noted that since the real-time images captured by the camera device are color images, and color images are not conducive to identifying the position of lane lines, this embodiment needs to perform grayscale processing on the current real-time images to improve the accuracy of recognition.

[0075] Based on this, this embodiment will perform grayscale processing on the current real-world image using the existing maximum value method to generate the required first grayscale image. At the same time, all pixels in the current first grayscale image will be detected simultaneously, and each pixel will correspond to a pixel value.

[0076] Based on this, it is further determined whether the pixel value corresponding to each first pixel point is greater than the pre-set first preset threshold. It should be noted that since the pixel value of the road is higher than the pixel value of the objects on both sides of the road, this characteristic can be used to quickly filter out the road in the current real scene image.

[0077] Specifically, if it is determined in real time that the pixel value corresponding to several first pixels is greater than the first preset threshold, it means that the image of the road corresponds to the several first pixels. Therefore, in this embodiment, the pixel area corresponding to the current several first pixels will be immediately set as the road area.

[0078] It should be noted that the method provided in the second embodiment of the present invention has the same implementation principle and some technical effects as the first embodiment. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content provided in the first embodiment.

[0079] In summary, the lane line recognition method provided by the above embodiments of the present invention, under the premise of acquiring a real-scene image, only needs to perform one grayscale processing and two grayscale processing on the current real-scene image to simply and effectively identify the lane line region in the current real-scene image, and finally fit the required lane line based on the lane line pixels in the lane line region. Therefore, the requirements for hardware devices are low, while significantly shortening the lane line recognition time, thereby improving the efficiency of lane line recognition and enhancing the user's driving experience.

[0080] The third embodiment of the present invention also provides a lane line recognition method. The lane line recognition method provided in this embodiment differs from the lane line recognition method provided in the first embodiment above in that:

[0081] Furthermore, in this embodiment, it should be noted that the step of performing secondary grayscale processing on the road area to identify the driving area and lane line area in the road area includes:

[0082] When the road area is obtained, the road area is subjected to secondary grayscale processing based on the weighted average method to generate a corresponding second grayscale image, and all second pixels in the second grayscale image are detected. Each second pixel has a corresponding pixel value.

[0083] Determine whether the pixel value corresponding to each second pixel point is greater than the second preset threshold;

[0084] If it is determined that there are several second pixel points whose corresponding pixel values ​​are greater than the second preset threshold, then the pixel regions corresponding to these several second pixel points are set as the lane line regions.

[0085] Similarly, in this embodiment, it should be noted that after obtaining the road area, since the pixel value corresponding to the existing lane line is greater than the pixel value of the road, in order to accurately identify the location of the lane line in the current road area, this embodiment needs to further perform secondary grayscale processing on the current road area.

[0086] Specifically, in this embodiment, the current road area will be subjected to secondary grayscale processing using the existing weighted average method to generate the required second grayscale image. Similarly, all second pixels in the current second grayscale image will be detected simultaneously, and each second pixel will correspond to a pixel value.

[0087] Based on this, it is only necessary to further determine whether the pixel value corresponding to the current second pixel is greater than the second preset threshold. If so, it means that the pixel area corresponding to the current second pixel is the area where the lane line is located, so that the pixel areas corresponding to the current number of second pixels can be further set as the aforementioned lane line area.

[0088] It should be noted that the method provided in the third embodiment of the present invention has the same implementation principle and some technical effects as the first embodiment. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content provided in the first embodiment.

[0089] In summary, the lane line recognition method provided by the above embodiments of the present invention, under the premise of acquiring a real-scene image, only needs to perform one grayscale processing and two grayscale processing on the current real-scene image to simply and effectively identify the lane line region in the current real-scene image, and finally fit the required lane line based on the lane line pixels in the lane line region. Therefore, the requirements for hardware devices are low, while significantly shortening the lane line recognition time, thereby improving the efficiency of lane line recognition and enhancing the user's driving experience.

[0090] The fourth embodiment of the present invention also provides a lane line recognition method. The lane line recognition method provided in this embodiment differs from the lane line recognition method provided in the first embodiment above in that:

[0091] Furthermore, in this embodiment, it should be noted that the steps of performing image segmentation processing on the lane line region to obtain the lane line binary image corresponding to the lane line region include:

[0092] When the lane line region is acquired, the lane line region is converted into an image space with a brightness channel to obtain the corresponding lane line space image.

[0093] The first edge information of the lane line spatial image is obtained based on the vertical direction of the brightness channel, and the second edge information of the lane line spatial image is obtained based on the horizontal direction of the brightness channel.

[0094] The first edge information and the second edge information are smoothed to obtain lane line edge information corresponding to the lane line spatial image, and the lane line binary image is generated based on the lane line edge information.

[0095] In addition, it should be noted that in order to transform the lane line area from a two-dimensional image to a three-dimensional image, this embodiment will further convert the current lane line area into an image space with a brightness channel, thereby obtaining the corresponding lane line space image.

[0096] Based on this, since the above-mentioned brightness channel has a certain directionality, that is, the vertical and horizontal directions of the brightness channel can be identified, this embodiment can obtain the first edge information of the lane line space image based on the vertical direction of the current brightness channel. Similarly, it can obtain the second edge information of the lane line space image based on the horizontal direction of the brightness channel.

[0097] Based on this, this embodiment can obtain the required lane line edge information simply by using the current first edge information and the second edge information, and thus can finally obtain the required lane line binary map based on the lane line edge information.

[0098] It should be noted that the method provided in the fourth embodiment of the present invention has the same implementation principle and some technical effects as the first embodiment. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content provided in the first embodiment.

[0099] In summary, the lane line recognition method provided by the above embodiments of the present invention, under the premise of acquiring a real-scene image, only needs to perform one grayscale processing and two grayscale processing on the current real-scene image to simply and effectively identify the lane line region in the current real-scene image, and finally fit the required lane line based on the lane line pixels in the lane line region. Therefore, the requirements for hardware devices are low, while significantly shortening the lane line recognition time, thereby improving the efficiency of lane line recognition and enhancing the user's driving experience.

[0100] The fifth embodiment of the present invention also provides a lane line recognition method. The lane line recognition method provided in this embodiment differs from the lane line recognition method provided in the first embodiment above in that:

[0101] Furthermore, in this embodiment, it should be noted that after the step of extracting the lane line pixels contained in the lane line binary image and fitting the lane lines existing in the real-world image based on the lane line pixels, the method further includes:

[0102] When the lane lines in the real-world image are identified, image enhancement and mosaic stitching are performed on the lane lines in sequence to restore the lane lines to both sides of the driving area.

[0103] The driving area and the lane lines are simultaneously converted into corresponding display signals, and the display signals are transmitted in real time to the display terminal inside the vehicle so as to display the driving area and the lane lines in real time on the display terminal.

[0104] In addition, it should be noted in this embodiment that after identifying the lane lines in the current real-world image, in order to enable the driver to observe the changes in the lane lines on both sides of the vehicle in real time, this embodiment will further perform image enhancement and mosaic stitching processing on the acquired lane lines, so as to restore the lane lines to both sides of the driving area.

[0105] Based on this, the current driving area and lane lines are simultaneously converted into corresponding display signals. At the same time, the display signals are transmitted in real time to the display terminal inside the vehicle, which allows the current driving area and lane lines of the vehicle to be displayed in real time on the current display terminal simply and quickly.

[0106] It should be noted that the method provided in the fifth embodiment of the present invention has the same implementation principle and some technical effects as the first embodiment. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content provided in the first embodiment.

[0107] In summary, the lane line recognition method provided by the above embodiments of the present invention, under the premise of acquiring a real-scene image, only needs to perform one grayscale processing and two grayscale processing on the current real-scene image to simply and effectively identify the lane line region in the current real-scene image, and finally fit the required lane line based on the lane line pixels in the lane line region. Therefore, the requirements for hardware devices are low, while significantly shortening the lane line recognition time, thereby improving the efficiency of lane line recognition and enhancing the user's driving experience.

[0108] Please see Figure 2 The image shows a lane line recognition system provided in the sixth embodiment of the present invention. The system includes:

[0109] The acquisition module 12 is used to acquire real-time images of the scene in front of the current vehicle through a preset camera device, and to perform grayscale processing on the real-scene images to identify the road area and non-road area in the real-scene images.

[0110] Extraction module 22 is used to extract the road area and perform secondary grayscale processing on the road area to identify the driving area and lane line area in the road area.

[0111] The processing module 32 is used to perform image segmentation processing on the lane line region to obtain a lane line binary map corresponding to the lane line region, and extract the lane line pixels contained in the lane line binary map, so as to fit the lane lines existing in the real scene image based on the lane line pixels.

[0112] In the lane line recognition system described above, the acquisition module 12 is specifically used for:

[0113] When the real-scene image is acquired, the real-scene image is processed into grayscale based on the maximum value method to generate a corresponding first grayscale image, and all first pixels in the first grayscale image are detected. Each first pixel has a corresponding pixel value.

[0114] Determine whether the pixel value corresponding to each of the first pixels is greater than a first preset threshold.

[0115] If it is determined that there are several first pixel points whose corresponding pixel values ​​are greater than the first preset threshold, then the pixel regions corresponding to these several first pixel points are set as the road region.

[0116] In the lane line recognition system described above, the extraction module 22 is specifically used for:

[0117] When the road area is obtained, the road area is subjected to secondary grayscale processing based on the weighted average method to generate a corresponding second grayscale image, and all second pixels in the second grayscale image are detected. Each second pixel has a corresponding pixel value.

[0118] Determine whether the pixel value corresponding to each second pixel point is greater than the second preset threshold;

[0119] If it is determined that there are several second pixel points whose corresponding pixel values ​​are greater than the second preset threshold, then the pixel regions corresponding to these several second pixel points are set as the lane line regions.

[0120] In the lane line recognition system described above, the processing module 32 is specifically used for:

[0121] When the lane line region is acquired, the lane line region is converted into an image space with a brightness channel to obtain the corresponding lane line space image.

[0122] The first edge information of the lane line spatial image is obtained based on the vertical direction of the brightness channel, and the second edge information of the lane line spatial image is obtained based on the horizontal direction of the brightness channel.

[0123] The first edge information and the second edge information are smoothed to obtain lane line edge information corresponding to the lane line spatial image, and the lane line binary image is generated based on the lane line edge information.

[0124] In the lane line recognition system described above, the lane line recognition system further includes a display module 42, which is specifically used for:

[0125] When the lane lines in the real-world image are identified, image enhancement and mosaic stitching are performed on the lane lines in sequence to restore the lane lines to both sides of the driving area.

[0126] The driving area and the lane lines are simultaneously converted into corresponding display signals, and the display signals are transmitted in real time to the display terminal inside the vehicle so as to display the driving area and the lane lines in real time on the display terminal.

[0127] The seventh embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the lane line recognition method provided in the above embodiments.

[0128] The eighth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lane line recognition method as provided in the above embodiments.

[0129] In summary, the lane line recognition method, system, computer, and readable storage medium provided in the above embodiments of the present invention, under the premise of acquiring a real-scene image, only need to perform one grayscale processing and two grayscale processing on the current real-scene image to simply and effectively identify the lane line region in the current real-scene image, and finally fit the required lane line based on the lane line pixels in the lane line region. Therefore, the requirements for hardware devices are low, while significantly shortening the lane line recognition time, thereby improving the efficiency of lane line recognition and enhancing the user's driving experience.

[0130] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0131] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0132] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0133] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0134] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0135] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A lane line recognition method, characterized in that, The method includes: The system uses a preset camera device to capture real-time images of the scene in front of the vehicle and performs grayscale processing on the real-scene images to identify road areas and non-road areas in the real-scene images. The road region is extracted and then subjected to secondary grayscale processing to identify the driving area and lane line area within the road region. The lane line region is subjected to image segmentation processing to obtain a lane line binary image corresponding to the lane line region, and the lane line pixels contained in the lane line binary image are extracted to fit the lane lines existing in the real scene image based on the lane line pixels. The step of performing grayscale processing on the real-scene image to identify road areas and non-road areas in the real-scene image includes: When the real-scene image is acquired, the real-scene image is processed into grayscale based on the maximum value method to generate a corresponding first grayscale image, and all first pixels in the first grayscale image are detected. Each first pixel has a corresponding pixel value. Determine whether the pixel value corresponding to each of the first pixels is greater than a first preset threshold. If it is determined that there are several first pixel points whose corresponding pixel values ​​are greater than the first preset threshold, then the pixel regions corresponding to the several first pixel points are set as the road region. The step of performing secondary grayscale processing on the road area to identify the driving area and lane line area in the road area includes: When the road area is obtained, the road area is subjected to secondary grayscale processing based on the weighted average method to generate a corresponding second grayscale image, and all second pixels in the second grayscale image are detected. Each second pixel has a corresponding pixel value. Determine whether the pixel value corresponding to each second pixel point is greater than the second preset threshold; If it is determined that there are several second pixel points whose corresponding pixel values ​​are greater than the second preset threshold, then the pixel region corresponding to the several second pixel points is set as the lane line region; The step of performing image segmentation processing on the lane line region to obtain a lane line binary image corresponding to the lane line region includes: When the lane line region is acquired, the lane line region is converted into an image space with a brightness channel to obtain the corresponding lane line space image. The first edge information of the lane line spatial image is obtained based on the vertical direction of the brightness channel, and the second edge information of the lane line spatial image is obtained based on the horizontal direction of the brightness channel. The first edge information and the second edge information are smoothed to obtain lane line edge information corresponding to the lane line spatial image, and the lane line binary image is generated based on the lane line edge information.

2. The lane line recognition method according to claim 1, characterized in that: After the step of extracting the lane line pixels contained in the lane line binary image and fitting the lane lines existing in the real scene image based on the lane line pixels, the method further includes: When the lane lines in the real-world image are identified, image enhancement and mosaic stitching are performed on the lane lines in sequence to restore the lane lines to both sides of the driving area. The driving area and the lane lines are simultaneously converted into corresponding display signals, and the display signals are transmitted in real time to the display terminal inside the vehicle so as to display the driving area and the lane lines in real time on the display terminal.

3. A lane line recognition system, characterized in that, For implementing the lane line recognition method as described in any one of claims 1 to 2, the system comprises: The acquisition module is used to acquire real-time images of the scene in front of the vehicle through a preset camera device, and to perform grayscale processing on the real-scene images to identify the road area and non-road area in the real-scene images. An extraction module is used to extract the road area and perform secondary grayscale processing on the road area to identify the driving area and lane line area in the road area. The processing module is used to perform image segmentation processing on the lane line region to obtain a lane line binary map corresponding to the lane line region, and extract the lane line pixels contained in the lane line binary map, so as to fit the lane lines existing in the real scene image based on the lane line pixels. The acquisition module is specifically used for: When the real-scene image is acquired, the real-scene image is processed into grayscale based on the maximum value method to generate a corresponding first grayscale image, and all first pixels in the first grayscale image are detected. Each first pixel has a corresponding pixel value. Determine whether the pixel value corresponding to each of the first pixels is greater than a first preset threshold. If it is determined that there are several first pixel points whose corresponding pixel values ​​are greater than the first preset threshold, then the pixel regions corresponding to the several first pixel points are set as the road region. The extraction module is specifically used for: When the road area is obtained, the road area is subjected to secondary grayscale processing based on the weighted average method to generate a corresponding second grayscale image, and all second pixels in the second grayscale image are detected. Each second pixel has a corresponding pixel value. Determine whether the pixel value corresponding to each second pixel point is greater than the second preset threshold; If it is determined that there are several second pixel points whose corresponding pixel values ​​are greater than the second preset threshold, then the pixel regions corresponding to these several second pixel points are set as the lane line regions.

4. A computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the lane line recognition method as described in any one of claims 1 to 2.

5. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the lane line recognition method as described in any one of claims 1 to 2.

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