A vehicle wiper control method, apparatus, device, medium and product

Abstract

The application discloses a vehicle wiper control method, device, equipment, medium and product. The method comprises the following steps: during the operation of the vehicle, if a wiper control instruction is detected, an image is collected by a vehicle-mounted camera to obtain a target image containing a front windshield of the vehicle; an initial raindrop coverage rate corresponding to the target image is determined, and the target image is iteratively divided based on the initial raindrop coverage rate and an iterative bisection method until a divided region image meets an end recursion condition; a rainfall estimation value is determined according to a target recursion depth at the end of recursion, and the wiper frequency of the vehicle is controlled according to the determined rainfall estimation value. The technical scheme of the application can improve the intelligent level of wiper control and improve driving safety and comfort by dynamically adjusting the wiper frequency through real-time analysis of the raindrop situation on the front windshield.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to a method, device, equipment, medium, and product for controlling vehicle windshield wipers. Background Technology

[0002] In rainy weather, proper control of windshield wiper frequency is crucial for driving safety and clear visibility. Currently, most traditional windshield wiper control methods are based on time intervals or simple rain sensors, which suffer from slow response and insufficient accuracy.

[0003] Therefore, how to analyze the raindrops on the windshield in real time, dynamically adjust the wiper frequency, and improve the intelligence level of wiper control to enhance driving safety and comfort is an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a vehicle windshield wiper control method, device, equipment, medium, and product to improve the intelligence level of windshield wiper control and enhance driving safety and comfort.

[0005] According to one aspect of the present invention, a vehicle windshield wiper control method is provided, comprising:

[0006] If a wiper control command is detected during vehicle operation, an image is captured by the onboard camera to obtain a target image containing the vehicle's windshield.

[0007] Determine the initial raindrop coverage corresponding to the target image, and based on the initial raindrop coverage, iteratively divide the target image using the iterative binary division method until there are regions of the image that satisfy the termination recursion condition.

[0008] The estimated rainfall is determined based on the target recursion depth at the end of the recursion, and the vehicle's windshield wiper frequency is controlled based on the determined estimated rainfall.

[0009] According to another aspect of the present invention, a vehicle wiper control device is provided, comprising:

[0010] The acquisition module is used to acquire images through the vehicle camera when a wiper control command is detected during vehicle operation, so as to obtain a target image including the vehicle's windshield.

[0011] The iterative module is used to determine the initial raindrop coverage of the target image, and based on the initial raindrop coverage, iteratively divides the target image using the iterative binary method until there are regions of the image that satisfy the termination recursion condition.

[0012] The control module is used to determine the rainfall estimate based on the target recursion depth at the end of the recursion, and to control the vehicle's windshield wiper frequency based on the determined rainfall estimate.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] 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 the vehicle wiper control method according to any embodiment of the present invention.

[0017] 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 the vehicle wiper control method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, the computer program product including a computer program that, when executed by a processor, implements the vehicle wiper control method of any embodiment of the present invention.

[0019] The technical solution of this invention involves the following steps: During vehicle operation, if a wiper control command is detected, an image is captured via an onboard camera to obtain a target image containing the vehicle's windshield. An initial raindrop coverage rate corresponding to the target image is determined, and based on this initial raindrop coverage rate, the target image is iteratively divided using an iterative binary search method until a divided region satisfies the termination condition. A rainfall estimate is determined based on the target recursion depth at the termination of recursion, and the vehicle's wiper frequency is controlled according to this rainfall estimate. By analyzing the raindrop situation on the windshield in real time and dynamically adjusting the wiper frequency, the intelligence level of wiper control can be improved, enhancing driving safety and comfort.

[0020] 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

[0021] 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.

[0022] Figure 1 This is a flowchart of a vehicle windshield wiper control method provided in Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a vehicle windshield wiper control method provided in Embodiment 2 of the present invention;

[0024] Figure 3 This is a structural block diagram of a vehicle windshield wiper control device provided in Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0026] 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.

[0027] It should be noted that the terms "first," "second," "target," "candidate," and "alternative," etc., used 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 the 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 non-exclusive inclusion; for example, a process, method, system, product, or device that includes 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 devices. The acquisition, storage, use, and processing of data in the technical solutions of this application all comply with the relevant provisions of national laws and regulations.

[0028] Example 1

[0029] Figure 1This is a flowchart of a vehicle wiper control method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where the vehicle estimates rainfall during driving and adjusts the wiper frequency accordingly. The method can be executed by a vehicle wiper control device, which can be implemented in hardware and / or software. This vehicle wiper control device can be configured in an electronic device, such as in a vehicle, and executed by the vehicle's electronic control unit. Figure 1 As shown, the vehicle wiper control method includes:

[0030] S101. During vehicle operation, if a wiper control command is detected, an image is captured by the onboard camera to obtain a target image containing the vehicle's windshield.

[0031] The wiper control command is an instruction to control the vehicle's wiper frequency by instructing image acquisition and rainfall estimation. The vehicle-mounted camera refers to a high-definition camera located at the front of the vehicle to acquire images of the windshield. The target image refers to the image used for rainfall estimation; specifically, it can be an image acquired from the windshield or an image obtained after preprocessing the acquired image.

[0032] Optionally, the vehicle's electronic control unit can automatically generate wiper control commands in real time to execute the steps of S101-S103 of the present invention and control the vehicle's wiper frequency; it can also automatically generate wiper control commands periodically based on a preset cycle; or it can generate wiper control commands when it receives a wiper control request from the driver to execute the steps of S101-S103 of the present invention and control the vehicle's wiper frequency. The driver can actively send a wiper control request to the vehicle when he feels that it is about to rain or when it is already raining.

[0033] Optionally, image acquisition is performed using an in-vehicle camera to obtain a target image containing the vehicle's windshield, including: acquiring an image of the vehicle's windshield using the in-vehicle camera to obtain a candidate image; performing grayscale processing on the candidate image to obtain a grayscale image, and applying a Gaussian filter to filter the grayscale image to obtain a filtered image; performing image segmentation processing on the filtered image to remove the background area other than the windshield area from the filtered image to obtain the target image.

[0034] Here, candidate images refer to the preliminary images acquired by the vehicle-mounted camera; candidate images can be, for example, color images. Filtered images refer to the images obtained after denoising grayscale images using a Gaussian filter.

[0035] Optionally, a preset image recognition algorithm can be used to identify the filtered image, determine the windshield area and background area in the filtered image, and then a preset image segmentation algorithm can be used to remove the background area other than the windshield area in the filtered image to obtain the target image.

[0036] It should be noted that converting color images to grayscale images can effectively reduce the amount of computation. At the same time, using a Gaussian filter for denoising can smooth the image and effectively reduce noise interference, thereby improving the accuracy of subsequent rainfall estimation results.

[0037] S102. Determine the initial raindrop coverage rate corresponding to the target image, and based on the initial raindrop coverage rate, iteratively divide the target image using the iterative binary division method until there are divided regions that satisfy the termination recursion condition.

[0038] The initial raindrop coverage rate refers to the raindrop coverage rate calculated based on the contrast, blurriness, edge point ratio, and preset weighting coefficients of the target image without prior segmentation. The initial raindrop coverage rate is used to evaluate the raindrop coverage of the entire target image. The iterative bisection method refers to iteratively segmenting the target image until the raindrop coverage rate of a segmented region exceeds a preset coverage threshold. A region image refers to a portion of the target image obtained through segmentation. The termination recursion condition is the condition for evaluating whether to end the iterative segmentation. Specifically, the termination recursion condition can be that at least one region image in a segmented region image has a region raindrop coverage rate greater than the preset coverage threshold, or that the recursion depth reaches the maximum recursion depth.

[0039] Optionally, after each round of iterative segmentation of the target image, it can be determined whether the raindrop coverage rate of the region is greater than a preset coverage threshold. If not, it can be further determined whether the current recursion depth has reached the maximum recursion depth. If the maximum recursion depth has been reached, the recursion can be terminated. If the maximum recursion depth has not been reached, iterative segmentation can continue. Furthermore, if the raindrop coverage rate of the corresponding region is greater than the preset coverage threshold, it is not necessary to calculate whether the current recursion depth has reached the maximum recursion depth, and the recursion can be terminated directly.

[0040] Optionally, determining the initial raindrop coverage corresponding to the target image includes: determining the proportion of edge points corresponding to the target image based on a preset edge detection algorithm, and determining the blur of the target image based on a Laplacian transform strategy; and determining the initial raindrop coverage corresponding to the target image based on a preset coverage calculation rule, according to the contrast, blur, proportion of edge points, and preset weighting coefficients of the target image.

[0041] The preset edge detection algorithm can be, for example, the Canny edge detection algorithm. The preset weighting coefficients can include a first weighting coefficient corresponding to contrast, a second weighting coefficient corresponding to blurriness, and a third weighting coefficient corresponding to the proportion of edge points.

[0042] It's important to note that contrast, blurriness, and edge point ratio are three features that reflect the impact of raindrops on an image from different perspectives. Therefore, by weighting these features, a more accurate estimate of raindrop coverage can be obtained. Specifically, raindrops on the windshield cause local brightness variations, thus affecting image contrast. Contrast reflects the difference between bright and dark areas in an image, and raindrops increase the local contrast. Raindrops form blurry spots on the windshield, increasing blurriness in local areas of the image. Blurriness can be measured using the Laplacian transform of the image, so the variance of the Laplacian operator reflects the image's sharpness. The edges formed by raindrops in the image increase the number of edge points, and the edge point ratio can be calculated using the Canny edge detection algorithm. Therefore, the density of edge points reflects the edge characteristics of the raindrop-covered area.

[0043] Optionally, the contrast of the target image can be determined based on the maximum and minimum values ​​of the corresponding pixels in the target image. Specifically, the difference between the maximum and minimum values ​​can be used as the contrast of the target image.

[0044] Optionally, the Canny edge detection algorithm can be used. Each pixel in the target image is substituted into the Canny function to determine whether the pixel is an edge point, so as to count the number of edge points in the target image. Finally, the ratio of the number of edge points to the total number of pixels in the target image is determined as the edge point ratio.

[0045] For example, the Canny edge detection algorithm can be used to determine whether each pixel in a target image is an edge point based on the following formula:

[0046]

[0047] Here, E(x, y) is the evaluation value for whether each pixel belongs to an edge point. If the calculated E(x, y) is 1, it indicates that the pixel belongs to an edge point; otherwise, it is determined that the pixel does not belong to an edge point. Canny(I(x, y)) is the target value obtained by substituting the pixel in the target image into the Canny function, and threshold is the edge point threshold. Specifically, if the target value is greater than the edge point threshold, it can be determined that the pixel belongs to an edge point; otherwise, it is determined that the pixel does not belong to an edge point.

[0048] For example, the edge point ratio can be determined based on the following formula:

[0049]

[0050] Where Pedge refers to the proportion of edge points, N refers to the total number of pixels in the target image, and ∑E(x,y) refers to the number of edge points in the target image.

[0051] Optionally, based on the Laplacian transform strategy, the target image and the Laplacian operator kernel can be multiplied according to a preset Laplacian operator kernel to obtain a Laplacian image. The variance of the Laplacian image can then be determined as the ambiguity corresponding to the target image.

[0052] For example, based on the Laplacian image, the blur level of the target image can be determined using the following formula:

[0053]

[0054] Where Var(L) refers to the blur level of the target image, M and N refer to the number of rows and columns of the pixel matrix of the Laplacian image, respectively, and μ refers to the pixel mean of the Laplacian image. L(x, y) refers to the values ​​of each item in the pixel matrix of the Laplacian image.

[0055] For example, based on the contrast, blur, edge point ratio, and preset weighting coefficients of the target image, the initial raindrop coverage of the target image can be determined using the following formula:

[0056] W rain =w1·C global +ω2·Var(L)+w3·P edge ;

[0057] Where w1, w2, and w3 are the first weighting coefficient for contrast, the second weighting coefficient for blur, and the third weighting coefficient for edge point ratio, respectively, satisfying w1 + w2 + w3 = 1. Wrain refers to the initial raindrop coverage, Cglobal refers to contrast, Var(L) refers to blur, and Pedge refers to edge point ratio.

[0058] Optionally, based on the initial raindrop coverage, the target image is iteratively divided using an iterative binary method until a divided region satisfies the termination recursion condition. This includes: determining whether the initial raindrop coverage satisfies the termination recursion condition based on the relationship between the initial raindrop coverage and a preset coverage threshold; if not, the target image is iteratively divided using an iterative binary method until a divided region satisfies the termination recursion condition.

[0059] Optionally, if the initial raindrop coverage is greater than a preset coverage threshold, the recursion termination condition is met, and the recursion can be terminated, with the current recursion depth recorded as 1. If the initial raindrop coverage is less than or equal to the preset coverage threshold, the target image is iteratively divided using an iterative binary search method, and it is determined whether the raindrop coverage of each divided region is greater than the coverage threshold.

[0060] Optionally, the preset coverage threshold and maximum recursion depth can be adjusted according to the actual situation to adapt to different rainfall conditions. For example, considering that iterative recursion will consume a lot of computing resources, the maximum recursion depth can be set according to the large, medium and small levels of the vehicle's computing resources, such as 34, 14 and 5 respectively.

[0061] It should be noted that the preset coverage threshold indicates how sensitive the vehicle's windshield wipers are to rainfall. A smaller preset coverage threshold means that the wipers will be activated when the rainfall is light, while a larger threshold means that the wipers will only be activated when the rainfall is heavy. Specifically, the preset coverage threshold can be automatically adjusted based on the user's input requirements.

[0062] Optionally, based on the iterative binary division method, the target image is iteratively divided until there are regions that satisfy the termination recursion condition. This includes: dividing the target image into a preset number of regions based on the iterative binary division method; determining the region raindrop coverage rate corresponding to each region; and determining whether there are regions whose region raindrop coverage rate satisfies the termination recursion condition based on the region raindrop coverage rate and a preset coverage threshold. If so, the iterative division of the target image is stopped.

[0063] The preset number can be, for example, two or four. For instance, the target image can be recursively divided into two or four parts (e.g., upper left, upper right, lower left, lower right), and the raindrop coverage calculation and judgment process can be repeated for each region until a preset coverage threshold or maximum recursion depth is reached.

[0064] For example, the image can be divided into four regions each time, and the regional raindrop coverage rate of each region can be calculated based on the above initial raindrop coverage rate calculation formula. If the regional raindrop coverage rate of any part exceeds the preset coverage rate threshold, the current recursion depth is recorded as 2, and the target recursion depth is obtained. Otherwise, the four regions are divided into four sub-regions, and it is determined whether the regional raindrop coverage rate of the 16 sub-regions exceeds the preset coverage rate threshold. If so, the current recursion depth is determined as 3, and the target recursion depth is obtained. If not, the division is carried out until the preset coverage rate threshold or the maximum recursion depth is reached.

[0065] It should be noted that if the current rainfall is very heavy, the calculated raindrop coverage will be very large, exceeding the threshold in the initial calculation, and the recursion depth will be 1. Conversely, in extreme cases, if the weather is sunny and there is no rain, the iteration will continue until the maximum recursion depth is reached, at which point the recursion depth value will be very large.

[0066] S103. Determine the estimated rainfall based on the target recursion depth at the end of the recursion, and control the vehicle's windshield wiper frequency based on the determined estimated rainfall.

[0067] The target recursion depth refers to the recursion depth at which the recursion ends. The magnitude of rainfall can be reflected by the size of the recursion depth value. A larger recursion depth indicates smaller rainfall, and a smaller recursion depth indicates larger rainfall.

[0068] Optionally, the rainfall estimate is determined based on the target recursion depth at the end of the recursion, including: determining the target recursion depth at the end of the recursion; and determining the rainfall estimate based on the target recursion depth, the maximum recursion depth, and the maximum rainfall, according to a preset rainfall estimation rule.

[0069] For example, based on the target recursion depth, the maximum recursion depth, and the maximum rainfall, the estimated rainfall can be determined using the following formula:

[0070]

[0071] Where R is the estimated rainfall, Rmax is the maximum rainfall, D is the target recursion depth, and Dmax is the maximum recursion depth. When D equals 0, the formula is R = Rmax, indicating very heavy rain; when D equals Dmax, the formula is R = 0, indicating no rain. The greater the target recursion depth, the smaller the estimated rainfall R. The maximum rainfall can be calibrated based on the rainfall sensor or actual conditions.

[0072] For example, assuming the maximum recursion depth is 10, the maximum rainfall is 100 mm / h, and the target recursion depth is 4, the estimated rainfall can be determined as R = 100(1-4 / 10) = 60 mm / h.

[0073] Optionally, after determining the estimated rainfall value, the corresponding wiper frequency can be determined based on the rainfall level to which the estimated rainfall value belongs and the preset correspondence between rainfall level and wiper frequency to control the vehicle's wipers. Specifically, a smaller target recursion depth indicates a larger rainfall and requires a higher wiper frequency; a larger target recursion depth indicates a smaller rainfall and requires a lower wiper frequency. If the estimated rainfall value is 0, the corresponding wiper frequency can be directly determined to be 0.

[0074] It should be noted that in reality, when it starts to rain or when the rain is light, the raindrops cannot cover the entire windshield. This invention uses an iterative binary method to continuously segment the target image, which can quickly locate the area that may contain rain. In other words, the windshield area with a final raindrop coverage rate greater than a preset coverage rate threshold is actually the area with the most rainwater on the windshield.

[0075] The technical solution of this invention involves the following steps: During vehicle operation, if a wiper control command is detected, an image is captured via an onboard camera to obtain a target image containing the vehicle's windshield. An initial raindrop coverage rate corresponding to the target image is determined, and based on this initial raindrop coverage rate, the target image is iteratively divided using an iterative binary search method until a divided region satisfies the termination condition. A rainfall estimate is determined based on the target recursion depth at the termination of recursion, and the vehicle's wiper frequency is controlled according to this rainfall estimate. By analyzing the raindrop situation on the windshield in real time and dynamically adjusting the wiper frequency, the intelligence level of wiper control can be improved, enhancing driving safety and comfort.

[0076] Example 2

[0077] Figure 2 This is a flowchart of a vehicle windshield wiper control method provided in Embodiment 2 of the present invention; based on the above embodiments, this embodiment provides a preferred example of using an iterative bisection method to estimate rainfall and thus control the vehicle windshield wipers, specifically, as follows: Figure 2 As shown, the method includes:

[0078] S201. During vehicle operation, if a wiper control command is detected, the vehicle's windshield is captured by the onboard camera to obtain candidate images.

[0079] S202. Perform grayscale processing on the candidate image to obtain a grayscale image, and then use a Gaussian filter to filter the grayscale image to obtain a filtered image.

[0080] S203. Perform image segmentation processing on the filtered image to remove the background area except for the windshield area, and obtain the target image.

[0081] S204. Based on the preset edge detection algorithm, determine the proportion of edge points corresponding to the target image, and based on the Laplacian transform strategy, determine the blur of the target image.

[0082] S205. Based on the contrast, blur, edge point ratio and preset weighting coefficient of the target image, determine the initial raindrop coverage of the target image according to the preset coverage calculation rules.

[0083] S206. Based on the relationship between the initial raindrop coverage rate and the preset coverage threshold, determine whether the initial raindrop coverage rate meets the recursion termination condition.

[0084] S207. If not, then based on the iterative binary division method, the target image is iteratively divided until there are regions of the image that satisfy the termination recursion condition.

[0085] S208. Determine the target recursion depth when the recursion ends, and based on the target recursion depth, the maximum recursion depth, and the maximum rainfall, determine the rainfall estimate based on the preset rainfall estimation rules.

[0086] S209. Control the frequency of the vehicle's windshield wipers based on the determined rainfall estimate.

[0087] The technical solution of the present invention has the following technical effects: (1) Improve the accuracy and intelligence of wiper frequency control: Most existing wiper control methods are based on fixed time intervals or simple rain sensors, which cannot reflect changes in rainfall in real time and accurately. The present invention achieves dynamic adjustment of wiper frequency by comprehensively analyzing raindrop coverage, thereby improving the accuracy and intelligence of wiper control. (2) Enhance driving safety and visibility: In rainy weather, raindrops on the windshield will significantly affect the driver's visibility. Through the method of the present invention, the wiper frequency can be adjusted in a timely and accurate manner to ensure the driver's visibility is clear, thereby enhancing driving safety. (3) Real-time performance and multi-feature fusion: The present invention collects images in real time through an onboard high-definition camera and calculates raindrop coverage by combining multiple features such as contrast, blur, and edge point ratio, providing a real-time rainfall detection method that comprehensively considers multiple influencing factors, avoiding the limitations of traditional single-feature methods.

[0088] In summary, the technical solution of this invention can achieve high precision: by comprehensively considering multiple features, the calculation of raindrop coverage is more accurate; intelligent control: the wiper frequency is dynamically adjusted according to the amount of rainfall, improving driving safety and comfort; real-time performance: by using an onboard camera to collect images in real time, the algorithm has high real-time performance.

[0089] Example 3

[0090] Figure 3This is a structural block diagram of a vehicle wiper control device provided in Embodiment 3 of the present invention. This embodiment is applicable to situations where the vehicle estimates the amount of rain during driving and adjusts the wiper frequency accordingly. The vehicle wiper control device provided in this embodiment can execute the vehicle wiper control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. This vehicle wiper control device can be implemented in hardware and / or software and configured in an electronic device with vehicle wiper control function, such as in a vehicle, and executed by the vehicle's electronic control unit, such as... Figure 3 As shown, the vehicle wiper control device specifically includes:

[0091] The acquisition module 301 is used to acquire images through the vehicle camera during vehicle operation if a wiper control command is detected, so as to obtain a target image including the vehicle's windshield.

[0092] The iteration module 302 is used to determine the initial raindrop coverage corresponding to the target image, and based on the initial raindrop coverage, iteratively divide the target image according to the iterative binary division method until there are divided regions that satisfy the termination recursion condition.

[0093] The control module 303 is used to determine the rainfall estimate based on the target recursion depth at the end of the recursion, and to control the vehicle's windshield wiper frequency based on the determined rainfall estimate.

[0094] The technical solution of this invention involves the following steps: During vehicle operation, if a wiper control command is detected, an image is captured via an onboard camera to obtain a target image containing the vehicle's windshield. An initial raindrop coverage rate corresponding to the target image is determined, and based on this initial raindrop coverage rate, the target image is iteratively divided using an iterative binary search method until a divided region satisfies the termination condition. A rainfall estimate is determined based on the target recursion depth at the termination of recursion, and the vehicle's wiper frequency is controlled according to this rainfall estimate. By analyzing the raindrop situation on the windshield in real time and dynamically adjusting the wiper frequency, the intelligence level of wiper control can be improved, enhancing driving safety and comfort.

[0095] Furthermore, the iteration module 302 may include:

[0096] The judgment unit is used to determine whether the initial raindrop coverage rate meets the recursion termination condition based on the relationship between the initial raindrop coverage rate and the preset coverage rate threshold.

[0097] The iterative unit is used to iteratively divide the target image based on the iterative binary division method if no, until there are regions of the image that satisfy the termination recursion condition.

[0098] Furthermore, the iteration module 302 is specifically used for:

[0099] Based on a preset edge detection algorithm, the proportion of edge points corresponding to the target image is determined, and based on the Laplacian transform strategy, the blur level of the target image is determined.

[0100] Based on the contrast, blur, edge point ratio, and preset weighting coefficients of the target image, the initial raindrop coverage of the target image is determined according to the preset coverage calculation rules.

[0101] Furthermore, the iterative unit is specifically used for:

[0102] Based on the iterative binary division method, the target image is divided into a preset number of region images;

[0103] Determine the raindrop coverage rate of each region image, and based on the region raindrop coverage rate and the preset coverage threshold, determine whether there are any regions whose region raindrop coverage rate meets the termination recursion condition. If so, stop the iterative division of the target image.

[0104] Furthermore, the acquisition module 301 is specifically used for:

[0105] Candidate images are obtained by capturing images of the vehicle's windshield using an in-vehicle camera;

[0106] The candidate image is processed to obtain a grayscale image, and then a Gaussian filter is applied to filter the grayscale image to obtain a filtered image.

[0107] The filtered image is segmented to remove the background area except for the windshield area, thus obtaining the target image.

[0108] Furthermore, the control module 303 is specifically used for:

[0109] Determine the target recursion depth when the recursion ends;

[0110] Based on the target recursion depth, maximum recursion depth, and maximum rainfall, the estimated rainfall value is determined according to the preset rainfall estimation rules.

[0111] Example 4

[0112] Figure 4 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Figure 4A 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.

[0113] like Figure 4 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.

[0114] 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.

[0115] 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 vehicle windshield wiper control methods.

[0116] In some embodiments, the vehicle wiper control 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 into 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 vehicle wiper control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle wiper control method by any other suitable means (e.g., by means of firmware).

[0117] 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), system-on-a-chip (SoCs), complex 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.

[0118] 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.

[0119] 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, semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. 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 of the foregoing.

[0120] 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).

[0121] 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.

[0122] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact via 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.

[0123] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the vehicle wiper control method of any embodiment of the present invention.

[0124] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[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 vehicle wiper control method characterized by, The method comprises the following steps: During the operation of the vehicle, if a wiper control instruction is detected, an image is collected by a vehicle-mounted camera to obtain a target image containing a front windshield of the vehicle; An initial raindrop coverage corresponding to the target image is determined, and the target image is iteratively divided based on an iterative bisection method according to the initial raindrop coverage until there exists a divided region image that meets an end recursion condition; A rainfall estimation value is determined according to a target recursion depth at the end of recursion, and a wiper frequency of the vehicle is controlled according to the determined rainfall estimation value; The target image is iteratively divided based on the iterative bisection method according to the initial raindrop coverage until there exists a divided region image that meets the end recursion condition, which comprises: determining whether the initial raindrop coverage meets the end recursion condition according to a size relationship between the initial raindrop coverage and a preset coverage threshold; if not, the target image is iteratively divided based on the iterative bisection method until there exists a divided region image that meets the end recursion condition; The target image is iteratively divided based on the iterative bisection method until there exists a divided region image that meets the end recursion condition, which comprises: dividing the target image into a preset number of region images based on the iterative bisection method; determining a region raindrop coverage corresponding to each region image, and determining whether there exists a region raindrop coverage of a region image that meets the end recursion condition according to the region raindrop coverage and the preset coverage threshold, if so, stopping the iterative division of the target image.

2. The method of claim 1, wherein, The initial raindrop coverage corresponding to the target image is determined, which comprises: An edge point proportion corresponding to the target image is determined based on a preset edge detection algorithm, and a blur degree corresponding to the target image is determined based on a Laplace transform strategy; The initial raindrop coverage corresponding to the target image is determined based on a preset coverage calculation rule according to a contrast of the target image, the blur degree, the edge point proportion and a preset weighting coefficient.

3. The method of claim 1, wherein, The image is collected by the vehicle-mounted camera to obtain the target image containing the front windshield of the vehicle, which comprises: The front windshield of the vehicle is imaged by the vehicle-mounted camera to obtain a candidate image; The candidate image is subjected to grayscale processing to obtain a grayscale image, and a Gaussian filter is used to filter the grayscale image to obtain a filtered image; The filtered image is subjected to image segmentation processing to remove a background region other than the front windshield region of the filtered image to obtain the target image.

4. The method of claim 1, wherein, The rainfall estimation value is determined according to the target recursion depth at the end of recursion, which comprises: The target recursion depth at the end of recursion is determined; The rainfall estimation value is determined based on a preset rainfall estimation rule according to the target recursion depth, a maximum recursion depth and a maximum rainfall.

5. A vehicle wiper control device characterized by comprising: The method comprises the following steps: The image collection module is configured to collect an image by the vehicle-mounted camera to obtain a target image containing a front windshield of the vehicle if a wiper control instruction is detected during the operation of the vehicle; The iteration module is configured to determine an initial raindrop coverage corresponding to the target image, and iteratively divide the target image based on an iterative bisection method according to the initial raindrop coverage until there exists a divided region image that meets an end recursion condition; The control module is configured to determine a rain estimation value according to a target recursion depth when recursion ends, and control a wiper frequency of the vehicle according to the determined rain estimation value. The iteration module comprises: a judging unit configured to determine whether the initial raindrop coverage rate meets an end recursion condition according to a size relationship between the initial raindrop coverage rate and a preset coverage rate threshold; and an iteration unit configured to, if not, perform iterative division on the target image based on an iterative bisection method until there is a divided region image that meets the end recursion condition. The iteration unit is specifically configured to: divide the target image into a preset number of region images based on the iterative bisection method; determine region raindrop coverage rates corresponding to the region images, and determine whether there is a region raindrop coverage rate of a region image that meets the end recursion condition according to the region raindrop coverage rates and the preset coverage rate threshold; and if yes, stop the iterative division on the target image.

6. An electronic device, comprising: The electronic device comprises at least one processor and a memory connected with the at least one processor in communication; the memory stores a computer program executed by the at least one processor; the computer program is executed by the at least one processor to enable the at least one processor to execute the vehicle wiper control method in any one of claims 1-4.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the vehicle wiper control method in any one of claims 1-4 when executed.

8. A computer program product, characterised in that, The computer program product comprises a computer program that, when executed by the processor, implements the vehicle wiper control method according to any one of claims 1-4.

Images