CN116101224A - Vehicle windshield wiper adjusting method, device, equipment and storage medium - Google Patents
Vehicle windshield wiper adjusting method, device, equipment and storage medium Download PDFInfo
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- CN116101224A CN116101224A CN202310228697.7A CN202310228697A CN116101224A CN 116101224 A CN116101224 A CN 116101224A CN 202310228697 A CN202310228697 A CN 202310228697A CN 116101224 A CN116101224 A CN 116101224A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60S—SERVICING, CLEANING, REPAIRING, SUPPORTING, LIFTING, OR MANOEUVRING OF VEHICLES, NOT OTHERWISE PROVIDED FOR
- B60S1/00—Cleaning of vehicles
- B60S1/02—Cleaning windscreens, windows or optical devices
- B60S1/04—Wipers or the like, e.g. scrapers
- B60S1/06—Wipers or the like, e.g. scrapers characterised by the drive
- B60S1/08—Wipers or the like, e.g. scrapers characterised by the drive electrically driven
- B60S1/0818—Wipers or the like, e.g. scrapers characterised by the drive electrically driven including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like
- B60S1/0822—Wipers or the like, e.g. scrapers characterised by the drive electrically driven including control systems responsive to external conditions, e.g. by detection of moisture, dirt or the like characterized by the arrangement or type of detection means
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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Abstract
The application provides a vehicle windshield wiper adjusting method, device, equipment and storage medium, wherein the method comprises the following steps: receiving driving images of at least two continuous frames sent by driving image pick-up equipment; extracting sampling images from sampling points of a plurality of preset positions of each driving image aiming at driving images of any two continuous frames to obtain a plurality of pairs of sampling images; detecting whether the sampling points have preset weather properties or not based on a pair of sampling images of each sampling point; according to the number of sampling points with preset weather properties and the total number of sampling points, calculating a windshield wiper stroke value of the period; and adjusting the wiping frequency of the windshield wiper according to the wiping value of the windshield wiper in the period. According to the intelligent windshield wiper adjusting device, intelligent adjustment of the windshield wiper can be achieved under the condition that hardware is not increased, and vehicle production cost is reduced.
Description
Technical Field
The application relates to the technical field of vehicle intelligence, in particular to a vehicle windshield wiper adjusting method, device, equipment and storage medium.
Background
With the increasing degree of intellectualization of automobiles, automobiles are gradually developed from common transportation means to one of important places in people's daily lives. Particularly in severe weather conditions, automobiles are an important choice for people to travel. At present, intelligent control of a windshield wiper of a vehicle automatically adjusts the stroke frequency of the windshield wiper according to the rainfall (or the snow amount).
In the related art, a sensor for detecting a separate amount of rain (or snow) is installed in a vehicle, and the amount of rain (or snow) is automatically detected by the sensor. However, this approach requires the addition of new sensors, which can create additional hardware costs.
Disclosure of Invention
The application provides a vehicle windshield wiper adjusting method, device, equipment and storage medium, which are used for solving the problem that the existing windshield wiper intelligent control needs to be added with a new sensor and extra hardware cost is generated.
In a first aspect, the present application provides a vehicle wiper adjustment method, including:
receiving driving images of at least two continuous frames sent by driving image pick-up equipment;
extracting sampling images from sampling points of a plurality of preset positions of each driving image aiming at driving images of any two continuous frames to obtain a plurality of pairs of sampling images;
detecting whether the sampling points have preset weather properties or not based on a pair of sampling images of each sampling point;
according to the number of sampling points with preset weather properties and the total number of sampling points, calculating a windshield wiper stroke value of the period;
and adjusting the wiping frequency of the windshield wiper according to the wiping value of the windshield wiper in the period.
In one possible design, the detecting whether the sampling point has a preset weather attribute based on the pair of sampling images of each sampling point includes: for a pair of sampling images of each sampling point, respectively determining an optical flow field of the sampling image of the current frame and a flow optical flow field of the sampling image of the previous frame; and comparing the optical flow field of the sampling image of the current frame with the optical flow field of the sampling image of the previous frame to judge whether the sampling point has preset weather attribute.
In one possible design, the comparing the optical flow field of the sampled image of the current frame with the optical flow field of the sampled image of the previous frame to determine whether the sampling point has a preset weather attribute includes: if the difference value between the average speed of the optical flow field of the sampling image of the current frame and the average speed of the optical flow field of the sampling image of the previous frame is smaller than or equal to a preset threshold value, determining that the sampling point has a preset weather attribute; otherwise, determining that the sampling point does not have the preset weather attribute.
In one possible design, before comparing the optical flow field of the sampled image of the current frame with the optical flow field of the sampled image of the previous frame to determine whether the sampling point has a preset weather attribute, the method further includes: calculating the difference value of the gray average pixel value of the sampling image of the current frame and the sampling image of the previous frame; calculating the difference value of the brightness average pixel value of the sampling image of the current frame and the sampling image of the previous frame; according to the difference value of the gray average pixel value and the difference value of the brightness average pixel value, calculating to obtain a comprehensive pixel difference value of the sampling image of the current frame and the sampling image of the previous frame; correspondingly, comparing the optical flow field of the sampling image of the current frame with the optical flow field of the sampling image of the previous frame to judge whether the sampling point has a preset weather attribute, including: if the difference value between the average speed of the optical flow field of the sampling image of the current frame and the average speed of the optical flow field of the sampling image of the previous frame is smaller than or equal to a preset threshold value, and the integrated pixel difference value falls into a preset pixel range, determining that the sampling point has a preset weather attribute; otherwise, determining that the sampling point does not have the preset weather attribute.
In one possible design, the calculating the difference between the gray level average pixel value of the sampled image of the current frame and the sampled image of the previous frame includes: converting the sampled image of the current frame and the sampled image of the previous frame into gray images, calculating a first gray average pixel value of the converted sampled image of the current frame and a second gray average pixel value of the converted sampled image of the previous frame, and calculating a difference value of the gray average pixel values of the sampled image of the current frame and the sampled image of the previous frame according to the first gray average pixel value and the second gray average pixel value; the calculating the difference value between the brightness average pixel value of the sampling image of the current frame and the brightness average pixel value of the sampling image of the previous frame comprises: converting the sampled image of the current frame and the sampled image of the previous frame into a preset color space, calculating a first brightness average pixel value of the converted sampled image of the current frame and a second brightness average pixel value of the converted sampled image of the previous frame, and calculating a difference value of the brightness average pixel values of the sampled image of the current frame and the sampled image of the previous frame according to the first brightness average pixel value and the second brightness average pixel value.
In one possible design, the calculating the wiper stroke value of the period according to the number of sampling points with the preset weather attribute and the total number of sampling points includes: and taking the ratio of the number of the sampling points with the preset weather attribute to the total number of the sampling points as a windshield wiper stroke value of the period.
In one possible design, the calculating the wiper stroke value of the period according to the number of sampling points with the preset weather attribute and the total number of sampling points includes: and calculating the windshield wiper stroke value of the period according to the number of sampling points with preset weather attributes, the total number of the sampling points and the windshield wiper stroke value calculated in the previous period.
In one possible design, the calculating the wiper stroke value of the present period according to the number of sampling points with the preset weather attribute, the total number of sampling points and the wiper stroke value calculated in the previous period includes: multiplying the ratio of the number of sampling points with preset weather properties to the total number of sampling points by a first preset weight coefficient to obtain a first part, and multiplying the wiper stroke value calculated in the last detection period by a second preset weight coefficient to obtain a second part; and summing the first part and the second part to obtain the windshield wiper stroke value in the period.
In one possible design, the adjusting the stroke frequency of the wiper according to the stroke value of the wiper in the present period includes: and comparing the windshield wiper stroke value in the period with a preset stroke frequency threshold value, and adjusting the stroke frequency of the windshield wiper according to a comparison result.
In one possible design, the preset swipe frequency threshold includes a first preset limit and a second preset limit, wherein the first preset limit is greater than the second preset limit; correspondingly, comparing the wiper stroke value of the present period with a preset stroke frequency threshold, and adjusting the stroke frequency of the wiper according to the comparison result, including: if the windshield wiper stroke value in the period is greater than or equal to the first preset limit value, starting the windshield wiper and gradually increasing the stroke frequency of the windshield wiper; if the wiper stroke value in the period is smaller than or equal to the second preset limit value, starting the wiper and gradually reducing the stroke frequency of the wiper; and if the wiper stroke value in the period is smaller than the first preset limit value and larger than the second preset limit value, starting the wiper and maintaining the stroke frequency of the wiper at a preset fixed value.
In one possible design, the plurality of preset positions are selected from a region of the driving image that does not include a moving interfering object.
In a second aspect, the present application provides a vehicle wiper adjustment device comprising:
the receiving module is used for receiving the driving images of at least two continuous frames sent by the driving camera equipment;
the sampling module is used for extracting sampling images from sampling points at a plurality of preset positions of each driving image aiming at driving images of any two continuous frames to obtain a plurality of pairs of sampling images;
the detection module is used for detecting whether the sampling points have preset weather properties or not based on a pair of sampling images of each sampling point;
the calculation module is used for calculating the windshield wiper stroke value of the period according to the number of sampling points with preset weather properties and the total number of the sampling points;
and the adjusting module is used for adjusting the wiping frequency of the windshield wiper according to the wiping value of the windshield wiper in the period.
In a third aspect, the present application provides an electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored in the memory, such that the at least one processor performs the vehicle wiper adjustment method as described above in the first aspect and various possible designs of the first aspect.
In a fourth aspect, the present application provides a computer storage medium having stored therein computer-executable instructions which, when executed by a processor, implement the vehicle wiper adjustment method according to the first aspect and the various possible designs of the first aspect.
In a fifth aspect, embodiments of the present disclosure provide a computer program product comprising a computer program which, when executed by a processor, implements the vehicle wiper adjustment method according to the first aspect and the various possible designs of the first aspect.
In the method, the device, the equipment and the storage medium for adjusting the windshield wiper of the vehicle, firstly, the original driving record shooting equipment on the vehicle is received, and driving images of at least two continuous frames are acquired; then, extracting a plurality of pairs of sampling images of sampling points at a plurality of preset positions from driving images of any two continuous frames, judging whether the sampling points have preset weather properties or not based on a pair of sampling images of each sampling point, and calculating to obtain a current windshield wiper stroke value according to the number of the sampling points with the preset weather properties and the total number of the sampling points; finally, according to the current windshield wiper stroke value, the stroke frequency of the windshield wiper is adjusted, so that intelligent adjustment of the windshield wiper can be realized under the condition that hardware is not increased, and the vehicle production cost is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the prior art descriptions, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a schematic view of an application scenario of a vehicle wiper adjustment method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for adjusting a vehicle wiper according to an embodiment of the present application;
fig. 3 is a schematic diagram of a preset rectangular frame according to an embodiment of the present application;
fig. 4 is a schematic structural view of a vehicle wiper adjusting device according to an embodiment of the present disclosure;
fig. 5 is a schematic hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The intellectualization of vehicles such as automobiles is becoming more and more important, and especially the intelligent control of vehicles is becoming an important direction for improving the driving experience of users. At present, the intelligent regulation control of the vehicle windshield wiper mainly comprises the steps of adding a sensor arranged or installed on the vehicle to detect the current rainfall and/or snow amount, so as to realize the intelligent regulation of the sliding frequency of the vehicle windshield wiper. However, the addition of the sensor leads to a significant increase in hardware cost of the vehicle.
In order to solve the above technical problems, the embodiments of the present application provide the following technical solutions: acquiring a driving image in front of a vehicle by using original driving recording and shooting equipment on the vehicle; and carrying out image sampling processing based on the driving image, judging whether the sampling points have preset weather attributes (rain falling attributes and/or snow falling attributes) according to an image recognition technology, and according to the number of the sampling points with the preset weather attributes and the total number of the sampling points, estimating and calculating to obtain a current windshield wiper stroke value, so as to adjust the stroke frequency of the windshield wiper, and realizing the technical effect of intelligently adjusting the windshield wiper under the condition of not adding hardware.
Fig. 1 is a schematic application scenario diagram of a vehicle wiper adjustment method according to an embodiment of the present application. As shown in fig. 1, a travelling imaging apparatus 101, a controller 102, and a vehicle wiper 103.
Here, the traffic image capturing apparatus 101 may be a camera mounted on a windshield of a vehicle for recording a traffic image. Note that the travelling camera apparatus 101 may be composed of one or a plurality of cameras. Alternatively, the live camera apparatus 101 may also be integrated on other devices, for example the live camera apparatus 101 may be integrated with an ETC (Electronic Toll Collection ) apparatus. In this example, the running camera apparatus 101 may be: at least one of a forward-running imaging apparatus, a backward-running imaging apparatus, and a forward-and-backward-running imaging apparatus.
The controller 102 may be an electronic device such as an onboard ECU (Electronic Control Unit ) or other onboard controller, which is not limited in this application.
The vehicle wiper 103 is an actuator on the vehicle, the composition of which includes, but is not limited to: a motion motor, a windshield wiper body, etc. The wiper body can be a bone wiper or a boneless wiper.
In the embodiment of the present application, the traffic image capturing apparatus 101 captures a traffic image in front of the vehicle. The controller 102 performs the adoption process based on the running image in front of the vehicle, and obtains the estimated wiper stroke value based on the sampling result. Meanwhile, the controller 102 outputs a wiper stroke value to the vehicle wiper 103 to adjust the stroke frequency of the vehicle wiper 103.
Fig. 2 is a schematic flow chart of a method for adjusting a vehicle wiper according to an embodiment of the present application. The execution body of the embodiment may be the controller in the embodiment shown in fig. 1, or may be other electronic devices such as a controller, a processor, or a server, which is not particularly limited herein. As shown in fig. 2, the method includes:
s201: and receiving driving images of at least two continuous frames sent by the driving image pickup equipment.
In this embodiment, the running images of at least two consecutive frames belong to one detection period. Wherein one detection period may be a duration of time, for example 10 seconds.
Specifically, a driving video picture in front of a vehicle is shot through driving imaging equipment, the driving video picture in a detection period is intercepted, and driving images of at least two continuous frames are extracted from the driving video picture according to a preset time interval.
For example, the preset time interval may be 1 second, and from a driving video frame lasting 10 seconds, driving images of 10 consecutive frames may be extracted.
S202: and extracting sampling images from sampling points at a plurality of preset positions of each driving image aiming at driving images of any two continuous frames to obtain a plurality of pairs of sampling images.
In this embodiment of the present application, the plurality of preset positions are selected from an area in the driving image that does not include the moving interferents. The specific preset position is selected with the following characteristics:
1) The preset position is a static part of the object in the driving image, so as to prevent the detection from being influenced by the moving object (a moving disturbance object such as a vehicle or a building which moves relatively) in the driving image.
2) The preset position corresponds to an area that the wiper can sweep. (here, if the wiper speed is too slow, the falling rain is detected seriously, and then the wiper speed is increased, so that the detected falling rain amount is reduced, and when the detected falling rain amount is reduced to a certain degree, the wiper speed is automatically reduced, so that the detection area is required to be swept by the wiper, and the detected falling rain amount and/or the falling snow amount can be influenced by different wiper speeds.
Optionally, the preset position is a portion of the driving image corresponding to the sky.
Specifically, the following processing is performed for each of the running images of any two consecutive frames: and placing the preset sampling frames at each preset position in the driving image, and extracting the local images in the sampling frames to serve as sampling images.
The preset sampling frame may be a rectangular sampling frame with a preset length and a preset width, or may be a circular or other shaped sampling frame.
Referring to fig. 3, fig. 3 is a schematic diagram of a preset rectangular frame according to an embodiment of the present application.
S203: based on a pair of sampled images for each sampling point, it is detected whether the sampling point has a preset weather attribute.
In one embodiment of the present application, the preset weather properties include a rain fall property and/or a snow fall property.
In one implementation manner, image information contained in two sampled images may be subjected to image analysis processing, and whether the sampling points have preset weather properties is determined according to an analysis result. The image information may be information such as a pixel value and/or an optical flow field.
In another implementation manner, two sampling images can be input into a pre-trained model to obtain a prediction result of whether the sampling images have preset weather properties or not, and further whether sampling points have preset weather properties or not is obtained. The pre-trained model can be a deep learning model, and the deep learning model can be obtained by performing model training on a sampling image obtained by processing a large number of driving images acquired by driving image pickup equipment.
S204: and calculating the windshield wiper stroke value of the period according to the number of sampling points with preset weather attributes and the total number of sampling points.
Specifically, the ratio of the number of sampling points having a preset weather property to the total number of sampling points is taken as the wiper stroke value of the present period. Wherein the ratio of the number of sampling points with preset weather properties to the total number of sampling points is used to characterize the amount (or intensity) of rain and/or snow falling.
Wherein, the calculation formula of the ratio is:
wherein R is a windshield wiper stroke value in the period; n is the number of sampling points with preset weather properties; n is the total number of sampling points.
S205: and according to the windshield wiper stroke value of the period, regulating the stroke frequency of the windshield wiper.
Specifically, comparing the wiper stroke value in the period with a preset stroke frequency threshold value, and adjusting the stroke frequency of the wiper according to the comparison result; the preset scratching frequency threshold comprises a first preset limit value (R1) and a second preset limit value (R2), and the first preset limit value is larger than the second preset limit value (R1 > R2).
The specific process for adjusting the stroke frequency of the windshield wiper comprises the following steps:
a: if the wiper stroke value in the period is greater than or equal to a first preset limit value (R is greater than or equal to R1), starting the wiper and gradually increasing the stroke frequency of the wiper.
The step of starting the windshield wiper and gradually increasing the wiping frequency of the windshield wiper may be to reduce the wiping frequency of the windshield wiper by a set value at intervals of a set time after starting the windshield wiper.
b: if the wiper stroke value in the period is smaller than or equal to a second preset limit value (R is smaller than or equal to R2), starting the wiper and gradually reducing the stroke frequency of the wiper.
The method includes starting the windshield wiper and gradually reducing the wiping frequency of the windshield wiper, wherein after the windshield wiper is started, the wiping frequency of the windshield wiper is increased by a set value at intervals of set time.
c: if the wiper stroke value in the period is smaller than the first preset limit value and larger than the second preset limit value (R1 > R > R2), starting the wiper and maintaining the stroke frequency of the wiper at a preset fixed value.
The preset fixed value may be a stroke frequency where the wiper is located at the moment.
As can be seen from the above description, first, a driving image of at least two consecutive frames acquired by an original driving recording and image capturing device on a vehicle is received; then, extracting a plurality of pairs of sampling images of sampling points at a plurality of preset positions from driving images of any two continuous frames, judging whether the sampling points have preset weather properties or not based on a pair of sampling images of each sampling point, and calculating to obtain a current windshield wiper stroke value according to the number of the sampling points with the preset weather properties and the total number of the sampling points; finally, according to the current windshield wiper stroke value, the stroke frequency of the windshield wiper is adjusted, so that intelligent adjustment of the windshield wiper can be realized under the condition that hardware is not increased, and the vehicle production cost is reduced.
In an embodiment of the present application, based on the embodiment provided in fig. 2, the embodiment describes in detail a specific process of detecting, in step S203, whether a sampling point has a preset weather attribute based on a pair of sampling images of each sampling point, and a specific implementation manner is described in detail as follows:
s301: for a pair of sample images of each sample point, the optical flow field of the sample image of the current frame and the optical flow field of the sample image of the previous frame are respectively determined.
The Optical Flow field (Optical Flow) is an instantaneous velocity field used for representing the change trend of the gray value of the pixel point in the image. In the real world, the motion of an object is typically characterized by a change in the gray scale distribution of individual pixels in a video stream.
In this embodiment, the specific manner of determining the optical flow field of the sampling image of the current frame is: and calculating the optical flow field of the sampling image of the current frame according to the sampling image of the current frame and the sampling image of the previous frame.
Similarly, the specific mode for determining the optical flow field of the sampling image of the previous frame is as follows: the optical flow field of the sampled image of the previous frame is calculated from the sampled image of the previous frame and the sampled image of the previous frame.
S302: and comparing the optical flow field of the sampling image of the current frame with the optical flow field of the sampling image of the previous frame to judge whether the sampling point has the preset weather attribute.
Specifically, a speed value of the optical flow field of the sampling image of the current frame in a preset direction is obtained through calculation according to the optical flow field of the sampling image of the current frame, and an average value is obtained according to the speed value of the preset direction to obtain an average speed of the optical flow field of the sampling image of the current frame.
In one example, the velocity Vx of the optical flow field of the sampled image of the current frame in the x-direction and the velocity Vy in the y-direction are calculated, vx 2 And Vy 2 And taking the square root as the average velocity of the optical flow field of the sampled image of the current frame.
Similarly, the velocity Vx of the optical flow field of the sampling image of the previous frame in the x direction and the velocity Vy in the y direction are calculated, and Vx is calculated 2 And Vy 2 And taking the square root as the average velocity of the optical flow field of the sampled image of the previous frame.
Specifically, the optical flow field average speed of the optical flow field of the sample image of the current frame is compared with the optical flow field average speed of the optical flow field of the sample image of the previous frame. If the difference value between the average speed of the optical flow field of the sampling image of the current frame and the average speed of the optical flow field of the sampling image of the previous frame is smaller than or equal to a preset threshold value, determining that the sampling point has a preset weather attribute; otherwise, determining that the sampling point does not have the preset weather attribute.
If the difference between the average velocity of the optical flow field of the sampling image of the current frame and the average velocity of the optical flow field of the obtained sampling image of the previous frame is greater than a preset threshold value, determining that the change of the optical flow field of the sampling point is caused by a preset object entering or leaving the sampling image. Wherein the preset object may be a cloud or a moving tree shadow or bridge hole in the sky. If the difference value between the average speed of the optical flow field of the sampling image of the current frame and the average speed of the optical flow field of the sampling image of the last frame is smaller than or equal to a preset threshold value, determining that the change of the optical flow field of the sampling point is caused by rain and/or snow falling.
It should be noted that, in the driving image, the portion (sampling point) corresponding to the sky may be changed due to cloud or moving tree shadow or entering bridge hole, and some changes are due to the motion of the automobile, which is a trend of overall change, and there is a definite overall speed in the optical flow field, where the optical flow field filters out the situations; the remaining change is due to rain.
From the above description, it can be seen that the optical flow field average speed of the optical flow field of the sampled image is used to determine the optical flow field variation of the sampled point, and based on the magnitude of the optical flow field variation of the sampled point, it is determined whether the optical flow field variation of the sampled point is caused by the preset weather attribute, so as to obtain the determination result of whether the sampled point has the preset weather attribute.
In one embodiment of the present application, based on the embodiment provided in fig. 2, the embodiment describes in detail the specific process of detecting, in step S203, whether a sampling point has a preset weather attribute based on a pair of sampling images of each sampling point, and another specific implementation manner is as follows:
s401: and calculating the difference value of the gray average pixel value of the sampling image of the current frame and the sampling image of the previous frame.
Specifically, a sampling image of a current frame and a sampling image of a previous frame are converted into gray images, a first gray average pixel value of the sampling image of the current frame after conversion and a second gray average pixel value of the sampling image of the previous frame after conversion are calculated, and a difference value between the sampling image of the current frame and the gray average pixel value of the sampling image of the previous frame is calculated according to the first gray average pixel value and the second gray average pixel value.
In this embodiment, according to the first gray average pixel value and the second gray average pixel value, a difference value between the gray average pixel value of the sampled image of the current frame and the gray average pixel value of the sampled image of the previous frame is calculated, which specifically includes:
and taking the absolute value of the difference value between the first gray average pixel value and the second gray average pixel value as the difference value between the gray average pixel value of the sampling image of the current frame and the gray average pixel value of the sampling image of the previous frame.
S402: and calculating the difference value of the brightness average pixel value of the sampling image of the current frame and the sampling image of the previous frame.
Specifically, converting a sampling image of a current frame and a sampling image of a previous frame into a preset color space, calculating a first brightness average pixel value of the sampling image of the current frame after conversion and a second brightness average pixel value of the sampling image of the previous frame after conversion, and calculating a difference value between the sampling image of the current frame and the brightness average pixel value of the sampling image of the previous frame according to the first brightness average pixel value and the second brightness average pixel value.
In this embodiment, the preset color space may be an HSL color space. In the HSL, an H (hue) channel represents a color range which can be perceived by human eyes; the S (saturation) channel, which refers to the saturation of a color, describes the change in color purity at the same hue, brightness, with a value of 0% to 100%; the L (light) channel refers to the lightness of a color, and functions to control the shade change of the color.
In this embodiment, the luminance average pixel value in the first luminance average pixel value and the second luminance average pixel value is the average pixel value of the L channel of the HSL color space.
Specifically, according to the first luminance average pixel value and the second luminance average pixel value, calculating to obtain a difference value between the luminance average pixel value of the sampling image of the current frame and the luminance average pixel value of the sampling image of the previous frame, including:
and taking the absolute value of the difference value between the first brightness average pixel value and the second brightness average pixel value as the difference value between the brightness average pixel value of the sampling image of the current frame and the brightness average pixel value of the sampling image of the previous frame.
S403: and calculating to obtain the comprehensive pixel difference value of the sampling image of the current frame and the sampling image of the previous frame according to the difference value of the gray average pixel value and the difference value of the brightness average pixel value.
In the present embodiment, the integrated pixel difference value is calculated by the following calculation formula:
PL_diff＝wpP_diff+w l L_diff
where pl_diff is the integrated pixel difference; p_diff is the difference of the gray average pixel values; l_diff is the luminance average pixel value; wp and w l Is a preset weight, wp+w l Where=1.
S404: if the difference value between the average speed of the optical flow field of the sampling image of the current frame and the average speed of the optical flow field of the sampling image of the previous frame is smaller than or equal to a preset threshold value, and the integrated pixel difference value falls into a preset pixel range, determining that the sampling point has a preset weather attribute; otherwise, determining that the sampling point does not have the preset weather attribute.
Wherein, the average speed of the optical flow field of the sampling image of the current frame is expressed as V; the average speed of an optical flow field of the sampling image of the previous frame is expressed as V_previous; the preset threshold, denoted V0.
In this embodiment, if the difference between V and v_previous is greater than the preset threshold V0, then it is considered that the change of the sampled image is caused by an object entering or leaving the sampled image (such as cloud in the sky or moving tree shadow), and no rain and/or snow is detected in the sampled point is output; whereas it is believed that image variations in the sampling points may result from rain and/or snow. Then the result of detecting rain and/or snow in the sampling point is output while considering that when pl_2< pl_diff < pl_1 (the integrated pixel difference is too large or too small and does not conform to the characteristics of rain and/or snow).
As can be seen from the above description, the present embodiment adds a judgment basis on the basis of whether the optical flow field variation of the sampled images of the adjacent frames based on the sampling points is the judgment basis caused by the preset weather attribute, that is, further according to whether the integrated pixel difference value of the sampled images of the sampling points falls within a reasonable range, the detection accuracy of detecting whether the sampling points have the preset weather attribute can be improved by adopting a plurality of judgment basis.
In an embodiment of the present application, based on the embodiment provided in fig. 2, the embodiment describes in detail that in step S204, according to the number of sampling points with preset weather properties and the total number of sampling points, the wiper stroke value of the present period is calculated, and another implementation manner is as follows:
and calculating the windshield wiper stroke value of the period according to the number of sampling points with the preset weather attribute, the total number of the sampling points and the windshield wiper stroke value calculated in the previous period.
Specifically, multiplying the ratio of the number of sampling points with preset weather properties to the total number of sampling points by a first preset weight coefficient to obtain a first part, and multiplying the wiper stroke value calculated in the last detection period by a second preset weight coefficient to obtain a second part; and summing the first part and the second part to obtain the windshield wiper stroke value of the period.
The specific calculation formula is as follows:
wherein R is a windshield wiper stroke value in the period; w (w) 1 N is the number of sampling points with preset weather properties for the first preset weight coefficient; n is the total number of sampling points; w (w) 2 The second preset weight coefficient is the second preset weight coefficient; r is R pre The wiper stroke value for the last detection period.
From the above description, the wiper stroke value in the previous detection period is smoothed by using the wiper stroke value calculated in the previous detection period, so that sudden and large-amplitude jumping of the wiper stroke value caused by false detection can be avoided, and the user experience is improved.
It should be noted that: to accommodate different ones of a rain scene, a snow scene, and a rain plus snow scene, embodiments of the present application may accommodate adjustment of one or more of the following parameters:
adjusting the size and the position of a preset sampling frame;
adjusting a preset threshold V0;
and adjusting the preset pixel ranges (pl_2, pl_1);
and adjusting the first preset limit to be greater than the second preset limit (R1 > R2).
Fig. 4 is a schematic structural view of a vehicle wiper adjusting device according to an embodiment of the present application. As shown in fig. 4, the vehicle wiper adjusting apparatus includes: a receiving module 401, a sampling module 402, a detecting module 403, a calculating module 404 and an adjusting module 405.
Wherein, the receiving module 401 is configured to receive driving images of at least two consecutive frames sent by the driving image capturing device;
the sampling module 402 is configured to extract, for any two consecutive driving images, sampling images from sampling points at a plurality of preset positions of each driving image, so as to obtain a plurality of pairs of sampling images;
a detection module 403, configured to detect whether a sampling point has a preset weather attribute based on a pair of sampling images of each sampling point;
a calculating module 404, configured to calculate a wiper stroke value of the present period according to the number of sampling points with the preset weather attribute and the total number of sampling points;
The adjusting module 405 is configured to adjust a wiping frequency of the wiper according to the wiping value of the present period.
In one possible design, the detection module 403 is specifically configured to: for a pair of sampling images of each sampling point, respectively determining an optical flow field of the sampling image of the current frame and a flow optical flow field of the sampling image of the previous frame; and comparing the optical flow field of the sampling image of the current frame with the optical flow field of the sampling image of the previous frame to judge whether the sampling point has the preset weather attribute.
In one possible design, the detection module 403 is specifically configured to: if the difference value between the average speed of the optical flow field of the sampling image of the current frame and the average speed of the optical flow field of the sampling image of the previous frame is smaller than or equal to a preset threshold value, determining that the sampling point has a preset weather attribute; otherwise, determining that the sampling point does not have the preset weather attribute.
In one possible design, the detection module 403 is further specifically configured to: calculating the difference value of the gray average pixel value of the sampling image of the current frame and the sampling image of the previous frame; calculating the difference value of the brightness average pixel value of the sampling image of the current frame and the sampling image of the previous frame; according to the difference value of the gray average pixel value and the difference value of the brightness average pixel value, calculating to obtain the comprehensive pixel difference value of the sampling image of the current frame and the sampling image of the previous frame; if the difference value between the average speed of the optical flow field of the sampling image of the current frame and the average speed of the optical flow field of the sampling image of the previous frame is smaller than or equal to a preset threshold value, and the integrated pixel difference value falls into a preset pixel range, determining that the sampling point has a preset weather attribute; otherwise, determining that the sampling point does not have the preset weather attribute.
In one possible design, the detection module 403 is further specifically configured to: converting the sampling image of the current frame and the sampling image of the previous frame into gray images, calculating a first gray average pixel value of the sampling image of the current frame after conversion and a second gray average pixel value of the sampling image of the previous frame after conversion, and calculating to obtain a difference value of the gray average pixel values of the sampling image of the current frame and the sampling image of the previous frame according to the first gray average pixel value and the second gray average pixel value; converting the sampling image of the current frame and the sampling image of the previous frame into a preset color space, calculating a first brightness average pixel value of the sampling image of the current frame after conversion and a second brightness average pixel value of the sampling image of the previous frame after conversion, and calculating a difference value of the brightness average pixel values of the sampling image of the current frame and the sampling image of the previous frame according to the first brightness average pixel value and the second brightness average pixel value.
In one possible design, the computing module 404 is specifically configured to: and taking the ratio of the number of sampling points with preset weather properties to the total number of sampling points as a windshield wiper stroke value of the period.
In one possible design, the computing module 404 is specifically configured to: and calculating the windshield wiper stroke value of the period according to the number of sampling points with the preset weather attribute, the total number of the sampling points and the windshield wiper stroke value calculated in the previous period.
In one possible design, the computing module 404 is specifically configured to: multiplying the ratio of the number of sampling points with preset weather properties to the total number of sampling points by a first preset weight coefficient to obtain a first part, and multiplying the wiper stroke value calculated in the last detection period by a second preset weight coefficient to obtain a second part; and summing the first part and the second part to obtain the windshield wiper stroke value of the period.
In one possible design, the adjustment module 405 is specifically configured to: comparing the wiper stroke value of the period with a preset stroke frequency threshold value, and adjusting the stroke frequency of the wiper according to the comparison result.
In one possible design, the preset swipe frequency threshold includes a first preset limit and a second preset limit, wherein the first preset limit is greater than the second preset limit; the adjusting module 405 is specifically configured to: if the wiper stroke value in the period is greater than or equal to a first preset limit value, starting the wiper and gradually increasing the stroke frequency of the wiper; if the wiper stroke value in the period is smaller than or equal to a second preset limit value, starting the wiper and gradually reducing the stroke frequency of the wiper; if the wiper stroke value in the period is smaller than the first preset limit value and larger than the second preset limit value, starting the wiper and maintaining the stroke frequency of the wiper at a preset fixed value.
In one possible design, the preset weather properties include rain fall properties and/or snow fall properties.
The device provided in this embodiment may be used to implement the technical solution of the foregoing method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
Fig. 5 is a schematic hardware structure of an electronic device according to an embodiment of the present application. As shown in fig. 5, the electronic device 50 of the present embodiment includes: a processor 501 and a memory 502; wherein the method comprises the steps of
A memory 502 for storing computer-executable instructions;
the processor 501 is configured to execute computer-executable instructions stored in the memory to implement the steps performed by the electronic device in the above-described embodiments. Reference may be made in particular to the relevant description of the embodiments of the method described above.
Alternatively, the memory 502 may be separate or integrated with the processor 501.
When the memory 502 is provided separately, the electronic device further comprises a bus 503 for connecting the memory 502 and the processor 501.
The embodiment of the application also provides a computer storage medium, wherein computer execution instructions are stored in the computer storage medium, and when a processor executes the computer execution instructions, the vehicle windshield wiper adjusting method is realized.
The embodiment of the application also provides a computer program product, which comprises a computer program, and when the computer program is executed by a processor, the method for adjusting the windshield wiper of the vehicle is realized. The embodiment of the application also provides a computer program product, which comprises a computer program, and when the computer program is executed by a processor, the method for adjusting the windshield wiper of the vehicle is realized.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, e.g., the division of modules is merely a logical function division, and there may be additional divisions of actual implementation, e.g., multiple modules may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to implement the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each module may exist alone physically, or two or more modules may be integrated in one unit. The units formed by the modules can be realized in a form of hardware or a form of hardware and software functional units.
The integrated modules, which are implemented in the form of software functional modules, may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or processor to perform some steps of the methods of the various embodiments of the present application.
It should be understood that the above processor may be a central processing unit (Central Processing Unit, abbreviated as CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, abbreviated as DSP), application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, and may also be a U-disk, a removable hard disk, a read-only memory, a magnetic disk or optical disk, etc.
The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or one type of bus.
The storage medium may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). It is also possible that the processor and the storage medium reside as discrete components in an electronic device or a master device.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (15)
1. A vehicle wiper adjustment method, characterized by comprising:
receiving driving images of at least two continuous frames sent by driving image pick-up equipment;
extracting sampling images from sampling points of a plurality of preset positions of each driving image aiming at driving images of any two continuous frames to obtain a plurality of pairs of sampling images;
detecting whether the sampling points have preset weather properties or not based on a pair of sampling images of each sampling point;
according to the number of sampling points with preset weather properties and the total number of sampling points, calculating a windshield wiper stroke value of the period;
and adjusting the wiping frequency of the windshield wiper according to the wiping value of the windshield wiper in the period.
2. The method of claim 1, wherein detecting whether the sampling point has a preset weather attribute based on the pair of sampled images for each sampling point comprises:
for a pair of sampling images of each sampling point, respectively determining an optical flow field of the sampling image of the current frame and a flow optical flow field of the sampling image of the previous frame;
and comparing the optical flow field of the sampling image of the current frame with the optical flow field of the sampling image of the previous frame to judge whether the sampling point has preset weather attribute.
3. The method of claim 2, wherein comparing the optical flow field of the sampled image of the current frame with the optical flow field of the sampled image of the previous frame to determine whether the sample point has a preset weather attribute comprises:
if the difference value between the average speed of the optical flow field of the sampling image of the current frame and the average speed of the optical flow field of the sampling image of the previous frame is smaller than or equal to a preset threshold value, determining that the sampling point has a preset weather attribute; otherwise, determining that the sampling point does not have the preset weather attribute.
4. The method of claim 2, wherein the comparing the optical flow field of the sampled image of the current frame with the optical flow field of the sampled image of the previous frame to determine whether the sample has a preset weather attribute further comprises:
calculating the difference value of the gray average pixel value of the sampling image of the current frame and the sampling image of the previous frame;
calculating the difference value of the brightness average pixel value of the sampling image of the current frame and the sampling image of the previous frame;
according to the difference value of the gray average pixel value and the difference value of the brightness average pixel value, calculating to obtain a comprehensive pixel difference value of the sampling image of the current frame and the sampling image of the previous frame;
Correspondingly, comparing the optical flow field of the sampling image of the current frame with the optical flow field of the sampling image of the previous frame to judge whether the sampling point has a preset weather attribute, including:
if the difference value between the average speed of the optical flow field of the sampling image of the current frame and the average speed of the optical flow field of the sampling image of the previous frame is smaller than or equal to a preset threshold value, and the integrated pixel difference value falls into a preset pixel range, determining that the sampling point has a preset weather attribute; otherwise, determining that the sampling point does not have the preset weather attribute.
5. The method of claim 4, wherein said calculating a difference in gray-scale average pixel values of the sampled image of the current frame and the sampled image of the previous frame comprises:
converting the sampled image of the current frame and the sampled image of the previous frame into gray images, calculating a first gray average pixel value of the converted sampled image of the current frame and a second gray average pixel value of the converted sampled image of the previous frame, and calculating a difference value of the gray average pixel values of the sampled image of the current frame and the sampled image of the previous frame according to the first gray average pixel value and the second gray average pixel value;
The calculating the difference value between the brightness average pixel value of the sampling image of the current frame and the brightness average pixel value of the sampling image of the previous frame comprises:
converting the sampled image of the current frame and the sampled image of the previous frame into a preset color space, calculating a first brightness average pixel value of the converted sampled image of the current frame and a second brightness average pixel value of the converted sampled image of the previous frame, and calculating a difference value of the brightness average pixel values of the sampled image of the current frame and the sampled image of the previous frame according to the first brightness average pixel value and the second brightness average pixel value.
6. The method according to any one of claims 1 to 5, wherein calculating the wiper stroke value of the present period according to the number of sampling points and the total number of sampling points having a preset weather property comprises:
and taking the ratio of the number of the sampling points with the preset weather attribute to the total number of the sampling points as a windshield wiper stroke value of the period.
7. The method according to any one of claims 1 to 5, wherein calculating the wiper stroke value of the present period according to the number of sampling points and the total number of sampling points having a preset weather property comprises:
And calculating the windshield wiper stroke value of the period according to the number of sampling points with preset weather attributes, the total number of the sampling points and the windshield wiper stroke value calculated in the previous period.
8. The method according to claim 7, wherein the calculating the wiper stroke value of the present period according to the number of sampling points having the preset weather attribute, the total number of sampling points, and the wiper stroke value calculated in the previous period includes:
multiplying the ratio of the number of sampling points with preset weather properties to the total number of sampling points by a first preset weight coefficient to obtain a first part, and multiplying the wiper stroke value calculated in the last detection period by a second preset weight coefficient to obtain a second part;
and summing the first part and the second part to obtain the windshield wiper stroke value in the period.
9. The method according to any one of claims 1 to 5, wherein adjusting the stroke frequency of the wiper according to the wiper stroke value of the present cycle includes:
and comparing the windshield wiper stroke value in the period with a preset stroke frequency threshold value, and adjusting the stroke frequency of the windshield wiper according to a comparison result.
10. The method of claim 9, wherein the preset swipe frequency threshold comprises a first preset limit and a second preset limit, and the first preset limit is greater than the second preset limit;
correspondingly, comparing the wiper stroke value of the present period with a preset stroke frequency threshold, and adjusting the stroke frequency of the wiper according to the comparison result, including:
if the windshield wiper stroke value in the period is greater than or equal to the first preset limit value, starting the windshield wiper and gradually increasing the stroke frequency of the windshield wiper;
if the wiper stroke value in the period is smaller than or equal to the second preset limit value, starting the wiper and gradually reducing the stroke frequency of the wiper;
and if the wiper stroke value in the period is smaller than the first preset limit value and larger than the second preset limit value, starting the wiper and maintaining the stroke frequency of the wiper at a preset fixed value.
11. The method of any one of claims 1 to 5, wherein the plurality of predetermined locations are selected from areas of the driving image that do not include moving disruptors.
12. A vehicle wiper adjustment method, characterized by comprising:
The receiving module is used for receiving the driving images of at least two continuous frames sent by the driving camera equipment;
the sampling module is used for extracting sampling images from sampling points at a plurality of preset positions of each driving image aiming at driving images of any two continuous frames to obtain a plurality of pairs of sampling images;
the detection module is used for detecting whether the sampling points have preset weather properties or not based on a pair of sampling images of each sampling point;
the calculation module is used for calculating the windshield wiper stroke value of the period according to the number of sampling points with preset weather properties and the total number of the sampling points;
and the adjusting module is used for adjusting the wiping frequency of the windshield wiper according to the wiping value of the windshield wiper in the period.
13. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing computer-executable instructions stored in the memory causes the at least one processor to perform the vehicle wiper adjustment method as set forth in any one of claims 1 to 11.
14. A computer storage medium having stored therein computer executable instructions which, when executed by a processor, implement the vehicle wiper adjustment method according to any one of claims 1 to 11.
15. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements a method of vehicle wiper adjustment according to any one of claims 1 to 11.
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| CN202310228697.7A CN116101224A (en) | 2023-03-10 | 2023-03-10 | Vehicle windshield wiper adjusting method, device, equipment and storage medium |
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| CN202310228697.7A CN116101224A (en) | 2023-03-10 | 2023-03-10 | Vehicle windshield wiper adjusting method, device, equipment and storage medium |
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