CN115984335A - A Method of Obtaining the Feature Parameters of Fog Droplets Based on Image Processing - Google Patents

Abstract

The invention aims to provide a method for acquiring characteristic parameters of fog drops based on image processing, which comprises the following steps: acquiring a fog drop image of a target area; carrying out image preprocessing on the fogdrop image; screening out a characteristic region according to the image preprocessing result, calculating the matchable fogdrops in the characteristic region, and acquiring the number of fogdrops, the pixel size occupied by the fogdrops and the coordinate position of the fogdrops image according to the image preprocessing result; and calculating the movement speed of the matchable fogdrops according to the result of the matching calculation of the matchable fogdrops in the characteristic region and the coordinate positions of the matchable fogdrops. The method for acquiring the characteristic parameters of the fog drops based on the image processing does not interfere with the wind field of the rotor wing and the motion of the fog drops, and is suitable for the wind field spraying characteristic test in a small range and in a short time.

Description

A Method of Obtaining the Feature Parameters of Fog Droplets Based on Image Processing

technical field

The invention relates to the technical field of precision agricultural aerial pesticide application, in particular to a method for obtaining characteristic parameters of droplets based on image processing.

Background technique

The application of chemical pesticides for pest control is the most important and effective control method used in our country at present.

With the rapid development of my country's agricultural aviation industry, the spraying method of plant protection drones has the characteristics of high operating efficiency, strong adaptability, and less environmental pollution; but the process of plant protection drone spraying is affected by the rotor wind field and the external wind field Under the influence, the drift and deposition of liquid mist droplets will be caused, which will affect the spraying effect of plant protection drones. There are following problems at prior art:

1. In the process of pesticide spraying, the deposition effect of liquid spray droplets is an important indicator that users pay attention to, and the drift of chemical liquid is the main reason for the low effective utilization rate of pesticides and environmental pollution. Existing technologies mainly focus on the analysis of the influence of droplet size on droplet deposition and drift. Most of the research is to change the operating parameters to change the droplet size to reduce the drift problem in the spraying process; the movement of the droplet itself is ignored. Velocity also has a greater impact on droplet deposition motion.

2. In the process of analyzing the deposition and drift of droplets, water-sensitive paper, wiring measurement and other means are used to measure the drift rate, and the drift rate is obtained by measuring the droplet parameters on the water-sensitive paper; There will be diffusion phenomenon in the process, and there will be some errors in the drift rate or deposition amount.

Contents of the invention

The technical problem to be solved in the present invention is to provide a method for obtaining the characteristic parameters of the droplets based on image processing, and the calculated characteristic parameters of the droplets include the size of the droplets, the position coordinates of the droplets and the moving speed of the droplets. The invention does not interfere with the wind field of the rotor and the movement of the droplets, and is suitable for small-scale, short-term spray characteristic tests of the wind field to solve the problems raised in the above-mentioned background technology.

The present invention proposes a method for obtaining characteristic parameters of fog droplets based on image processing, including:

Obtain the droplet image of the target area;

Perform image preprocessing on the droplet image;

According to the results of image preprocessing, filter out the feature area, calculate the matching droplets in the feature area, and obtain the number of droplets, the pixel size of the droplets and the coordinate position of the droplets according to the results of image preprocessing;

According to the matching calculation result of the matching droplet in the feature area and the coordinate position of the matching droplet, the motion velocity of the matching droplet is calculated.

Preferably, the image preprocessing of the droplet image includes:

Obtain the original color image of the droplet;

The original color image is converted into a binarized image by fixed threshold binarization;

Perform dilation operations on the binarized image;

Perform Laplacian image enhancement on the dilated image.

Preferably, the dilation operation includes: first defining a 3*3 cross-shaped structural element;

Traverse each pixel of the binarized image, and judge the correspondence between the 3*3 neighborhood of the pixel and the origin of the structural element;

If the value of the pixel point is 0, then the 3*3 neighborhood of the pixel point corresponds to the position of the structural element;

If the value in the corresponding structure element is 1, reset the value of the pixel to 1.

Preferably, the matching calculation of the matchable droplets in the characteristic area includes:

Input two adjacent images that have undergone image preprocessing, and perform HARRIS corner detection first image calculation to obtain candidate corners;

Calculate the relevant information of the candidate corner points, and perform the feature description of the candidate corner points;

According to the feature description, the corner points in the first image and the second image with the highest matching degree calculated using normalized cross-correlation;

Display the matching droplet results and return the relevant information of the corresponding matching corner points.

Preferably, the HARRIS corner point detection first image calculation to obtain candidate corner points includes, using the convolution frame to perform up or down, left or right translation of one pixel point on each point of the first image translation operation;

For each pixel (x, y) in the image, in the neighborhood of the 9*9 convolution frame, use the Sobel operator with a size of 9 to calculate the covariance matrix M of the gradient map;

The corner point response function R is defined as 0.06, and the result of judging R>0.04 is regarded as a corner point.

Preferably, calculating and identifying the characteristic parameters of the droplets and extracting the relevant information of the droplets, specifically including: after using the findContours function to identify the outline of each droplet, using the labeling algorithm to follow the rules from left to right and from top to bottom Label the droplets;

Use the count_nonzero function to calculate the number of pixels in each droplet outline;

Use the boundingRect function to extract the bounding rectangle coordinates of the droplet outline, and calculate the center position of the droplet;

Calculation can match the velocity of the droplets;

According to the results of the matching droplets in the neighborhood droplet matching calculation part and the coordinate position of the matching droplets, the motion velocity of the matching droplets is calculated.

Preferably, the droplet size in the droplet characteristic parameters can be calculated by the following formula:

M=L/N D=(P*M)/a

Among them, M is the actual length of a pixel, unit: cm;

L is the grid size in grid calibration, unit: cm;

N is the number of pixels occupied by the grid in the image, unit: pixel;

D is the original size of the droplet, unit: cm;

P is the number of pixels in the image occupied by fog droplets, unit: pixel;

a is the relationship coefficient.

Preferably, the calculation that can match the velocity of motion of a pair of droplets can be calculated by the following formula:

S=M*√[(X ₁ -X ₂ ) ² +(Y ₁ -Y ₂ ) ² ]

Wherein, S is the actual distance of the droplet movement; X ₁ , X ₂ , Y ₁ , Y ₂ are obtained from the coordinate position of the center of the droplet in the calculation of the droplet characteristic parameters;

M is the actual length of a pixel, unit: cm, the calculation method is consistent with the calculation formula in the calculation of the original size of the droplet;

V=S/T

Among them, V is the speed of the droplet movement, unit: m/s, S is the actual distance of the droplet movement, unit: cm; T is the time interval between two images, unit: s.

The present invention obtains the droplet image of the target area; performs image preprocessing on the droplet image; screens out the feature area according to the image preprocessing result, calculates the matching droplet in the feature area, and obtains the fog according to the image preprocessing result The number of droplets in the droplet image, the pixel size of the droplets and the coordinate position of the droplets; according to the matching calculation results of the matching droplets in the feature area and the coordinate positions of the matching droplets, the movement speed of the matching droplets can be calculated at a high speed Camera shooting, image processing technology and computer vision technology processing and analysis, get the characteristic parameters of the spray during the spraying process, which provides a new method for the study of the actual movement of the droplets during the spraying process and the calculation of the deposition and drift of the droplets.

Description of drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description serve to explain the principles of the invention.

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, In other words, on the premise of no creative labor, other drawings are also obtained based on these drawings.

Fig. 1 is the schematic flow chart of the method for obtaining the characteristic parameters of droplets based on image processing in the present invention;

Fig. 2 shows the figure with connecting line between the matching corner points in the field droplet matching step;

Fig. 3 is a schematic diagram of outputting the minimum oblique rectangle and calculating the coordinate position of the center point in the calculation step of the droplet characteristic parameters.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

It should be noted that all directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relationship between the components in a certain posture (as shown in the figure). Relative positional relationship, movement conditions, etc., if the specific posture changes, the directional indication will also change accordingly.

In addition, the descriptions involving "first", "second" and so on in the present invention are only for descriptive purposes, and should not be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" explicitly or implicitly include at least one of these features. In addition, the technical solutions of various embodiments are combined with each other, but it must be based on the realization of those skilled in the art. When the combination of technical solutions is contradictory or cannot be realized, it should be considered that the combination of technical solutions does not exist. Also not within the scope of protection required by the present invention.

If only a few air relay communication nodes with relatively fixed positions are built in the air, it will be difficult to adapt to the rapidly changing position of the combat team, and it is still impossible to ensure efficient communication between the combat teams and the entire combat unit. The present invention determines whether a relay communication node is needed between combat teams; if so, adjusts the quantity and position of relay communication drones according to the distance between combat teams. Facing the complex battlefield environment of urban operations, on the premise of not changing the communication equipment, the method of real-time adjustment of the position of the communication relay UAV is used to realize effective and stable information communication of each combat team, and provide effective information support for command operations; The real-time calculation and dynamic adjustment of the man-machine position ensure the stability and effectiveness of information communication.

Example 1

The present invention relates to the method for obtaining droplet characteristic parameters based on image processing, with reference to Fig. 1, comprises the following steps:

1. Obtain the droplet image of the target area;

Specifically, the fog droplet image collection step uses a high-speed camera to collect the fog droplet field image, and obtains the fog droplet image of the target area;

2. Perform image preprocessing on the fog droplet image;

Specifically, the computer acquires the image information of the fog droplets and performs image preprocessing, and the high-speed camera transmits the image information of the fog droplets to the computer for image preprocessing;

3. According to the results of image preprocessing, filter out the feature area, calculate the matching droplets in the feature area, and obtain the number of droplets, the pixel size of the droplets and the coordinates of the droplets according to the results of image preprocessing Location;

Specifically, in the neighborhood droplet matching calculation step, according to the result of the image preprocessing, the feature area is screened out, and the matchable droplets in the feature area are calculated.

4. According to the matching calculation result of the matching droplet in the characteristic area and the coordinate position of the matching droplet, calculate the moving speed of the matching droplet.

Specifically, the droplet feature parameter calculation step is to obtain the number of droplets in the droplet image, the pixel size and the coordinate position of the droplets according to the results of the image preprocessing; Match the coordinate position of the droplet and calculate the speed of motion of the droplet.

The present invention obtains the droplet image of the target area; performs image preprocessing on the droplet image; screens out the feature area according to the image preprocessing result, calculates the matching droplet in the feature area, and obtains the fog according to the image preprocessing result The number of droplets in the droplet image, the pixel size of the droplets and the coordinate position of the droplets; according to the matching calculation results of the matching droplets in the feature area and the coordinate positions of the matching droplets, the movement speed of the matching droplets can be calculated at a high speed Camera shooting, image processing technology and computer vision technology processing and analysis, get the characteristic parameters of the spray during the spraying process, which provides a new method for the study of the actual movement of the droplets during the spraying process and the calculation of the deposition and drift of the droplets.

For step one, the specific implementation method is: illuminate the shooting area with the help of frequency-free light source, use high-speed camera equipment to perform grid calibration, and then set the shooting frame rate to collect fog drop images;

In this embodiment, the high-speed camera is placed in the lower left area of the entire spray plane, 40cm away from the spray plane, and the size of the spray area actually photographed is 5cm*8cm; and the grid paper with the size of 1cm*1cm is used for calibration; the shooting frame The rate F is set to 2000fp/s, and the continuous spraying time is 3s.

For step 2, the computer obtains the droplet image information and performs image preprocessing;

The computer obtains the mode of fog droplet image, can adopt the standard interface mode such as USB interface, twisted pair to transmit fog droplet image in the computer;

The main operations of the preprocessing of the original droplet image include converting the original color image acquired by the computer into a binarized image through fixed threshold binarization; performing dilation operations on the binarized image; Carry out the Laplacian image enhancement operation; The further operation of preprocessing the original fog droplet image includes the following steps:

For each frame of image acquired, according to the number and distribution characteristics of fog droplets in the image, image processing technology is used for preprocessing analysis. Firstly, for the acquired 24bit RGB original color image, set a fixed threshold value of 127, and convert the original color image into a binary image.

Then, perform a 3*3 expansion operation on the binary image, first define a 3*3 cross-shaped structural element, and then traverse each pixel of the image, and compare the 3*3 neighborhood of the pixel with the structure The origin of the element is correspondingly judged; if the value of the pixel point is 0, then the 3*3 neighborhood of the pixel point is judged against the corresponding position of the structural element, and if the value of the corresponding structural element is 1, the pixel is The point value is reset to 1.

Image enhancement is performed on the image after the dilatation operation. The selected image enhancement method is the Laplacian algorithm, and the Laplacian operator size ksize is set to 5. The Laplacian image enhancement algorithm is a commonly used image enhancement algorithm, which can produce a very obvious gray boundary, which is beneficial to the subsequent extraction of fog droplet contours.

On the basis of the above-mentioned embodiments, the further operation of the step three neighborhood droplet matching calculation is as follows:

In the first step, two adjacent images that have been preprocessed by the image are input, and the corner points (pixels) that are most easily recognized are searched for as detectors; this step uses the HARRIS corner detection calculation method to extract detectors. The specific principle is to use the convolution frame to perform a translation operation after moving up or down, left or right by one pixel point on each point on the map. If the gray value in the convolution frame has a large change, then it is considered There are corner points in the area where the convolution frame is located. In the specific implementation, for each pixel (x, y) in the image, in the neighborhood of the 9*9 convolution frame, use the Sobel operator with a size of 9 to calculate the covariance matrix M of the gradient map; set the angle The point response function R is 0.06, and the result of judging the corner point response function R>0.04 is regarded as a corner point.

The second step is to calculate the relevant information of the candidate corner points and describe the corner point features. The further steps are: set the corner threshold to 0.1, and search for candidate corners higher than the threshold in the HARRIS response image in step E; after finding the candidate corners, obtain their coordinates and save them in an array, and obtain the HARRIS of the candidate corners at the same time Response value, and sort by the size of the response value.

Find the best HARRIS point among the candidate corner points. The specific method is: set the minimum number of pixels to segment corner points and image boundaries as min_dist, and set its value to 10; determine whether the value of min_dist is greater than 10, only min_dist is greater than 10 The corner point of is regarded as the best HARRIS point;

Find the best HARRIS point, but the HARRIS corner detection method does not provide a method for matching corners according to the information of the corners, so the descriptor must be extracted as the feature of each corner. The corner descriptor after Harris corner detection is usually the information of its surrounding image pixel blocks.

Therefore, in this embodiment, by setting the width wid to 9, the value of 2*wid+1 pixels around the best HARRIS point is returned as the corner point descriptor.

The third step is to calculate the matching degree of the paired corner point descriptors; this step uses the normalized cross-correlation calculation method (NCC), and NCC is used to indicate the degree of correlation between the normalized targets to be matched.

The specific matching method is as follows: combined with the feature descriptors in the above steps, for each corner descriptor in the first image, use the normalized cross-correlation calculation method (NCC) to select its matching in the second image The corner with the highest degree is obtained to obtain the matching pair of corners.

The fourth step is to display the matching droplet results, and return the relevant information of the corresponding matching corner points; stitch two adjacent fog droplet images into a new image, and then obtain the positions of the paired feature descriptors that are successfully matched, and display a A picture with lines connecting the matching corners is shown in Figure 2.

On the basis of the above embodiments, the droplet characteristic parameters can be calculated. The droplet characteristic parameters in this embodiment mainly include: the number of droplets in the droplet image, the pixel size and the coordinate position of the droplets; The velocity of the drop.

The above-mentioned method for calculating the characteristic parameters of the droplets, the step of calculating the characteristic parameters of the droplets in the step 4 further includes:

Identify the droplets and extract the relevant information of the droplets (the number of droplets, the size of the pixels occupied, the coordinate position of the droplets and the movement speed of the droplets that can be matched), the main implementation process consists of identifying the droplets, sorting and calculating the characteristic parameters of the droplets It consists of two parts:

The further operation of identifying and sorting the droplets is: to identify and extract the contour of each droplet in the droplet image, in the present invention, the findContours function is used to identify the contour of each droplet. For each extracted droplet contour, use the sort_contours function to label the droplet contours according to the rule order from left to right and from top to bottom;

In the step of calculating the characteristic parameters of the droplets, the characteristic parameters of the droplets involved in the present invention include the number of droplets, the pixel size occupied by the droplets, the coordinate position of the droplets, and the moving speed of the droplets that can be matched.

Regarding the statistics of the number of droplets, the total number of droplets in a droplet image can be obtained after the step of identifying and sorting the droplets, which will not be described here.

With regard to calculating the size of the pixels occupied by the droplets, this embodiment uses the count_nonzero function to calculate the number of pixels in the outline of each droplet; the count_nonzero function can return the number of pixels whose gray value is not 0, and can more accurately calculate the number of pixels that the droplets occupy. The size in pixels. It is worth noting that this step is calculated on the basis of the image preprocessing in step 2, and in the image preprocessing there are dilation processing and Laplacian image enhancement operations on the fog droplets, which will make the fog The size of the droplet becomes larger and thicker; therefore, the size of the pixels occupied by the droplets calculated in this step is not the size of the pixels occupied by the real droplets.

In the follow-up calculation, it is found that in the present invention, the droplet size after image preprocessing is about 3 times larger than the droplet size in the original color image, and the specific and detailed coefficient relationship remains to be further studied; therefore, this step calculates The size of the pixels occupied by the droplets can be used to compare the size of the droplets; the original size of the droplets is not particularly accurate, and based on the fact that many scholars are still interested in the original size of the droplets, the present invention provides a calculation based on an estimated relationship coefficient The formula for calculating the original size of the droplet.

M=L/N D=(P*M)/a

Among them, M is the actual length of a pixel; L is the grid size in grid calibration; N is the number of pixels occupied by the grid in the image. D is the original size of the droplet, P is the number of pixels in the image occupied by the droplet, and a is the relationship coefficient.

Regarding the calculation of the coordinate position of the droplet, this embodiment uses the minAreaRect function to extract the boundary rectangle coordinates of the droplet outline, and calculates the center coordinate position of the droplet; the minAreaRect function can calculate the smallest oblique rectangle covering the outline, and can obtain the droplet's position more accurately. Center position; at the same time, the minAreaRect function can output the coordinates (x, y) of the center point in the smallest oblique rectangle and the width and height of the oblique rectangle, as shown in Figure 3.

The calculation of the motion speed of the matchable droplets is as follows: According to the results of the matchable droplets in the third step of the neighborhood droplet matching calculation part, find out the paired matching droplets, and then combine the characteristic parameter information of the above droplets with the following The formula is calculated to match the speed of motion of the droplets.

S=M*√[(X ₁ -X ₂ ) ² +(Y ₁ -Y ₂ ) ² ]

Wherein S is the actual distance of droplet movement; X ₁ , X ₂ , Y ₁ , Y ₂ are obtained in the coordinate position of the center of the calculation droplet in the calculation droplet characteristic parameters; M is the actual length of a pixel , the calculation method is consistent with the calculation formula in the calculation of the original size of the droplet.

V=S/T

Among them, V is the speed of the droplet movement, unit: m/s, S is the actual distance of the droplet movement, unit: cm; T is the time interval between two images, unit: S.

The above descriptions are only specific embodiments of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Accordingly, the present invention will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims (8)

1. The method for obtaining fog drop feature parameters based on image processing, is characterized in that, comprising: Obtain the droplet image of the target area; Perform image preprocessing on the droplet image; According to the results of image preprocessing, filter out the feature area, calculate the matching droplets in the feature area, and obtain the number of droplets, the pixel size of the droplets and the coordinate position of the droplets according to the results of image preprocessing; According to the matching calculation result of the matching droplet in the feature area and the coordinate position of the matching droplet, the motion velocity of the matching droplet is calculated. 2. according to claim 1, based on the image processing method for obtaining the characteristic parameters of the droplets, it is characterized in that, the image preprocessing is carried out to the droplets image, comprising: Obtain the original color image of the droplet; The original color image is converted into a binarized image by fixed threshold binarization; Perform dilation operations on the binarized image; Perform Laplacian image enhancement on the dilated image. 3. The method for obtaining the characteristic parameters of droplets based on image processing according to claim 2, wherein the dilation operation comprises: first defining a 3*3 cross-shaped structural element; Traverse each pixel of the binarized image, and judge the correspondence between the 3*3 neighborhood of the pixel and the origin of the structural element; If the value of the pixel point is 0, then the 3*3 neighborhood of the pixel point corresponds to the position of the structural element; If the value in the corresponding structure element is 1, reset the value of the pixel to 1. 4. according to claim 2 based on the image processing method for obtaining the characteristic parameters of the droplets, it is characterized in that the matching calculation of the droplets that can be matched in the feature area includes: Input two adjacent images that have undergone image preprocessing, and perform HARRIS corner detection first image calculation to obtain candidate corners; Calculate the relevant information of the candidate corner points, and perform the feature description of the candidate corner points; According to the feature description, the corner points in the first image and the second image with the highest matching degree calculated using normalized cross-correlation; Display the matching droplet results and return the relevant information of the corresponding matching corner points. 5. according to claim 4 based on the method for obtaining the droplet characteristic parameter of image processing, it is characterized in that, described carrying out HARRIS corner point detection first image calculation to obtain candidate corner point, comprise, use convolution frame in the first image Each point of each point is translated up or down, left or right after a translation operation of one pixel; For each pixel (x, y) in the image, in the neighborhood of the 9*9 convolution frame, use the Sobel operator with a size of 9 to calculate the covariance matrix M of the gradient map; The corner point response function R is defined as 0.06, and the result of judging R>0.04 is regarded as a corner point. 6. according to claim 3 based on the image processing method of obtaining the characteristic parameter of the droplet, it is characterized in that comprising: Calculate and identify the characteristic parameters of the droplets and extract the relevant information of the droplets, specifically including: after using the findContours function to identify the outline of each droplet, use the labeling algorithm to give the droplets according to the rules from left to right and from top to bottom labeling; Use the count_nonzero function to calculate the number of pixels in each droplet outline; Use the boundingRect function to extract the bounding rectangle coordinates of the droplet outline, and calculate the center position of the droplet; Calculation can match the velocity of the droplets; According to the results of the matching droplets in the neighborhood droplet matching calculation part and the coordinate position of the matching droplets, the motion velocity of the matching droplets is calculated. 7. according to claim 6 based on the method for obtaining the droplet characteristic parameter based on image processing, it is characterized in that, the droplet size can be calculated and obtained by the following formula in the described droplet characteristic parameter: M=L/N D=(P*M)/a Among them, M is the actual length of a pixel, unit: cm; L is the grid size in grid calibration, unit: cm; N is the number of pixels occupied by the grid in the image, unit: pixel; D is the original size of the droplet, unit: cm; P is the number of pixels in the image occupied by fog droplets, unit: pixel; a is the relationship coefficient. 8. The method for obtaining characteristic parameters of droplets based on image processing according to claim 6, is characterized in that, comprising: the calculation of the velocity of motion of the paired droplets can be calculated and obtained by the following formula: S=M*√[(X ₁ -X ₂ ) ² +(Y ₁ -Y ₂ ) ² ] Wherein, S is the actual distance of the droplet movement; X ₁ , X ₂ , Y ₁ , Y ₂ are obtained from the coordinate position of the center of the droplet in the calculation of the droplet characteristic parameters; M is the actual length of a pixel, unit: cm, the calculation method is consistent with the calculation formula in the calculation of the original size of the droplet; V=S/T Among them, S is the actual distance of the droplet movement, V is the velocity of the droplet movement, unit: m/s, S is the actual distance of the droplet movement, unit: cm, T is the time interval between two images, unit: s.

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