Dispensing quality evaluation method and device

Abstract

The embodiment of the invention provides a dispensing quality evaluation method and device. The method comprises the steps that an acquisition image of a dispensing area is acquired; acquiring point cloud data based on the acquired image; obtaining colloid characteristic data based on the point cloud data; calculating the colloid thickness after the colloid is flattened based on the colloid characteristic data; and evaluating the dispensing quality based on the colloid characteristic data and the colloid thickness. According to the method and the device, the problem that the dispensing amount is difficult to accurately judge in related technologies is solved, and the effect of improving the accuracy of judging the dispensing amount is further achieved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of industrial intelligent detection, and specifically, to a method and device for evaluating dispensing quality. Background Art

[0002] In the production process of heat dissipation devices, dispensing robots are introduced into production lines and production workshops to replace manual work. Due to the chemical and physical properties of heat dissipation silicone, the corrosiveness to rubber hoses can cause abnormal valve closure and release, thereby affecting the amount of heat dissipation silicone dispensed.

[0003] In the quality inspection process, the mainstream detection technology is to use a 2D camera detection solution, using ROI (region of interest) and image processing technology to measure the area of ​​the colloid, and set the threshold offset for judgment based on the size and proportion of the area.

[0004] The above method can only vaguely determine whether there is glue in the area of ​​interest, and the result of judging the amount of glue dispensed is not accurate. Summary of the invention

[0005] The embodiments of the present application provide a method and device for evaluating glue dispensing quality, so as to at least solve the problem in the related art of improving the accuracy of judging the glue dispensing amount.

[0006] According to one embodiment of the present application, a method for evaluating dispensing quality is provided, comprising:

[0007] Acquire a collection image of the dispensing area;

[0008] Acquire point cloud data based on the collected image;

[0009] Acquire colloid characteristic data based on the point cloud data;

[0010] Calculating the thickness of the colloid after the colloid is flattened based on the colloid characteristic data;

[0011] The dispensing quality is evaluated based on the colloid characteristic data and the colloid thickness.

[0012] According to another embodiment of the present application, a dispensing quality assessment device is provided, comprising:

[0013] An acquisition module is used to acquire a collection image of the dispensing area through a binocular structured light camera;

[0014] a processing module, configured to acquire point cloud data based on the collected image; and

[0015] Acquire colloid characteristic data based on the point cloud data; and,

[0016] Calculating the thickness of the colloid after the colloid is flattened based on the colloid characteristic data; and,

[0017] The dispensing quality is evaluated based on the colloid characteristic data and the colloid thickness.

[0018] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0019] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0020] Through the present application, by obtaining the real shape and volume information and calculating the colloid thickness, the dispensing quality can be evaluated more accurately, which solves the problem of inaccurate dispensing amount results in the prior art and thereby improves the accuracy of judging the dispensing amount. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A method for evaluating dispensing quality according to an embodiment of the present application is described as follows: Figure 1 ;

[0022] Figure 2 A method for evaluating dispensing quality according to an embodiment of the present application is described as follows: Figure 2 ;

[0023] Figure 3 A method for evaluating dispensing quality according to an embodiment of the present application is described as follows: Figure 3 ;

[0024] Figure 4 A method for evaluating dispensing quality according to an embodiment of the present application is described as follows: Figure 4 ;

[0025] Figure 5 It is a structural schematic diagram of a dispensing quality assessment device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0027] In this embodiment, a method for evaluating dispensing quality is provided. Figure 1 is a flow chart of a method for evaluating dispensing quality according to an embodiment of the present application. Figure 1 As shown, the process includes the following steps:

[0028] Step S101, acquiring a collection image of the dispensing area;

[0029] In an exemplary embodiment, the image of the workstation to be inspected is acquired by a three-dimensional structured light camera, for example, a DMD (digital micromirror chip) module is used to project coded structured light and two high-resolution industrial cameras are used to collect image data.

[0030] Step S102, acquiring point cloud data based on the collected image;

[0031] In an exemplary implementation, a multi-step phase method and parallax calculation are used to obtain point cloud data under each camera perspective.

[0032] The multi-step phase shift method is a method for measuring optical wavefronts. It is based on the principle of interference and measures the shape and phase information of optical wavefronts by introducing multiple phase steps in the optical path.

[0033] In the multi-step phase method, the optical wavefront is first divided into two optical paths by a beam splitter. Then, in each optical path, the phase of the light in the two optical paths is changed differently by introducing different phase steps, such as plane wave plates, liquid crystals, or electric modulators. Then, the light in the two optical paths is combined again to produce an interference image through the interference effect. By analyzing the brightness distribution and phase difference in the interference image, the shape and phase information of the optical wavefront can be inferred.

[0034] The multi-step phase method can provide high-precision optical wavefront measurement results and is widely used in the design of optical systems, the manufacture of optical components, and the measurement of optical surface topography. Its advantages include high measurement accuracy, wide measurement range, and applicability to light sources of different wavelengths.

[0035] In an exemplary embodiment, the point cloud data under two camera perspectives is calculated using parallax using the following method:

[0036] Capture images from two camera perspectives: Use two cameras to capture the same scene at the same time, keeping the horizontal distance between the cameras to ensure the accuracy of parallax.

[0037] Extract feature points: For each image, use a feature point detection algorithm (such as SI FT, SURF, or ORB) to extract feature points in the image. These feature points should have properties that can be matched in both views.

[0038] Feature point matching: By matching the feature points under two perspectives, find the point pairs with the same features in the two images. This can be achieved using feature descriptors (such as SI FT descriptors) and feature matching algorithms (such as the nearest neighbor algorithm).

[0039] Calculate disparity: For each pair of matched feature points, calculate the difference in their horizontal positions on the image, i.e., disparity. The disparity can be obtained by calculating the difference in the x-axis coordinates of the feature points.

[0040] Triangulation: Using parallax, the 3D coordinates of each matching point can be calculated by triangulation. Triangulation is the process of calculating the 3D position of a point using the camera parameters and parallax under two viewing angles. Based on the parallax and camera parameters, the 3D coordinates of each matching point can be calculated using the principle of triangulation.

[0041] Generate point cloud data: Convert the 3D coordinates of each matching point into point cloud data. Point cloud data is a collection of points, each of which contains its 3D coordinates and possible other attributes (such as color, normal, etc.).

[0042] Through the above method, the point cloud data under the two camera perspectives can be calculated using parallax. These point cloud data can be used for 3D reconstruction, depth estimation and other related applications.

[0043] Step S103, acquiring colloid characteristic data based on the point cloud data;

[0044] Furthermore, the colloid characteristic data includes: the total volume of the colloid, the area of ​​the colloid, the position of the colloid and the height of the colloid at each point, wherein the total volume of the colloid, the area of ​​the colloid and the position of the colloid are the overall data of all single points in the dispensing area.

[0045] Step S104, calculating the thickness of the colloid after the colloid is flattened based on the colloid characteristic data;

[0046] Step S105 , evaluating the dispensing quality based on the colloid characteristic data and the colloid thickness.

[0047] By introducing point cloud data and colloid feature data and calculating the colloid thickness, the problem of difficult to accurately judge the amount of glue dispensed in traditional 2D camera detection solutions is solved. By comprehensively evaluating the quality of glue dispensed, the accuracy of judging the amount of glue dispensed can be improved, thereby improving the quality and efficiency of heat dissipation silicone dispensing work.

[0048] In one embodiment, calculating the thickness of the colloid after being flattened based on the colloid characteristic data includes: calculating the thickness of the colloid after being flattened based on the total volume of the colloid and the area of ​​the glue-dispensing region.

[0049] In an exemplary embodiment, the area of ​​the dispensing area can be known before processing, and the thickness of the colloid after the colloid is flattened can be calculated by dividing the calculated total volume of the colloid by the area of ​​the dispensing area. The thickness of the colloid after the colloid is flattened can be compared with a standard threshold value, which can be used as a reference factor for evaluating the dispensing quality.

[0050] In an exemplary embodiment, before preparing to flatten the colloid, a mold is generally placed in the glue dot area (for example, a square frame is used to surround the glue dot area, and the inner wall of the square frame is used to prevent the colloid from overflowing the glue dot area during the process of flattening the colloid). The total volume of the colloid is divided by the area of ​​the glue dot area (or the area surrounded by the square frame) to obtain the thickness of the colloid after the colloid is flattened.

[0051] In an exemplary embodiment, before acquiring point cloud data based on the captured image, the method further includes: calibrating camera intrinsic parameters and system parameters with the assistance of a calibration board.

[0052] Specifically, the following methods can be used to assist in the calibration of camera intrinsic parameters and system parameters:

[0053] Prepare the calibration target: Choose a calibration target with a specific geometry and known dimensions, such as a checkerboard or dot target. Make sure the target is flat, undamaged, and clearly visible in the camera image.

[0054] Camera settings: Place the camera in a fixed position and make sure there is no obstruction between it and the calibration plate. Adjust the camera's focal length, exposure time and other parameters to ensure a clear image.

[0055] Take images: Use the camera to take a series of images containing the calibration plate. Take multiple images at different angles, distances, and poses to ensure that the calibration plate occupies different positions in the image.

[0056] Extract corner points: Use image processing software or computer vision library to extract corner points for each image. Corner points are the intersection points of specific geometric shapes on the calibration plate and can be extracted by detecting straight lines and edges in the image.

[0057] Calibrate camera internal parameters: Use the extracted corner point coordinates and apply the camera calibration algorithm to calculate the camera's internal parameters, including focal length, principal point coordinates, and distortion parameters. Commonly used camera calibration algorithms include Zhang Zhengyou calibration method and Tsai algorithm.

[0058] Calibrate system parameters: Based on the known size of the calibration plate and the camera intrinsic parameters, use the geometric relationship of the calibration plate and the camera model to derive system parameters, such as the distance from the camera to the calibration plate, the camera posture, etc.

[0059] Verification and optimization: Use the calibration results to perform distortion correction and pose estimation on new images to verify the accuracy of the calibration. If the calibration results do not meet the requirements, you can optimize them by increasing the number of captured images, changing the position and pose of the calibration board, etc.

[0060] Therefore, through the above steps, the calibration board can be used to assist in the calibration of camera intrinsic parameters and system parameters, thereby improving the measurement and analysis accuracy of camera images.

[0061] In one embodiment, obtaining colloid characteristic data based on the point cloud data specifically includes:

[0062] Step S201, converting point cloud data into a depth map to obtain depth information;

[0063] Point cloud data is three-dimensional data composed of a series of point coordinates, each point represents a specific position on the colloid surface. Depth map is the conversion of point cloud data into a two-dimensional image, in which each pixel represents the depth information of the point at the corresponding position. By converting point cloud data into a depth map, it is easier to obtain the depth information of each point.

[0064] Step S202, obtaining the colloid height of each point based on the depth information.

[0065] Using the depth information in the depth map, the height of the colloid at each point can be calculated. The depth information represents the distance of each point in the point cloud data relative to the camera. By calculating the depth value of each point, the height of the colloid at the location of the point can be obtained. The height of the colloid refers to the distance from the surface of the colloid to the bottom of the colloid. By obtaining the height of the colloid at each point, the three-dimensional shape and thickness of the colloid can be obtained.

[0066] Through the above steps, the point cloud data can be converted into a depth map, and the height of the colloid at each point can be obtained based on the depth information. In this way, more accurate colloid morphology and thickness information can be obtained, providing a more powerful basis for subsequent dispensing quality evaluation.

[0067] In one embodiment, after converting the point cloud data into a depth map and obtaining the depth information, the method further includes:

[0068] Step S301, converting the depth map into a grayscale map;

[0069] A depth map is an image that contains the depth information of each pixel, while a grayscale image is an image that converts the depth information in the depth map into grayscale values. After conversion to a grayscale image, the grayscale value of each pixel represents the depth information of the point. By converting a depth map to a grayscale image, it is easier to process and analyze the depth information.

[0070] Step S302 : Separating the colloid region from the standard plane based on the grayscale image to determine the colloid region.

[0071] The colloid area can be separated from the background area by threshold segmentation or other image processing algorithms on the grayscale image. The colloid area represents the position and morphological information of the colloid in the depth map, and the standard plane is a pre-determined reference plane used to judge the height and thickness of the colloid. By separating the colloid area from the standard plane, the accurate colloid area can be obtained, providing accurate data for subsequent colloid quality assessment.

[0072] Through the above steps, the depth map can be converted into a grayscale map, and the colloid area can be separated from the standard plane based on the grayscale map to determine the colloid area. In this way, an accurate colloid area can be obtained, providing a stronger basis for subsequent dispensing quality evaluation.

[0073] In one embodiment, after separating the colloid region from the standard plane based on the grayscale image to determine the colloid region, the method further includes:

[0074] Fill the colloid region with multiple standard squares of preset sizes to obtain multiple segmented regions.

[0075] A standard square is a square of known size, such as a 1 square millimeter square, which can be used as a reference object. By filling multiple standard squares in the colloid area, a part or all of the colloid area can be covered. In this way, the colloid image can be segmented into multiple segmented areas, and the data corresponding to the entire colloid image can be segmented, which can show more details for subsequent calculations and analysis, which is conducive to improving the accuracy of calculations and analysis.

[0076] In one embodiment, after filling the colloid region with a plurality of standard squares of a preset size to obtain a plurality of segmented regions, the method further includes:

[0077] Step S401, calculating the average height of the colloid in the segmented area based on the height of the colloid at each point in the segmented area;

[0078] The segmented area may include a plurality of adjacent point cloud data. The height average is obtained by averaging the colloid heights corresponding to the plurality of point cloud data, and the height average is used as the height of the colloid in the segmented area.

[0079] Step S402, obtaining the volume of the colloid in the segmented area based on the area and height of the standard square;

[0080] By multiplying the area of ​​the standard square and the average height in the corresponding segmented area, the colloid volume in the corresponding colloid area can be obtained.

[0081] Step S403, obtaining the total volume of the colloid based on the volume of the colloid.

[0082] The total volume of the colloids can be obtained by adding up the multiple colloid volumes obtained in step S402.

[0083] The volume of the colloid in each segmented area is first calculated, and then the total volume of the colloids is obtained by adding up the multiple colloids, which improves the accuracy of the measurement results.

[0084] In one embodiment, after calculating the colloid area based on the standard area, the following steps may also be included:

[0085] Calculate the height mean based on the colloid height at each point;

[0086] For each point in the colloid area, the corresponding colloid height can be obtained according to its depth or grayscale value. The mean height can be obtained by summing the colloid heights of all points and dividing it by the total number of points. The mean height represents the average height level of the colloid and can be used to describe the thickness or height distribution of the colloid.

[0087] The total volume of the colloid was obtained based on the average value of the colloid area and height.

[0088] The colloid area is calculated in the previous step, and the height average is calculated in the above step. The total volume of the colloid can be obtained by multiplying the colloid area by the height average. This is because the total volume of the colloid can be regarded as the extension of the colloid area in the vertical direction, that is, the colloid area multiplied by the height average of the colloid.

[0089] Through the above method, the height average can be calculated based on the height of the colloid at each point, and the total volume of the colloid can be obtained based on the colloid area and the height average. In this way, the volume information of the colloid can be obtained conveniently and quickly, providing accurate data for subsequent colloid quality assessment and improving the time efficiency of detection.

[0090] In an exemplary embodiment, the main factors affecting the thickness of the colloid after being flattened include factors such as the magnitude of the applied pressure, the properties of the colloid, and the structure of the colloid. However, during one or more processing, the magnitude of the applied pressure is a fixed value, the properties and structure of the colloid used are also fixed, and the location or area of ​​the glue dot is fixed. Therefore, through the method of the present application, under the premise that the magnitude of the applied pressure, the properties of the colloid, the structure of the colloid, the location or area of ​​the glue dot and other factors are fixed, after measuring the colloid area and volume of the colloid before the colloid is flattened, the thickness of the colloid after the colloid is flattened can be calculated.

[0091] Furthermore, in the actual processing process, the position or area of ​​the glue point may deviate, but before applying pressure to the colloid, the magnitude of the applied pressure, the properties of the colloid, the structure of the colloid and other factors are determined values. Through the method of the present application, the colloid thickness after the colloid is flattened can be calculated by combining the colloid volume and the colloid area, on the premise that the colloid position is correct. Specifically, when the colloid volume and the colloid area are known, after multiple experiments, by changing the magnitude of the pressure, the properties of the colloid, the structure of the colloid and other factors, the corresponding colloid thickness after the colloid is flattened can be obtained. The specific experimental method will not be repeated here.

[0092] In one embodiment, after converting the depth map into a grayscale map, the method further includes:

[0093] The brightness difference and details of the colloidal area are enhanced by grayscale dilation method.

[0094] Through the above method, the following effects can be achieved:

[0095] 1. Enhance the light-dark contrast of the colloid area: Grayscale expansion can make the bright part of the colloid area brighter and the dark part darker, thereby enhancing the light-dark contrast and making the details of the colloid area more prominent.

[0096] 2. Enhance the details and texture of the colloid area: Grayscale expansion can make the details of the colloid area clearer and richer, and make its texture more obvious, thereby improving the detail expression ability of the colloid area.

[0097] 3. Improve the visual effect of the colloid area: By enhancing the brightness difference and details of the colloid area, grayscale expansion can make the colloid area fuller, three-dimensional and layered, thereby improving the visual effect and appreciation of the colloid area.

[0098] In summary, improving the brightness difference and details of the colloid area through the grayscale expansion method can make the colloid area more prominent, clear and rich, thereby improving the quality and visual effect of the image.

[0099] In one embodiment, after improving the brightness difference and details of the colloid region by the grayscale expansion method, the method further includes:

[0100] The multiple discrete colloidal regions generated by the grayscale expansion method are merged into a continuous colloidal region through the region growing method.

[0101] Through the above method, the following effects can be achieved:

[0102] 1. Continuity: The merged colloid area will no longer be discrete, but a continuous area. This can make the colloid area more natural and real, and more in line with the actual situation.

[0103] 2. Smoothness: The region growing method can smooth multiple discrete colloid regions through certain rules and algorithms. This can reduce the jaggedness or discontinuity of the colloid region boundaries and make the merged region smoother.

[0104] 3. Accuracy: The region growing method can merge similar colloid regions together based on the characteristics and properties of the image. This can improve the precision and accuracy of the merged colloid regions, making the results more reliable.

[0105] 4. Efficiency: Through the region growing method, multiple discrete colloidal regions can be merged into a continuous region, thereby reducing the number of objects to be processed. This can improve the efficiency and speed of the algorithm and speed up the processing process.

[0106] In general, merging multiple discrete colloidal regions generated by the grayscale expansion method into a continuous colloidal region through the region growing method can achieve a more natural, smooth, precise and efficient effect.

[0107] In one embodiment, after merging multiple discrete colloid regions generated by the grayscale expansion method into a continuous colloid region by using a region growing method, the method further includes:

[0108] The edge contour of the continuous colloid area was confirmed by the window sliding method.

[0109] Through the above method, the following effects can be achieved:

[0110] 1. Determine the edge contour of the colloidal area: The window sliding method determines the edge contour of the colloidal area by sliding a fixed-size window on the image and calculating the average value or other characteristic value of the pixels in the window. This can be used to segment colloidal particles in the image and obtain their shape and position information.

[0111] 2. Analyze the size and shape of colloidal particles: After the edge contour of the continuous colloidal area is determined by the window sliding method, it can be used to calculate and analyze the size and shape of colloidal particles. For example, the diameter, area, perimeter and other parameters of the particles can be calculated, and then the properties and behavior of the particles can be studied.

[0112] 3. Image processing and computer vision applications: The window sliding method can be used as a technical means of image processing and computer vision to segment and extract target areas of interest.

[0113] In summary, confirming the edge contours of continuous colloidal regions through the window sliding method can realize the segmentation and shape analysis of colloidal particles, as well as its application in image processing and computer vision.

[0114] In one embodiment, after confirming the edge contour of the continuous colloid region by the window sliding method, the method further comprises:

[0115] When it is confirmed that the range of the colloid area exceeds the preset range of the dispensing area, it is confirmed that glue connection will occur.

[0116] Through the above method, the following effects can be achieved:

[0117] 1. Enhance the bonding strength between colloidal particles: Since there are connections between colloidal particles, their bonding strength will be enhanced.

[0118] 2. Improve the mechanical properties of materials: The connected colloidal particles form a continuous structure, which can increase the strength and stiffness of the material.

[0119] 3. Formation of specific structures and morphologies: The connected colloidal particles can form specific structures and morphologies, such as network, spindle or chain structures.

[0120] 4. Influence on fluid properties and flow behavior: The structure and morphology formed between the colloidal particles can affect the properties and flow behavior of the fluid.

[0121] In short, the bonding strength between colloidal particles can be enhanced, the mechanical properties of the material can be improved, a specific structure and morphology can be formed, and the fluid properties and flow behavior can be affected.

[0122] In one embodiment, the dispensing quality is evaluated based on the colloid characteristic data and the colloid thickness, including:

[0123] The fluid characteristic data and fluid thickness are compared with the preset thresholds in the workpiece drawing to evaluate the dispensing quality.

[0124] Through the above method, the following effects can be achieved:

[0125] 1. Glue dispensing quality assessment: By comparing the colloid characteristic data with the preset threshold, the dispensing quality can be assessed. If the colloid characteristic data and colloid thickness are within the preset threshold range, the dispensing quality can be considered good. On the contrary, if the colloid characteristic data or colloid thickness exceeds the preset threshold, it may indicate that there is a problem with the dispensing, which requires correction or re-dispensing.

[0126] 2. Determine whether the dispensing result meets the requirements: Compare the colloid feature data with the preset threshold to determine whether the dispensing result meets the requirements. According to the preset threshold in the workpiece drawing, it can be determined whether the size, shape or position of the colloid is within a reasonable range. If it meets the requirements, it means that the dispensing result meets the design requirements; if it does not meet the requirements, it may be necessary to adjust the dispensing process or re-dispense.

[0127] 3. Automated quality control: Automated quality control can be achieved by comparing the colloid feature data and colloid thickness with the preset threshold. By comparing these data with the threshold, it is possible to automatically determine whether the dispensing quality is qualified, thereby reducing the subjectivity and error of manual judgment. This can improve production efficiency and consistency and reduce the risk of quality problems.

[0128] In short, comparing the colloid feature data, colloid thickness and the preset threshold in the workpiece drawing can realize the dispensing quality assessment, determine whether the dispensing results meet the requirements, and automate quality control. These effects can improve the reliability and consistency of dispensing quality and reduce the generation of defective products.

[0129] In one embodiment, after acquiring the collected image of the dispensing area by a binocular structured light camera, the method further includes:

[0130] The point cloud data is preprocessed to eliminate noise data and redundant data.

[0131] Through the above method, the following effects can be achieved:

[0132] 1. Enhance the quality of point cloud data: The quality of point cloud data can be improved by eliminating noisy data. Noisy data may be caused by factors such as sensor noise, environmental interference, or data acquisition errors. Preprocessing can reduce or completely eliminate these noisy data through methods such as filtering, smoothing, or removing outliers, thereby improving the accuracy and reliability of point cloud data.

[0133] 2. Improve the stability of point cloud data: Preprocessing can improve the stability of point cloud data by removing redundant data. Redundant data may be caused by oversampling of the sensor or scanning at different angles. Through filtering, sampling or resampling, the redundant information in the point cloud data can be reduced, thereby improving the consistency and stability of the data.

[0134] 3. Reduce computing and storage overhead: Eliminating noise data and redundant data can reduce the computing and storage overhead of point cloud data. Noise data and redundant data will increase the volume and complexity of point cloud data, resulting in increased computing and storage requirements. Through preprocessing, the amount of data can be reduced and the data structure can be simplified, thereby reducing computing and storage overhead and improving processing efficiency.

[0135] 4. Improve the effect of subsequent data processing and analysis: Preprocessing can provide a better foundation for subsequent data processing and analysis. Eliminating noise and redundant data can reduce interference with subsequent algorithms and methods, provide more accurate and reliable data, and thus improve the effect of operations such as segmentation, registration, and recognition of point cloud data.

[0136] In summary, preprocessing point cloud data to eliminate noisy data and redundant data can enhance data quality, improve data stability, reduce computing and storage overhead, and improve the results of subsequent data processing and analysis.

[0137] The following is an example to explain the above content:

[0138] The grayscale dilation method compares the grayscale value of each pixel with the structural element, and sets the grayscale value of the pixel to the maximum grayscale value in its neighborhood. This operation can make the darker area "expand" and gradually brighten, thereby enhancing the brightness difference and details in the image.

[0139] The steps of grayscale expansion method are as follows:

[0140] (1) Define a structural element that has a specific shape and size.

[0141] (2) Overlap the structural element with each pixel of the image.

[0142] (3) Compare the grayscale value of the structural element with that of each pixel, and find the maximum grayscale value in its neighborhood.

[0143] (4) Set the grayscale value of the current pixel to the maximum grayscale value in the neighborhood.

[0144] (5) Repeat steps (2) to (4) until the entire image is traversed.

[0145] The basic idea of ​​the region growing method is to start from a seed point and gradually add neighboring pixels to an initial region until a certain stop condition is met. These neighboring pixels are selected based on some predefined pixel similarity criteria. For example, the grayscale value, color, texture or other features of the pixels can be used to determine whether they are similar.

[0146] The steps of region growing method are as follows:

[0147] (1) Select a seed point (starting point), which is usually selected by the user through interaction.

[0148] (2) Initialize an initial region and add seed points to the region.

[0149] (3) According to the similarity criterion, a neighborhood pixel is selected to be compared with the pixels in the current area.

[0150] (4) If the neighborhood pixel is similar to a pixel in the current region, the pixel is added to the current region and marked as visited.

[0151] (5) Repeat steps (3) to (4) until no more similar pixels can be added or the stopping condition is reached.

[0152] In steps (3), (4), and (5), the basic idea of ​​volume calculation is to segment the image using the smallest standard unit. The height value is fitted to the region with the mean value as the unit height. All unit volumes are accumulated and added together to finally obtain the volume of each region. The window sliding method uses a fixed-size window to slide on the image and capture the image blocks within the window. You can choose to slide the window with a fixed step size or overlap to cover the entire image. In this way, the image can be segmented into multiple fixed-size image blocks. The steps are as follows:

[0153] (1) Determine the size of the window: Set the unit to the minimum window unit of 1 square millimeter.

[0154] (2) Initialize the window position: Set the initial position of the window to the upper left corner of the colloid projection plane image.

[0155] (3) Capture the image block within the window: Based on the position of the window, capture the image block at the corresponding position from the original image.

[0156] (4) Move the window position: Move the window position according to the set step size or overlap.

[0157] (5) Repeat steps (3) to (5): Repeat steps (3) to (5) until the window slides to all parts of the image.

[0158] Therefore, by using the above-mentioned window sliding method, not only the edge area of ​​the colloid can be obtained, but also the colloid area can be obtained according to the number of window sliding times and the surface area of ​​the window.

[0159] The colloid volume calculated in the above way and the area of ​​the required silicone coating obtained from the workpiece drawing can be used to calculate the theoretical thickness of the area, thereby completing the evaluation of the colloid thickness in a quantitative way to avoid glue overflow / glue shortage (i.e. too much / too little colloid volume) in subsequent production links.

[0160] In addition, the glue ligation problem generated during the glue dispensing process described above can also be solved in the same way, that is, by performing grayscale expansion on the depth map and other methods to extract the depth part higher than the standard plane. If the obtained range exceeds the threshold of the framed ROI area, it can be determined that there is glue ligation in the part of the glue dispensing.

[0161] In the actual production process, some workpiece surfaces have inclined surfaces and other areas that need to be coated with silicone. For such areas, 2D cameras will have inaccurate area estimation when they lack angle and depth information. However, the dispensing measurement solution after 3D reconstruction using a 3D structured light camera can well extract the colloid from the inclined surface. In a similar way, the inclined surface can be projected or placed on a standard plane. At this time, the colloid volume calculation and measurement method can be used to calculate the colloid volume of the inclined surface.

[0162] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by adding the necessary general hardware platform with the help of software, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), including a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0163] In this embodiment, a dispensing quality assessment device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0164] Figure 5 is a structural block diagram of a dispensing quality evaluation device according to an embodiment of the present application, such as Figure 5 As shown, the device comprises:

[0165] An acquisition module 51 is used to acquire a collection image of the dispensing area through a binocular structured light camera;

[0166] A processing module 52, configured to obtain point cloud data based on the collected image; and

[0167] Acquiring colloid feature data based on point cloud data; and,

[0168] Calculating the thickness of the colloid after the colloid is flattened based on the colloid characteristic data; and,

[0169] The dispensing quality is evaluated based on the colloid characteristic data and colloid thickness.

[0170] Furthermore, the device also includes an output module 53 for outputting the evaluation result.

[0171] Furthermore, the colloid characteristic data includes: the total volume of the colloid, the area of ​​the colloid, the position of the colloid and the height of the colloid at each point, wherein the colloid volume, the area of ​​the colloid and the position of the colloid are the overall data of all single points in the dispensing area.

[0172] Furthermore, the device is also used to calculate the thickness of the colloid after the colloid is flattened based on the colloid characteristic data, including: calculating the thickness of the colloid after the colloid is flattened based on the total volume of the colloid and the area of ​​the glue dot region.

[0173] In an exemplary embodiment, the area of ​​the dispensing area can be known before processing, and the device can calculate the thickness of the colloid after the colloid is flattened by dividing the calculated total volume of the colloid by the area of ​​the dispensing area. The thickness of the colloid after the colloid is flattened is compared with a standard threshold value, which can be used as a reference factor for evaluating the dispensing quality.

[0174] In an exemplary embodiment, before preparing to flatten the colloid, a mold is generally placed in the glue dot area (for example, a square frame is used to surround the glue dot area, and the inner wall of the square frame is used to prevent the colloid from overflowing the glue dot area during the process of flattening the colloid). The device uses the total volume of the colloid divided by the area of ​​the glue dot area (or the area surrounded by the square frame) to obtain the thickness of the colloid after the colloid is flattened.

[0175] Furthermore, the device is also used to: convert point cloud data into a depth map to obtain depth information;

[0176] Get the colloid height at each point based on the depth information.

[0177] Furthermore, the device is also used to: convert the point cloud data into a depth map, and after obtaining the depth information, it also includes:

[0178] Convert the depth map into a grayscale image;

[0179] The colloid area was separated from the standard plane based on the grayscale image to determine the colloid area.

[0180] Furthermore, the device is also used to: separate the colloid area from the standard plane based on the grayscale image to determine the colloid area, and also includes: filling the colloid area with multiple standard squares of preset sizes to obtain multiple segmented areas.

[0181] Furthermore, the device is also used to: fill the colloid area with a plurality of standard squares of preset sizes to obtain a plurality of segmented areas, and further comprises:

[0182] Calculate the average height of the colloid in the segmented area based on the colloid height of each point in the segmented area;

[0183] The volume of the colloid in the segmented region is obtained based on the area and height of the standard square;

[0184] The total volume of the colloid is obtained based on the volume of the colloid. Further, the device can also be used to: after calculating the colloid area based on the standard area, it can also include:

[0185] Calculate the height mean based on the colloid height at each point;

[0186] The total volume of the colloid was obtained based on the average value of the colloid area and height.

[0187] Furthermore, the device is also used for: after converting the depth map into a grayscale map, further comprising:

[0188] The brightness difference and details of the colloidal area are enhanced by grayscale dilation method.

[0189] Furthermore, the device is also used to: after improving the brightness difference and details of the colloid area by the grayscale expansion method, it also includes:

[0190] The multiple discrete colloidal regions generated by the grayscale expansion method are merged into a continuous colloidal region through the region growing method.

[0191] Furthermore, the device is also used to: after merging multiple discrete colloid regions generated by the grayscale expansion method into a continuous colloid region by a region growing method, it also includes:

[0192] The edge contour of the continuous colloid area was confirmed by the window sliding method.

[0193] Furthermore, the device is also used for: after confirming the edge contour of the continuous colloid area by the window sliding method, it also includes:

[0194] When it is confirmed that the range of the colloid area exceeds the preset range of the dispensing area, it is confirmed that glue connection will occur.

[0195] Furthermore, the device is also used to: evaluate the dispensing quality based on the colloid characteristic data and the colloid thickness, including:

[0196] The fluid characteristic data and fluid thickness are compared with the preset thresholds in the workpiece drawing to evaluate the dispensing quality.

[0197] Furthermore, the device is also used for: after acquiring the collected image of the dispensing area through the binocular structured light camera, it also includes:

[0198] The point cloud data is preprocessed to eliminate noise data and redundant data.

[0199] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0200] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0201] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0202] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0203] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0204] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0205] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0206] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating dispensing quality, characterized in that: include: Acquire a collection image of the dispensing area; Acquire point cloud data based on the collected image; Acquire colloid characteristic data based on the point cloud data; Calculating the thickness of the colloid after the colloid is flattened based on the colloid characteristic data; The dispensing quality is evaluated based on the colloid characteristic data and the colloid thickness.

2. The method according to claim 1, characterized in that Acquiring colloid characteristic data based on the point cloud data includes: Convert the point cloud data into a depth map to obtain depth information; The height of the colloid at each point is obtained based on the depth information.

3. The method according to claim 2, characterized in that After converting the point cloud data into a depth map and obtaining the depth information, the method further includes: Converting the depth map into a grayscale image; The colloid region is separated from a standard plane based on the grayscale image to determine the colloid region.

4. The method according to claim 3, characterized in that After separating the colloid region from the standard plane based on the grayscale image to determine the colloid region, the method further includes: The colloid region is filled with a plurality of standard squares of a preset size to obtain a plurality of segmented regions.

5. The method according to claim 4, characterized in that After filling the colloid region with a plurality of standard squares of a preset size to obtain a plurality of segmented regions, the method further includes: Calculating the average height of the colloid in the segmented area based on the colloid height of each point in the segmented area; Obtaining the volume of the colloid in the segmented region based on the area of ​​the standard square and the height; The total volume of the colloid was obtained based on the colloid volume.

6. The method according to claim 3, characterized in that After converting the depth map into a grayscale map, the method further includes: The brightness difference and details of the colloidal area are enhanced by grayscale dilation method.

7. The method according to claim 6, characterized in that After improving the brightness difference and details of the colloidal area by the grayscale expansion method, it also includes: The plurality of discrete colloidal regions generated by the grayscale expansion method are merged into a continuous colloidal region by a region growing method.

8. The method according to claim 7, characterized in that After merging the plurality of discrete colloid regions generated by the grayscale expansion method into a continuous colloid region by a region growing method, the method further includes: The edge contour of the continuous colloid region was confirmed by the window sliding method.

9. The method according to claim 8, characterized in that After confirming the edge contour of the continuous colloid region by the window sliding method, the method further includes: When it is confirmed that the range of the colloid area exceeds the preset range of the glue dispensing area, it is confirmed that glue connection will occur.

10. The method according to claim 5, characterized in that Calculating the thickness of the colloid after the colloid is flattened based on the colloid characteristic data, including: The thickness of the colloid after the colloid is flattened is calculated based on the total volume of the colloid and the area of ​​the glue-dispensing region.

11. The method according to claim 1, characterized in that: Based on the colloid characteristic data and the colloid thickness, the dispensing quality is evaluated, including: The colloid characteristic data, the colloid thickness and the preset threshold value in the workpiece drawing are compared to evaluate the dispensing quality.

12. The method according to claim 1, characterized in that After acquiring the acquisition image of the dispensing area, it also includes: The point cloud data is preprocessed to eliminate noise data and redundant data.

13. A dispensing quality assessment device, characterized in that: include: An acquisition module, used for acquiring a collection image of a dispensing area; A processing module, used for acquiring point cloud data based on the collected image; as well as, Acquire colloid characteristic data based on the point cloud data; as well as, Calculating the thickness of the colloid after the colloid is flattened based on the colloid characteristic data; and, The dispensing quality is evaluated based on the colloid characteristic data and the colloid thickness.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 12 when executed by a processor.

15. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 12 are implemented.

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