High-precision cutting and positioning method and system for patch gum based on machine vision

Abstract

The invention relates to the technical field of machine vision, and discloses a patch gum high-precision cutting and positioning method and system based on machine vision, and the method comprises the steps: collecting a patch gum surface image, and carrying out the global edge division and local center division of the patch gum surface image, performing detail scaling operation on the patch gum surface image after global edge division and local center division, and fusing the global gum surface image and the local gum surface image to obtain a patch gum image; identifying three-dimensional information of the patch gum image, and determining a cutting surface of the patch gum image according to the three-dimensional information; identifying a bidirectional cutting direction corresponding to the cutting surface, and generating a bidirectional cutting path of the patch gum image according to the bidirectional cutting direction and the cutting surface; and generating a cutting connection diagram of the patch gum image according to the bidirectional cutting path, and positioning a cutting position of the patch gum image through the cutting connection diagram. According to the invention, the cutting and positioning accuracy of the patch gum can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of machine vision, and particularly to a high-precision cutting and positioning method and system for patch back glue based on machine vision. Background Art

[0002] With the continuous development of electronic products towards miniaturization, lightness, and high performance, the size of electronic components is constantly shrinking. As a key material connecting electronic components and circuit boards, the cutting accuracy of patch back glue directly affects the accuracy and stability of component installation. Only by achieving high-precision cutting and positioning of patch back glue can a large number of tiny components be accurately placed in a limited space to ensure the stable operation of equipment performance.

[0003] The existing cutting and positioning technology for patch back glue is to obtain the surface information of the back glue based on image acquisition technology. In practical applications, traditional image acquisition may have a perspective limitation and cannot comprehensively obtain the surface information of the back glue, resulting in inaccurate positioning. In addition, the processing of the glue image is not fine enough, and it is difficult to clearly distinguish the edges and details, affecting the positioning accuracy, so the accuracy of cutting and positioning the patch back glue is relatively low. Summary of the Invention

[0004] The present invention provides a high-precision cutting and positioning method and system for patch back glue based on machine vision, and its main purpose is to solve the problem of relatively low accuracy in cutting and positioning patch back glue.

[0005] To achieve the above object, a high-precision cutting and positioning method for patch back glue based on machine vision provided by the present invention includes: Collecting the surface image of the patch back glue from all-round angles based on a preset image acquisition, performing global edge division on the surface image of the patch back glue, and performing a global detail scaling operation on the surface image of the patch back glue after global edge division to obtain a global back glue surface image; Performing local center division on the global back glue surface image, performing a local detail scaling operation on the global back glue surface image after local center division to obtain a local back glue surface image, and fusing the global back glue surface image and the local back glue surface image to obtain a patch back glue image; Identifying the three-dimensional information of the patch back glue image by using a preset dual-vision positioning algorithm, and determining the cutting plane of the patch back glue image according to the three-dimensional information; Identifying the two-way cutting direction corresponding to the cutting plane, and generating a two-way cutting path of the patch back glue image according to the two-way cutting direction and the cutting plane; Generating a cutting connection diagram of the patch back glue image according to the two-way cutting path, and positioning the cutting position of the patch back glue image through the cutting connection diagram.

[0006] Optionally, the method for collecting the image of the surface of the patch adhesive tape based on a preset all-round angle of image collection includes: Extracting the horizontal direction and the vertical direction corresponding to the all-round angle of image collection; Dividing the all-round angle of image collection into horizontal all-round angles at preset horizontal angle intervals in the horizontal direction; Dividing the all-round angle of image collection into vertical all-round angles at preset vertical angle intervals in the vertical direction; Configuring all-round image collection points according to the horizontal all-round angles and the vertical all-round angles, and collecting the image of the surface of the patch adhesive tape by using the all-round image collection points.

[0007] Optionally, the method for globally dividing the edge of the image of the surface of the patch adhesive tape includes: Converting the image of the surface of the patch adhesive tape into a grayscale image of the adhesive tape surface; Identifying the pixel points of the grayscale image of the adhesive tape surface, and generating the edge intensity distribution of the grayscale image of the adhesive tape surface according to the pixel points; Identifying edge points and non-edge points by using the edge intensity distribution and a preset edge threshold; Determining the global edge of the image of the surface of the patch adhesive tape according to the edge points and the non-edge points.

[0008] Optionally, the method for performing a global detail scaling operation on the image of the surface of the patch adhesive tape after global edge division to obtain a global image of the adhesive tape surface includes: Extracting the global edge corresponding to the image of the surface of the patch adhesive tape after global edge division; Identifying the initial edge points of the global edge, and calculating the dynamic edge point coefficient of the global edge according to the initial edge points: Wherein, is the dynamic edge point coefficient, is the grayscale change weight, is the edge change weight, is the initial edge point is the number of pixel points in the neighborhood of, is the pixel point in the neighborhood is the grayscale value of, is the average grayscale of the pixel points in the neighborhood, is the sine function, is the arctangent function, is the target candidate edge point; Determining the global edge points of the global edge according to the dynamic edge point coefficient; Perform local magnification operations on each of the global edge points, identify abnormal images of the patch adhesive surface image based on the magnified global edge points, perform multi-dimensional enhancement processing on the abnormal images, and obtain edge surface images corresponding to each global edge point; Perform shrinkage processing on the edge surface images, identify the edge intensity distribution of the edge images after shrinkage processing, and perform multi-dimensional enhancement processing on the edge images according to the edge intensity distribution to obtain the global adhesive surface image.

[0009] Optionally, the local center division of the global adhesive surface image includes: Determine an initial local division window according to the image shape of the global adhesive surface image; Determine the division area lines of the global adhesive surface image according to the initial local division window; Identify the two-way dynamic window factor of the initial local division window through the division area lines; Dynamically update the initial local division window using the two-way dynamic window factor, and return to the step of determining the division area lines of the global adhesive surface image according to the initial local division window until the global adhesive surface image is completely divided; When the global adhesive surface image is completely divided, determine the local areas of the global adhesive surface image according to each division area line.

[0010] Optionally, the step of fusing the global adhesive surface image and the local adhesive surface image to obtain the patch adhesive image includes: Identify the local edge features of the local adhesive surface image and the global edge features of the global adhesive surface image; Compare the feature positions of the local edge features with the feature positions of the global edge features to obtain the same feature positions; Fuse the local edge features and the global edge features according to the same feature positions to obtain the patch adhesive image.

[0011] Optionally, in the embodiments of the present invention, the step of identifying the three-dimensional information of the patch adhesive image using a preset dual visual positioning algorithm includes: Identify the shape complexity index and the feature stability index of the patch adhesive image; Convert the world coordinates of the patch adhesive image into image plane coordinates; Determine a first weight and a second weight according to the shape complexity index and the feature stability index, where the first weight and the second weight are: Where, is the first weight, is the second weight, is the shape complexity index, is the stability index of machine vision features, is the stability index of laser sensor features; Use the following dual vision positioning algorithm to perform three-dimensional fusion on the image plane coordinates and the pre-acquired image measurement height to obtain the three-dimensional information of the patch adhesive image: Among them, is the abscissa information in the three-dimensional information, is the ordinate information in the three-dimensional information, is the vertical coordinate information in the three-dimensional information, is the first weight, is the second weight, is the plane abscissa of the patch adhesive target point in the world coordinate system, is the plane ordinate of the patch adhesive target point in the world coordinate system, is the height coordinate of the patch adhesive target point, is the projected abscissa of the laser measurement point on the plane, is the projected ordinate of the laser measurement point on the plane, is the target height corresponding to the laser measurement point.

[0012] Optionally, generating the two-way cutting path of the patch adhesive image according to the two-way cutting direction and the cutting plane includes: Optionally select any one of the cutting planes as the initial cutting plane, and gradually move according to the two-way cutting direction from any endpoint in the initial cutting plane to obtain an initial cutting path; Extend the initial cutting path according to the first cutting direction in the two-way cutting direction and the cutting sequence of the cutting plane to obtain a first cutting path; Extend the initial cutting path according to the second cutting direction in the two-way cutting direction and the cutting sequence to obtain a second cutting path; Generate the two-way cutting path of the patch adhesive image according to the first cutting path and the second cutting path.

[0013] Optionally, generating the cutting connection diagram of the patch adhesive image according to the two-way cutting path includes: Discretize the two-way cutting path to obtain path discrete points; Identify the intersection points in the path discrete points, and determine the connection relationship between the path discrete points according to the positions corresponding to the intersection points; Connect the path discrete points according to the connection relationship to obtain a cutting connection diagram.

[0014] To solve the above problems, the present invention also provides a high-precision cutting and positioning system for patch back glue based on machine vision. The system includes: An image division module, configured to collect the surface image of the patch back glue at an all-round angle based on a preset image acquisition, perform global edge division on the surface image of the patch back glue, and perform a global detail scaling operation on the surface image of the patch back glue after global edge division to obtain a global back glue surface image; An image fusion module, configured to perform local center division on the global back glue surface image, perform a local detail scaling operation on the global back glue surface image after local center division to obtain a local back glue surface image, and fuse the global back glue surface image and the local back glue surface image to obtain a patch back glue image; A cutting plane determination module, configured to identify the three-dimensional information of the patch back glue image by using a preset dual vision positioning algorithm, and determine the cutting plane of the patch back glue image according to the three-dimensional information; A two-way cutting path generation module, configured to identify the two-way cutting direction corresponding to the cutting plane, and generate a two-way cutting path of the patch back glue image according to the two-way cutting direction and the cutting plane; A cutting position positioning module, configured to generate a cutting connection diagram of the patch back glue image according to the two-way cutting path, and position the cutting position of the patch back glue image through the cutting connection diagram.

[0015] In the embodiment of the present invention, the complete surface information of the patch back glue can be obtained by collecting images all-round. The global edge division and detail scaling make the overall features of the patch back glue clearer, which is beneficial to grasping the overall contour and edge details; the local center division and detail scaling focus on the key areas and highlight the subtle features; fusing the global and local back glue surface images combines the advantages of the whole and the part, and the generated patch back glue image has richer and more accurate information; the dual vision positioning algorithm identifies the three-dimensional information, can accurately determine the cutting plane, and significantly improves the cutting accuracy; identifying the two-way cutting direction and generating the path optimizes the cutting process, making the cutting more efficient and reasonable; the generated cutting connection diagram can visually and clearly present the cutting position. Therefore, the high-precision cutting and positioning method and system for patch back glue based on machine vision proposed by the present invention can solve the problem of low accuracy in cutting and positioning the patch back glue. Description of the Drawings

[0016] Figure 1 It is a schematic flow chart of a high-precision cutting and positioning method for patch back glue based on machine vision provided by an embodiment of the present invention; Figure 2 It is a functional module diagram of a high-precision cutting and positioning system for patch back glue based on machine vision provided by an embodiment of the present invention.

[0017] The implementation, functional features, and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] The embodiment of the present application provides a high-precision cutting and positioning method for patch back glue based on machine vision. The execution subject of the high-precision cutting and positioning method for patch back glue based on machine vision includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the high-precision cutting and positioning method for patch back glue based on machine vision can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0020] Referring to Figure 1 As shown, it is a flowchart of a high-precision cutting and positioning method for patch back glue based on machine vision provided by an embodiment of the present invention. In this embodiment, the high-precision cutting and positioning method for patch back glue based on machine vision includes: S1. Collect the surface image of the patch back glue based on a preset all-round angle of image acquisition, perform global edge division on the surface image of the patch back glue, and perform a global detail scaling operation on the surface image of the patch back glue after global edge division to obtain a global back glue surface image.

[0021] In the embodiment of the present invention, the surface image of the patch back glue refers to a two-dimensional image obtained by photographing the surface of the patch back glue through an image acquisition device (such as a camera).

[0022] In the embodiment of the present invention, the collection of the surface image of the patch back glue based on a preset all-round angle of image acquisition includes: Extract the horizontal direction and the vertical direction corresponding to the all-round angle of image acquisition; Divide the all-round angle of image acquisition into horizontal all-round angles according to a preset horizontal angle interval in the horizontal direction; Divide the all-round angle of image acquisition into vertical all-round angles according to a preset vertical angle interval in the vertical direction; Configure the omnidirectional image acquisition points according to the horizontal omnidirectional angle and the vertical omnidirectional angle, and use the omnidirectional image acquisition points to acquire the surface image of the patch adhesive.

[0023] Specifically, when performing image acquisition, based on a pre-determined reference coordinate system, the horizontal direction and the vertical direction are selected as two basic dimensions. For example, the horizontal direction of the camera's shooting plane is defined as the horizontal direction, and the vertical direction is defined as the vertical direction. The preset horizontal angle interval is a custom value used to determine the step size of the angle change when acquiring images in the horizontal direction. For example, the preset horizontal angle interval is 10 degrees. In the horizontal direction, starting from a starting angle (assumed to be 0 degrees), the angles are divided at intervals of 0 degrees in sequence to obtain the horizontal omnidirectional angle. Similar to the horizontal direction, in the vertical direction, it is also divided according to the preset vertical angle interval to obtain the vertical omnidirectional angle.

[0024] Specifically, the horizontal omnidirectional angle and the vertical omnidirectional angle together constitute the omnidirectional acquisition angle range, and the omnidirectional image acquisition points are determined by the combination of the horizontal omnidirectional angle and the vertical omnidirectional angle. For example, when the horizontal angle is 30 degrees and the vertical angle is 45 degrees, a specific acquisition point position is determined. By combining all the horizontal omnidirectional angles and vertical omnidirectional angles, the omnidirectional image acquisition points are obtained. Then, place the camera or other image acquisition devices at the positions of the acquisition points and take pictures according to the corresponding angles to be able to acquire the images of the patch adhesive surface at different angles.

[0025] Furthermore, in order to improve the image quality of the patch adhesive surface image and provide a high-quality image basis for positioning, it is necessary to analyze the edge image of the patch adhesive surface image.

[0026] In the embodiment of the present invention, global edge division refers to processing the edges of the entire patch adhesive surface image, extracting the edge information in the image, and clearly and accurately representing the overall boundary of the patch adhesive.

[0027] In the embodiment of the present invention, the global edge division of the patch adhesive surface image includes: Convert the patch adhesive surface image into a back adhesive surface grayscale image; Identify the pixel points of the back adhesive surface grayscale image, and generate the edge intensity distribution of the back adhesive surface grayscale image according to the pixel points; Use the edge intensity distribution and a preset edge threshold to identify edge points and non-edge points; Determine the global edge of the patch adhesive surface image according to the edge points and the non-edge points.

[0028] Specifically, the surface image of the patch adhesive is converted into a grayscale image, that is, the color information of each pixel point in the color image is represented by a grayscale value, which can simplify the image data while retaining useful information such as the brightness and contrast of the image for edge detection, that is, the RGB value is converted into a grayscale value to obtain the grayscale image of the adhesive surface; in the grayscale image, each pixel point has a corresponding grayscale value, and the edge is the area where the grayscale value changes greatly in the image. By calculating the change of the grayscale value of each pixel point and its neighboring pixel points, the edge intensity of the pixel point is obtained. For example, the Sobel operator calculates the gradients in the horizontal and vertical directions respectively by performing a convolution operation on the image, and then calculates the total edge intensity according to the Pythagorean theorem. For each pixel point in the grayscale image of the adhesive surface, a value representing its edge intensity can be obtained, thus forming an edge intensity distribution.

[0029] Specifically, the preset edge threshold is a self-defined value used to distinguish edge points from non-edge points. The edge intensity of each pixel point is compared with the edge threshold. If the edge intensity is greater than or equal to the edge threshold, the pixel point is considered an edge point; if the edge intensity is less than the edge threshold, the pixel point is determined to be a non-edge point. Thus, all pixel points in the grayscale image of the adhesive surface are divided into two categories: edge points and non-edge points. After identifying the edge points and non-edge points, all the edge points are connected to obtain the global edge of the surface image of the patch adhesive. For example, the edge tracking algorithm starts from an edge point and searches for adjacent edge points according to certain rules, gradually connecting all the edge points into a complete edge contour. The global edge represents the overall boundary information of the patch adhesive in the image.

[0030] Furthermore, although the global edge division can determine the overall boundary of the patch adhesive, it may ignore some subtle features and details. Therefore, it is necessary to perform global detail scaling operations on the details to improve the image quality.

[0031] In the embodiment of the present invention, the global adhesive surface image refers to the image obtained after processing the surface image of the patch adhesive after global edge division, which more clearly shows the surface features of the patch adhesive, especially the details of the edge part, as well as information such as the abnormal areas highlighted after processing.

[0032] In the embodiment of the present invention, performing global detail scaling operations on the surface image of the patch adhesive after global edge division to obtain the global adhesive surface image includes: Extracting the global edge corresponding to the surface image of the patch adhesive after global edge division; Identifying the initial edge points of the global edge and calculating the dynamic edge point coefficient of the global edge according to the initial edge points: Wherein, is the dynamic edge point coefficient, is the grayscale change weight, is the edge change weight, is the initial edge point The number of pixel points in the neighborhood, is the pixel point in the neighborhood The grayscale value of, is the average grayscale of the pixel points in the neighborhood, is the sine function, is the arctangent function, is the target candidate edge point; Determine the global edge points of the global edge according to the dynamic edge point coefficient; Perform local magnification operations on the global edge points one by one, identify abnormal images on the surface image of the patch adhesive based on the magnified global edge points, and perform multi-dimensional enhancement processing on the abnormal images to obtain the edge surface images corresponding to each global edge point; Perform shrinkage processing on the edge surface images, identify the edge intensity distribution of the edge images after shrinkage processing, and perform multi-dimensional enhancement processing on the edge images according to the edge intensity distribution to obtain the global adhesive surface image.

[0033] Specifically, the edge contour obtained by the edge detection algorithm is separated from the original image to form an image representation containing only the edge. On the extracted global edge, some starting edge points are determined as the basis for calculation. The initial edge point can be any point on the edge contour, so as to determine the dynamic edge point coefficient. The dynamic edge point coefficient refers to the dynamic interval between two edge points. Let the initial edge point be , and the next candidate edge point be . In the local neighborhood centered on (for example neighborhood), calculate the grayscale variance . The calculation formula of the grayscale variance is: , where is the grayscale value of the point in the neighborhood, is the average grayscale in the neighborhood, is the number of pixel points in the neighborhood. At the same time, calculate and The direction angle of the line connecting the two points . Let the vector , then (the quadrant situation needs to be considered), and and are the weight coefficients , the gray variance reflects the degree of gray change in the local area. The greater the degree of change may mean that the edge is more complex and requires a smaller interval to accurately describe; while | reflects the edge orientation. When the edge orientation is significantly different from the horizontal or vertical direction, the interval is adjusted appropriately to adapt to the edge change.

[0034] Specifically, according to the calculated dynamic edge point coefficients of each initial edge point, a certain threshold or rule is set to screen out the points with larger dynamic edge point coefficients or meeting specific conditions as global edge points. The global edge points are considered to have more important features on the edge or better represent the edge morphology; and for each determined global edge point, the local area around it is magnified with this point as the center, which is achieved through an image scaling algorithm, magnifying the image of the local area to an appropriate ratio to observe details more clearly; in the magnified local image, through image analysis algorithms (such as threshold segmentation, feature matching, etc.), the areas different from the normal situation are identified, that is, abnormal images, such as discontinuous edges detected, the appearance of extra spots or texture abnormalities, etc.; for the identified abnormal images, enhancement processing is performed from multiple dimensions (such as gray level, contrast, texture, etc.), and methods such as histogram equalization and filtering can be used to improve the visibility of the abnormal area and highlight its features, and finally the edge surface image corresponding to each global edge point is obtained; the enhanced edge surface image is subjected to an appropriate shrinking operation, that is, through downsampling or other image compression algorithms, to reduce the size of the image while retaining the main edge features; calculate the edge intensity distribution of the shrunk image, that is, determine the edge intensity magnitudes at different positions in the image, and then according to the edge intensity distribution, enhance the edge image from multiple dimensions again to highlight the edge features and obtain the global adhesive backing surface image.

[0035] Furthermore, although the global adhesive backing surface image has undergone a series of processes, some local detail features may be overlooked in the overall observation. Therefore, it is necessary to enhance the image quality inside the global adhesive backing surface image to more comprehensively understand the condition of the adhesive backing surface.

[0036] S2. Perform local center division on the global adhesive backing surface image, perform local detail scaling operation on the globally adhesive backing surface image after local center division to obtain a local adhesive backing surface image, and fuse the global adhesive backing surface image and the local adhesive backing surface image to obtain a patch adhesive backing image.

[0037] In the embodiment of the present invention, the local center division refers to dividing the global adhesive backing surface image into multiple smaller local areas, and the local center division helps to highlight the local features of the image.

[0038] In the embodiment of the present invention, the performing local center division on the global adhesive backing surface image includes: Determine an initial local division window according to the image shape of the global adhesive surface image; Determine the division area lines of the global adhesive surface image according to the initial local division window; Identify the two-way dynamic window factor of the initial local division window through the division area lines; Dynamically update the initial local division window using the two-way dynamic window factor, and return to the step of determining the division area lines of the global adhesive surface image according to the initial local division window until the entire global adhesive surface image is divided; When the entire global adhesive surface image is divided, determine the local areas of the global adhesive surface image according to each division area line.

[0039] Specifically, the global adhesive surface image may have different shapes, such as rectangles, irregular polygons, etc. An initial local division window is determined according to its shape, and the size and shape of the window should be able to reasonably cover a part of the image area. If the image is rectangular, a smaller rectangular window can be selected as the initial division window. After determining the initial local division window, the division area lines are determined based on the initial division window. The division area lines are the boundary lines that divide the image into different local areas, and the division area lines can be determined according to the gray-scale changes or edge information of the image within the window, so that the divided areas can better reflect the local characteristics of the image.

[0040] Specifically, the two-way dynamic window factor is a parameter used to adjust the initial local division window, including adjustments in the horizontal and vertical directions of the window, such as changes in the window size, position movement, etc. If it is found that the image features near the division area lines are relatively complex, it may be necessary to increase the size of the window. Then the two-way dynamic window factor will correspondingly adjust the dimensions of the window in the horizontal and vertical directions. Furthermore, based on the calculated two-way dynamic window factor, the initial local division window is updated, causing it to change in size, position, or shape. Then, based on the updated window, the division area lines are determined again. Next, the two-way dynamic window factor is calculated through the new division area lines, and so on in a loop. This process continues until the entire global adhesive surface image is divided into multiple local areas, that is, until the state of the image being completely divided is reached. When the image division is completed, the area enclosed by each division area line is a local area. The local area is a subdivision of the global adhesive surface image, and each area can be analyzed and processed independently.

[0041] Furthermore, after the local center division, each local area has its corresponding position and range information. Based on the position and range information, an appropriate scaling ratio is determined. If very fine details need to be observed, a larger scaling ratio can be selected, such as 2 times, 5 times or even higher. If only a general view of the overall situation of the local area is desired, a smaller scaling ratio (such as 1.5 times) is used. Then, the corresponding local area image is extracted from the global adhesive surface image, and the local area is separated from the original image using image cropping. The scaled local image is placed back in the position of the original image, and then an image fusion operation is performed. The scaled local image and the other parts of the original image are recombined together using image stitching. During the scaling process, it is possible that the local image exceeds the boundary of the original image. In this case, the excess part needs to be filled to avoid blank areas. The filling methods include but are not limited to zero filling (filling the excess part with black) and mirror filling (mirror copying the boundary pixels), so as to obtain the local adhesive surface image after the local detail scaling operation, which can more clearly display the detail features of the local area.

[0042] Furthermore, when analyzing the patch adhesive, relying solely on the global image may overlook some important local features, while focusing only on the local image may lose the overall situation. By fusing the global and local images, local details can be observed in the overall context, avoiding misjudgments caused by observing the local area in isolation.

[0043] In the embodiments of the present invention, the patch adhesive image refers to the image obtained by processing and fusing the global adhesive surface image and the local adhesive surface image, which contains both the macroscopic information of the overall patch adhesive, such as the overall shape and contour, and the microscopic detail information of the local area.

[0044] In the embodiments of the present invention, fusing the global adhesive surface image and the local adhesive surface image to obtain the patch adhesive image includes: Identifying the local edge features of the local adhesive surface image and the global edge features of the global adhesive surface image; Comparing the feature positions of the local edge features with the feature positions of the global edge features to obtain the same feature positions; Performing feature fusion on the local edge features and the global edge features according to the same feature positions to obtain the patch adhesive image.

[0045] Specifically, edge detection algorithms (such as the Canny algorithm, Sobel operator, etc.) are used to identify its edge information. The edge information reflects features such as the boundaries and shape changes of the adhesive surface in the local area. For the global adhesive surface image, the corresponding edge detection algorithm is used to extract its overall edge information, that is, the global edge features. For example, in the local adhesive surface image, the edges of some tiny cracks or texture changes may be detected, while in the global adhesive surface image, the overall contour edge of the adhesive can be identified.

[0046] Specifically, by comparing the position information of the local edge features and the global edge features, feature points or feature regions with the same or similar positions in the local image and the global image are screened. These positions are called the same feature positions. For example, a feature point at a corner of the adhesive edge in the local image can also find the corresponding position in the global image, that is, the same feature position, so as to ensure the accuracy of fusion. According to the same feature positions determined above, the local edge features and the global edge features are merged or fused. During the fusion process, for the edge pixels at the same position, the fused pixel value can be determined according to the fusion rule (such as taking the average value, selecting the clearer edge, etc.), so as to organically combine the local detailed features with the global overall features and obtain an image that combines local and global information, that is, the patch adhesive image.

[0047] Furthermore, since the surface of the patch adhesive may be uneven, and for adhesives with undulations and uneven thickness on the surface, in order to achieve more accurate cutting positioning, effectively avoid two-dimensional positioning deviation caused by the unevenness of the adhesive surface, and improve the cutting accuracy, it is necessary to identify the three-dimensional information of the patch adhesive surface.

[0048] S3. Use a preset dual-vision positioning algorithm to identify the three-dimensional information of the patch adhesive image, and determine the cutting plane of the patch adhesive image according to the three-dimensional information.

[0049] In the embodiment of the present invention, the three-dimensional information refers to the information describing the position of the target point of the patch adhesive in the three-dimensional space. Determining the specific position of the target point in the three-dimensional space can comprehensively and accurately reflect the spatial shape and position characteristics of the patch adhesive.

[0050] In the embodiment of the present invention, the use of a preset dual-vision positioning algorithm to identify the three-dimensional information of the patch adhesive image includes: Identifying the shape complexity index and feature stability index of the patch adhesive image; Converting the world coordinates of the patch adhesive image into image plane coordinates; Determining a first weight and a second weight according to the shape complexity index and the feature stability index, where the first weight and the second weight are: Where is the first weight, is the second weight, is the shape complexity index, is the stability index of machine vision features, is the stability index of laser sensor features; Use the following dual vision positioning algorithm to perform three-dimensional fusion on the image plane coordinates and the pre-acquired image measurement height to obtain the three-dimensional information of the patch adhesive image: Among them, is the abscissa information in the three-dimensional information, is the ordinate information in the three-dimensional information, is the vertical coordinate information in the three-dimensional information, is the first weight, is the second weight, is the plane abscissa of the patch adhesive target point in the world coordinate system, is the plane ordinate of the patch adhesive target point in the world coordinate system, is the height coordinate of the patch adhesive target point, is the projection abscissa of the laser measurement point on the plane, is the projection ordinate of the laser measurement point on the plane, is the target height corresponding to the laser measurement point.

[0051] Specifically, for an image region, the shape complexity is measured by calculating the fractal dimension of its contour, and the fractal dimension can be calculated using the box-counting method. Assume that the contour of the image region is covered with small boxes of side length , and the number of required small boxes is , then the fractal dimension can be approximately calculated by the following formula: , the higher the shape complexity, the more the fractal dimension deviates from an integer. For example, for a complex irregular adhesive shape, its fractal dimension may be between 1.5 and 2, while for a regular shape it is close to 1 (such as a straight line) or 2 (such as a square). Among them, the machine vision system is responsible for obtaining the plane features and position information of the patch adhesive, and the laser displacement sensor accurately measures the three-dimensional information such as the height of the adhesive. For the plane features extracted by the machine vision, the feature stability can be measured by calculating the matching consistency of feature points. Assume that feature extraction is performed on the same region at different times or from different perspectives, and the feature point sets and are obtained. The number of matching feature point pairs is , and the total number of feature points is , then the feature matching consistency is , the higher the consistency, the more stable the feature; for the height data measured by the laser displacement sensor, the stability can be measured by calculating the standard deviation of multiple measurements Let's assume that for a certain point times of height measurements are taken, and the measured values are . Then calculate the standard deviation corresponding to the measured values. The smaller the standard deviation, the more stable the height measurement. Based on the shape complexity and feature stability (machine vision features), (laser displacement sensor features) to adjust the weight coefficient. When the shape complexity is low (the shape is relatively regular) and the stability of machine vision features is high, is large, that is, increase the weight of the machine vision measurement data; when the shape complexity is high and the stability of the laser displacement sensor features is high, is large, that is, increase the weight of the laser displacement sensor measurement data.

[0052] Specifically, in the machine vision system, it is usually necessary to convert the three-dimensional coordinates of an object in the world coordinate system into two-dimensional coordinates on the image plane. Then, based on the imaging model of the camera, such as the pinhole camera model, through the internal parameters of the camera (focal length , image center coordinates ) and external parameters (rotation matrix and translation vector ) to achieve this. The rotation matrix of the camera is a 3×3 matrix that describes the rotation relationship of the camera coordinate system relative to the world coordinate system. The translation vector of the camera is a 3×1 vector that represents the position of the origin of the camera coordinate system in the world coordinate system. is the coordinate estimated based on the projection of the laser measurement point on the plane and participates in the fusion as auxiliary information. is the height estimated based on the machine vision image features and is also used as auxiliary information for fusion.

[0053] Furthermore, according to the three-dimensional information, determine the cutting plane of the patch adhesive image. That is, the known three-dimensional information is composed of the abscissa , ordinate and vertical coordinate . Together, they determine the positions of each point on the patch adhesive in three-dimensional space, thus being able to comprehensively reflect the spatial shape of the patch adhesive. Then, take the plane determined by each vertical coordinate in the three-dimensional coordinates as a cutting plane. On this plane, the abscissa and ordinate can take various values, while the vertical coordinate always remains , so as to determine the shape and characteristics of the patch adhesive at different heights.

[0054] Furthermore, the cutting plane is a plane fixed by the vertical coordinate. On this plane, the abscissa and the ordinate are two independent and orthogonal dimensions that can fully describe the position of any point on the plane. Therefore, by cutting along these two directions, various shapes and sizes of objects on the plane can be flexibly cut to meet different cutting requirements.

[0055] S4. Identify the two-way cutting direction corresponding to the cutting plane, and generate a two-way cutting path for the patch adhesive image according to the two-way cutting direction and the cutting plane.

[0056] In the embodiment of the present invention, the two-way cutting direction refers to cutting along the two mutually perpendicular directions of the abscissa and the ordinate in the cutting plane. Among them, since in the cutting plane (the plane with the vertical coordinate fixed as ), the abscissa can take various values, so cutting can be performed along the abscissa direction. The direction from left to right or from right to left is a cutting direction. On this direction, the starting point, ending point, and path of cutting can be determined as needed to achieve the cutting of the patch adhesive in this direction. For example, the patch adhesive can be cut into parts with different widths in this direction; similarly, on this cutting plane, the ordinate can also take different values, so cutting can also be performed along the ordinate direction. It can be the direction from top to bottom or from bottom to top. Through the cutting in this direction, the patch adhesive can be segmented or trimmed in this dimension. For example, the patch adhesive can be cut along the ordinate direction according to specific length requirements.

[0057] Furthermore, the two-way cutting directions are perpendicular to each other, which can provide more accurate positioning and control. In order to reduce the idle stroke and repeated cutting during the cutting process and improve the cutting efficiency, it is necessary to reasonably plan the two-way cutting path.

[0058] In the embodiment of the present invention, the two-way cutting path refers to a path set formed by combining the cutting paths generated along two mutually perpendicular cutting directions in the cutting plane, which clarifies the specific routes and sequences of cutting the patch adhesive in the two directions, enabling the cutting operation to comprehensively and accurately cover the area to be cut, thereby achieving the effective cutting of the patch adhesive.

[0059] In the embodiment of the present invention, the generating the two-way cutting path of the patch adhesive image according to the two-way cutting direction and the cutting plane includes: Arbitrarily select the cutting plane as the initial cutting plane, and gradually move from any endpoint in the initial cutting plane according to the two-way cutting direction to obtain an initial cutting path; Extend the initial cutting path according to the first cutting direction in the bidirectional cutting direction and the cutting sequence of the cutting plane to obtain a first cutting path; Extend the initial cutting path according to the second cutting direction in the bidirectional cutting direction and the cutting sequence to obtain a second cutting path; Generate a bidirectional cutting path for the patch adhesive image based on the first cutting path and the second cutting path.

[0060] Specifically, arbitrarily select one of all the cutting planes determined by the vertical coordinate as the starting cutting plane. Based on the bidirectional cutting direction (the horizontal coordinate direction and the vertical coordinate direction), start from any endpoint (such as the upper left corner, the lower right corner, etc.) of this initial cutting plane, move step by step according to the bidirectional cutting direction, moving a small unit distance each time (for example, the distance of one pixel point), and connect the points passed through in sequence to form an initial cutting path. Then the initial cutting path is the starting point for extending and perfecting the cutting path.

[0061] Specifically, the first cutting direction is the horizontal coordinate direction. According to the cutting sequence of the cutting plane (which can be preset, such as from left to right, from top to bottom, etc.), along the first cutting direction, continue to extend the path on the basis of the initial cutting path. The extension method is also to move step by step according to certain rules, moving a suitable distance each time, and connecting the newly passed points to the initial cutting path, so as to obtain a longer path, that is, the first cutting path; when the first cutting direction is the vertical coordinate direction, extend the initial cutting path along the second cutting direction according to the cutting sequence of the cutting plane. In the same way of moving step by step and connecting points, obtain the second cutting path. The second cutting path cuts the patch adhesive in the second cutting direction, cooperates with the first cutting path, forms a cutting coverage of the patch adhesive in two directions, and then combines the first cutting path and the second cutting path to obtain the bidirectional cutting path of the patch adhesive image. The bidirectional cutting path contains cutting information in two mutually perpendicular directions, and can guide the cutting device to perform a complete bidirectional cutting operation on the patch adhesive within the cutting plane to achieve the expected cutting effect.

[0062] Furthermore, the bidirectional cutting path describes the specific route for cutting the patch adhesive in two mutually perpendicular directions. In order to clarify information such as the starting point, ending point of different cutting paths and their intersection positions within the cutting plane, it is necessary to display the cutting path in an image way.

[0063] S5. Generate a cutting connection diagram of the patch adhesive image according to the bidirectional cutting path, and locate the cutting position of the patch adhesive image through the cutting connection diagram.

[0064] In an embodiment of the present invention, the cutting connection diagram refers to a graph formed by discretizing a bidirectional cutting path of a patch adhesive image, identifying the intersection points among the path discrete points and determining the connection relationship, and then connecting the path discrete points according to the connection relationship.

[0065] In an embodiment of the present invention, generating the cutting connection diagram of the patch adhesive image according to the bidirectional cutting path includes: Discretizing the bidirectional cutting path to obtain path discrete points; Identifying the intersection points among the path discrete points, and determining the connection relationship between the path discrete points according to the positions corresponding to the intersection points; Connecting the path discrete points according to the connection relationship to obtain a cutting connection diagram.

[0066] Specifically, discretization is to divide a continuous path into a series of discrete points. The points can be selected along the path at a certain interval (such as a fixed distance or the number of pixels). For example, in a graphical cutting path, a point is marked every certain length, and then the original continuous cutting path is represented as a set of discrete points, that is, path discrete points. After obtaining the path discrete points, it is necessary to find the intersection points among them, that is, the points where two or more cutting paths intersect. Once the intersection points are identified, the connection method can be determined according to the positions of the intersection points and the relative position relationships between each path discrete point and the intersection points. For example, if two path discrete points are both adjacent to the same intersection point and their order on their respective paths is continuous, then there is a connection relationship between these two points, so as to determine the mutual connection situation among all discrete points in the entire cutting path. Then, after connecting all the points that meet the connection relationship in sequence, a cutting connection diagram is obtained. The cutting connection diagram intuitively shows the structure of the bidirectional cutting path and the connection situation among its various parts. For example, in a simple cutting path, there are multiple discrete points and several intersection points. After connecting these points according to the connection relationship, the trend of the cutting path and the connection order among each path segment can be clearly seen.

[0067] Furthermore, the cutting connection diagram clearly shows the cutting path of the entire patch adhesive image in a graphical manner, converts the complex cutting path into intuitive lines and nodes, and then through the cutting connection diagram, the starting position, ending position, and connection points of each path segment can be clearly distinguished, so as to accurately determine the cutting position.

[0068] In an embodiment of the present invention, the cutting position refers to the specific position point or position area on the surface of the patch adhesive that needs to be cut according to the cutting requirements and the cutting connection diagram.

[0069] In the embodiment of the present invention, locating the cutting position of the patch back glue image through the cutting connection diagram includes: Identifying the cutting density of the patch back glue image according to the cutting connection diagram; Determining the cutting area of the patch back glue image according to the cutting density; Determining the cutting position of the patch back glue image through the cutting area.

[0070] Specifically, the cutting density refers to the density of cutting paths in a certain area of the patch back glue image. For example, if there are more cutting path line segments and denser distribution of path discrete points in a certain area in the cutting connection diagram, it indicates a higher cutting density in this area; conversely, if there are fewer cutting path line segments and sparser path discrete points in a certain area, the cutting density is lower. By analyzing the distribution of paths in the cutting connection diagram, the cutting density of different areas can be calculated or visually judged. For example, the number of path line segments or the number of path discrete points per unit area can be counted to quantify the cutting density. After determining the cutting density, the cutting area is divided according to certain criteria or requirements. That is, the area with a higher cutting density may be the part that needs more precise or more cutting operations, while the area with a lower cutting density has relatively fewer cutting operations. For example, a cutting density threshold is set, and the area with a cutting density higher than this threshold is divided into one cutting area, and the area lower than this threshold is divided into another or multiple cutting areas. In a cutting area, the starting point, ending point of the cutting path, and key turning points on the path are all important cutting positions. By analyzing the coordinate information of these positions in the cutting connection diagram, the cutting position on the patch back glue image can be accurately located.

[0071] Exemplarily, in the cutting area, the starting point coordinate is (10, 10), which means that in the plane coordinate system of the patch back glue image, this cutting path starts from the position with an abscissa of 10 mm and an ordinate of 10 mm. There are two key turning points, with coordinates (30, 20) and (50, 15) respectively. The first turning point indicates that the cutting path changes direction when it reaches the position with an abscissa of 30 mm and an ordinate of 20 mm; the second turning point is at the position with an abscissa of 50 mm and an ordinate of 15 mm and changes direction again. The ending point coordinate is (70, 30), that is, when the cutting path reaches the position with an abscissa of 70 mm and an ordinate of 30 mm, the cutting in this cutting area ends. Then, the cutting head is first moved to the starting point (10, 10) to start cutting. When cutting to the key turning point (30, 20), the cutting direction is changed, and continue cutting to (50, 15) and change the direction again. Finally, cut to the ending point (70, 30) to complete this section of the cutting operation in this cutting area.

[0072] Such as Figure 2As shown, it is a functional module diagram of a high-precision cutting and positioning system for patch back glue based on machine vision provided by an embodiment of the present invention.

[0073] The high-precision cutting and positioning system 100 for patch back glue based on machine vision according to the present invention can be installed in an electronic device. According to the functions achieved, the high-precision cutting and positioning system 100 for patch back glue based on machine vision can include an image division module 101, an image fusion module 102, a cutting plane determination module 103, a two-way cutting path generation module 104, and a cutting position positioning module 105. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0074] In this embodiment, the functions of each module / unit are as follows: The image division module 101 is used to collect the surface image of the patch back glue based on a preset all-round angle of image acquisition, perform global edge division on the surface image of the patch back glue, and perform a global detail scaling operation on the surface image of the patch back glue after global edge division to obtain a global back glue surface image; The image fusion module 102 is used to perform local center division on the global back glue surface image, perform a local detail scaling operation on the global back glue surface image after local center division to obtain a local back glue surface image, and fuse the global back glue surface image and the local back glue surface image to obtain a patch back glue image; The cutting plane determination module 103 is used to identify the three-dimensional information of the patch back glue image by using a preset dual vision positioning algorithm, and determine the cutting plane of the patch back glue image according to the three-dimensional information; The two-way cutting path generation module 104 is used to identify the two-way cutting direction corresponding to the cutting plane, and generate a two-way cutting path of the patch back glue image according to the two-way cutting direction and the cutting plane; The cutting position positioning module 105 is used to generate a cutting connection diagram of the patch back glue image according to the two-way cutting path, and position the cutting position of the patch back glue image through the cutting connection diagram.

[0075] Specifically, each module in the high-precision cutting and positioning system 100 for patch back glue based on machine vision in the embodiment of the present invention uses the same technical means as those Figure 1 in the high-precision cutting and positioning method for patch back glue based on machine vision described above, and can produce the same technical effects, which will not be elaborated here.

[0076] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0077] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0079] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0080] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is not limited only by the above description. Therefore, it is intended to include all changes within the meaning and scope of equivalent elements falling within the protection scope in the present invention.

[0081] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0082] In addition, obviously, the word "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or systems stated in the system claims can also be implemented by one unit or system through software or hardware. Words such as first and second are used to represent names and do not represent any specific order.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A high-precision cutting and positioning method for patch adhesive based on machine vision, characterized in that: The method comprises: Based on the preset image acquisition omni-directional angle, the patch adhesive surface image is acquired, global edge segmentation is performed on the patch adhesive surface image, and global detail scaling operation is performed on the patch adhesive surface image after global edge segmentation to obtain a global adhesive surface image; Performing local center division on the global adhesive surface image, performing local detail scaling operation on the global adhesive surface image after local center division to obtain a local adhesive surface image, and fusing the global adhesive surface image and the local adhesive surface image to obtain a patch adhesive image; Using a preset dual vision positioning algorithm to identify the three-dimensional information of the patch adhesive image, and determining the cutting surface of the patch adhesive image according to the three-dimensional information; Identify the bidirectional cutting direction corresponding to the cutting surface, and generate a bidirectional cutting path of the patch adhesive backing image according to the bidirectional cutting direction and the cutting surface; A cutting connection diagram of the patch adhesive back image is generated according to the bidirectional cutting path, and a cutting position of the patch adhesive back image is located by using the cutting connection diagram.

2. The high-precision cutting and positioning method for patch adhesive based on machine vision as claimed in claim 1, characterized in that: The method of collecting the surface image of the adhesive backing of the patch from all angles based on the preset image collection includes: Extract the horizontal and vertical directions corresponding to the full range of image acquisition angles; Dividing the image acquisition full-range angle into horizontal full-range angles according to preset horizontal angle intervals in the horizontal direction; Dividing the image acquisition full-range angle into vertical full-range angles according to a preset vertical angle interval in the vertical direction; Omnidirectional image acquisition points are configured according to the horizontal omnidirectional angle and the vertical omnidirectional angle, and the omnidirectional image acquisition points are used to acquire the surface image of the adhesive backing of the patch.

3. The high-precision cutting and positioning method for patch adhesive based on machine vision as claimed in claim 1, characterized in that: The global edge segmentation of the patch adhesive surface image includes: Converting the patch adhesive surface image into an adhesive surface grayscale image; Identify pixel points of the grayscale image of the adhesive surface, and generate edge intensity distribution of the grayscale image of the adhesive surface according to the pixel points; Identify edge points and non-edge points using the edge intensity distribution and a preset edge threshold; The global edge of the patch adhesive surface image is determined according to the edge points and the non-edge points.

4. The high-precision cutting and positioning method for patch adhesive based on machine vision as claimed in claim 1, characterized in that: The step of performing a global detail scaling operation on the patch adhesive surface image after global edge division to obtain a global adhesive surface image includes: Extract the global edge corresponding to the patch adhesive surface image after global edge division; Identify the initial edge point of the global edge, and calculate the dynamic edge point coefficient of the global edge according to the initial edge point: in, is the dynamic edge point coefficient, is the grayscale change weight, is the edge change weight, The initial edge point The number of pixels in the neighborhood, is the pixel point in the neighborhood The gray value of is the average grayscale value of pixels in the neighborhood. is a sine function, is the inverse tangent function, is the target candidate edge point; Determining a global edge point of the global edge according to the dynamic edge point coefficient; Performing a local amplification operation on the global edge points one by one, identifying abnormal images of the patch adhesive surface image according to the amplified global edge points, performing multi-dimensional enhancement processing on the abnormal images, and obtaining an edge surface image corresponding to each global edge point; The edge surface image is subjected to shrinkage processing, the edge intensity distribution of the edge image after the shrinkage processing is identified, and the edge image is subjected to multi-dimensional enhancement processing according to the edge intensity distribution to obtain a global adhesive surface image.

5. The high-precision cutting and positioning method for patch adhesive based on machine vision as claimed in claim 1, characterized in that: The local center division of the global adhesive surface image comprises: Determining an initial local division window according to the image shape of the global adhesive surface image; Determine the division area line of the global adhesive surface image according to the initial local division window; Identify the bidirectional dynamic window factor of the initial local partition window through the partition area line; Dynamically updating the initial local division window using the bidirectional dynamic window factor, and returning to the step of determining the division area lines of the global adhesive back surface image according to the initial local division window until the global adhesive back surface image is completely divided; When the global adhesive back surface image is divided completely, a local area of ​​the global adhesive back surface image is determined according to each divided area line.

6. The high-precision cutting and positioning method for patch adhesive based on machine vision as claimed in claim 1, characterized in that: The step of fusing the global adhesive surface image and the local adhesive surface image to obtain the patch adhesive image includes: Identifying local edge features of the local adhesive surface image and global edge features of the global adhesive surface image; Comparing the feature position of the local edge feature with the feature position of the global edge feature to obtain the same feature position; The local edge feature and the global edge feature are fused according to the same feature position to obtain a patch adhesive backing image.

7. The high-precision cutting and positioning method for patch adhesive based on machine vision as claimed in claim 1, characterized in that: In an embodiment of the present invention, the method of using a preset dual vision positioning algorithm to identify the three-dimensional information of the patch adhesive image includes: Identify the shape complexity index and feature stability index of the patch adhesive image; Convert the world coordinates of the patch adhesive image into image plane coordinates; The first weight and the second weight are determined according to the shape complexity index and the feature stability index, wherein the first weight and the second weight are: in, is the first weight, is the second weight, is the shape complexity index, is the stability index of machine vision features, It is the stability index of the laser sensor characteristics; The following dual vision positioning algorithm is used to perform three-dimensional fusion of the image plane coordinates and the pre-acquired image measurement height to obtain the three-dimensional information of the patch adhesive image: in, is the horizontal coordinate information in the three-dimensional information, is the vertical coordinate information in the three-dimensional information, is the vertical coordinate information in the three-dimensional information, is the first weight, is the second weight, is the plane horizontal coordinate of the target point of the patch adhesive in the world coordinate system, is the plane ordinate of the target point of the patch adhesive in the world coordinate system, is the height coordinate of the target point of the patch adhesive, is the projection horizontal coordinate of the laser measurement point on the plane, is the projection ordinate of the laser measurement point on the plane, is the target height corresponding to the laser measurement point.

8. The high-precision cutting and positioning method for patch adhesive based on machine vision as claimed in claim 1, characterized in that: The bidirectional cutting path of generating the patch adhesive image according to the bidirectional cutting direction and the cutting surface includes: Any of the cutting planes is selected as the initial cutting plane, and an initial cutting path is obtained by gradually moving from any end point of the initial cutting plane according to the bidirectional cutting direction; Extending the initial cutting path according to a first cutting direction in the bidirectional cutting directions and a cutting order of the cutting surface to obtain a first cutting path; Extending the initial cutting path according to the second cutting direction in the bidirectional cutting direction and the cutting order to obtain a second cutting path; A bidirectional cutting path of the patch adhesive image is generated according to the first cutting path and the second cutting path.

9. The high-precision cutting and positioning method for patch adhesive based on machine vision as claimed in claim 1, characterized in that: The step of generating a cutting connection diagram of the patch adhesive image according to the bidirectional cutting path includes: Discretizing the bidirectional cutting path to obtain path discrete points; Identifying intersections among the discrete points on the path, and determining a connection relationship between the discrete points on the path according to positions corresponding to the intersections; The discrete points of the path are connected according to the connection relationship to obtain a cutting connection graph.

10. A high-precision cutting and positioning system for patch adhesive based on machine vision, characterized in that: Used to perform the high-precision cutting and positioning method for patch adhesive based on machine vision according to any one of claims 1 to 9, the system comprises: An image segmentation module is used to acquire a patch adhesive surface image based on a preset image acquisition omni-directional angle, perform global edge segmentation on the patch adhesive surface image, perform global detail scaling operation on the patch adhesive surface image after global edge segmentation, and obtain a global adhesive surface image; An image fusion module is used to perform local center division on the global adhesive surface image, perform local detail scaling operation on the global adhesive surface image after local center division to obtain a local adhesive surface image, and fuse the global adhesive surface image and the local adhesive surface image to obtain a patch adhesive image; A cutting plane determination module, used to identify the three-dimensional information of the patch adhesive image using a preset dual vision positioning algorithm, and determine the cutting plane of the patch adhesive image according to the three-dimensional information; A bidirectional cutting path generation module, used for identifying the bidirectional cutting direction corresponding to the cutting surface, and generating a bidirectional cutting path of the patch adhesive backing image according to the bidirectional cutting direction and the cutting surface; A cutting position positioning module is used to generate a cutting connection diagram of the patch adhesive image according to the bidirectional cutting path, and locate the cutting position of the patch adhesive image through the cutting connection diagram.

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