CN117893612B - Visual positioning method for medium plate lifting appliance clamping process - Google Patents
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Abstract
The invention relates to the technical field of image data processing, in particular to a visual positioning method for a medium plate lifting appliance clamping process, which comprises the following steps: acquiring an image of a medium plate; acquiring each contour by using an edge detection algorithm and a corner detection algorithm; distinguishing two sides of the contour according to the normal direction, and constructing two-side long run factor difference coefficients at each pixel point and two-side circumscribed texture contrast ratios of the contour according to texture features at the two sides of the contour at each pixel point; further constructing a texture contrast sequence and local texture similarity of each window; obtaining the contrast fluctuation coefficients of the circumscribed textures at the two sides of each contour; acquiring suspected corner contours, constructing the corner saliency score of each suspected corner contour relative to the rest of the suspected corner contours, and constructing the corner contour saliency of each suspected corner contour based on the corner saliency score; and marking the position of the medium plate by adopting a machine learning algorithm according to the edge contour saliency. The invention can improve the accuracy of positioning the medium plate.
Description
Technical Field
The application relates to the technical field of image data processing, in particular to a visual positioning method for a medium plate lifting appliance clamping process.
Background
The medium plate is a plate widely used in engineering, the thickness of the medium plate is generally between 4.5mm and 25mm and is far smaller than the plane size of the medium plate, and the medium plate is widely applied in the fields of constructional engineering, machine manufacturing, container manufacturing, shipbuilding, bridge construction and the like. In the production process of the medium plate, the medium plate must be suspended by a hanger and accurately fed to a designated position. Because of the relatively large size of the sheet material, large spreaders with hooks are often used for gripping. Although the medium plate has a certain thickness, in the lifting process, the lifting appliance can still cause the problems of hanging injury, hanging bending and the like of the medium plate, thereby influencing the appearance quality of the medium plate. Therefore, in the clamping process, the medium plate needs to be accurately positioned so as to reduce damage caused by lifting to the greatest extent and ensure that the appearance quality of the medium plate meets the requirement.
The medium plates are usually manufactured by a rolling process, in which the billet is passed through a series of rolls, gradually reducing the section and obtaining the desired thickness. Different rolls and rolling parameters may leave streak marks on the surface of the medium plate. The existing method based on visual positioning has the defect that the surface stripes of the medium plate are easily identified as real boundaries by mistake, so that the positioning is inaccurate, and the medium plate is damaged.
Disclosure of Invention
In order to solve the technical problems, the invention provides a visual positioning method for a medium plate lifting appliance clamping process, which aims to solve the existing problems.
The invention discloses a visual positioning method for a medium plate lifting appliance clamping process, which adopts the following technical scheme:
One embodiment of the invention provides a visual positioning method for a medium plate lifting appliance clamping process, which comprises the following steps:
acquiring a medium plate image and acquiring an edge image of the medium plate image;
Detecting edge image corner points by using a corner point detection algorithm, and connecting lines between the corner points to obtain each contour; for each contour, acquiring the normal direction of each pixel point on the contour, distinguishing two sides of the contour according to the normal direction, and constructing two-side long run factor difference coefficients of each pixel point according to texture features of two sides of the contour of each pixel point; constructing contour two-side circumscribed texture contrast ratios at each pixel point according to the two-side long run factor difference coefficients at each pixel point; dividing each contour into windows according to the preset numerical value pixel length, and constructing a texture contrast sequence of each window according to the circumscribed texture contrast of the two sides of the contour at the pixel point in each window; constructing local texture similarity of each window according to the difference of texture comparison sequences of each window and other windows in the outline of each window; constructing two side circumscribed texture contrast fluctuation coefficients of each contour according to the local texture similarity of the windows in each contour; obtaining suspected corner contours according to the contrast fluctuation coefficients of the circumscribed textures at the two sides of each contour; constructing the edge significant fraction of each suspected edge profile relative to the other suspected edge profiles according to the gradient value sequences of each suspected edge profile and the other suspected edge profiles; constructing the edge profile saliency of each suspected edge profile according to the edge saliency fraction of each suspected edge profile relative to the other suspected edge profiles and the external-tangent texture contrast fluctuation coefficients of the two sides of the suspected edge profile;
And marking the position of the medium plate by utilizing a Boosting algorithm according to the edge profile saliency, and realizing visual positioning in the clamping process of the medium plate lifting tool.
Further, the constructing the difference coefficient of the long run factors at two sides of each pixel point according to the texture features at two sides of the outline at each pixel point includes:
For each pixel point, taking the pixel point as a tangent point to respectively make circumscribed circles on two sides of the outline, respectively calculating long run factors of gray scale run matrixes under four preset angles in each circumscribed circle region, respectively calculating average values of all long run factors in each circumscribed circle region, calculating a ratio of the average value of the circumscribed circle on the right side of the outline along the normal direction to the average value of the other circumscribed circle, taking the ratio as an index of an exponential function based on a natural constant, calculating a difference absolute value between a calculation result of the exponential function and 1, and taking the difference absolute value as a difference coefficient of the long run factors on two sides of the pixel point.
Further, the constructing the contour two-side circumscribed texture contrast ratio at each pixel point according to the two-side long run factor difference coefficient at each pixel point includes:
For each pixel point, respectively counting the gray value average value of all the pixel points in each circumscribed circle region of the pixel point, respectively calculating the difference absolute value of the gray value of each pixel point in each circumscribed circle region and the gray value average value of each circumscribed circle, marking the difference absolute value as a first difference absolute value, respectively calculating the ratio of the first difference absolute value of each pixel point in each circumscribed circle region to the total number of the pixel points in each circumscribed circle region, respectively calculating the sum value of the ratio of all the pixel points in each circumscribed circle region, obtaining the difference absolute value of the sum values of the two circumscribed circles, marking the difference absolute value as a second difference absolute value, calculating the product of the difference coefficient of the long run factors at two sides of the pixel point and the second difference absolute value, and taking the product as the contour two-side circumscribed texture contrast at the pixel point.
Further, the constructing a texture contrast sequence of each window according to the contour two-sided circumscribed texture contrast at the pixel point in each window includes:
And for each window, the two sides of the outline at all pixel points in the window are circumscribed with texture contrast to form a texture contrast sequence of the window.
Further, the constructing the local texture similarity of each window according to the difference of the texture contrast sequences of each window and the rest windows in the outline where each window is located includes:
and for each window, calculating the DTW distance between the window and the texture contrast sequences of the other windows in the outline of the window, calculating the sum value of all the DTW distances of the window, and taking the sum value as the local texture similarity of the window.
Further, the constructing the two-side circumscribed texture contrast fluctuation coefficients of each contour according to the local texture similarity of the windows in each contour includes:
For each contour, forming a local texture similarity sequence of all windows in the contour according to a window sequence, calculating the sum value of all elements in the local texture similarity sequence, marking the sum value as a first sum value, calculating the ratio of the first sum value to the number of windows in the contour, marking the ratio as a first ratio, calculating the mean value of all elements in the local texture similarity sequence, calculating the difference value of each element in the local texture similarity sequence and the mean value, calculating the variance of all elements in the local texture similarity sequence, calculating the ratio of the difference value of each element in the local texture similarity sequence to the variance, marking the ratio as a second ratio, marking the sum value of the second ratio of all elements in the local texture similarity sequence as a second sum value, and taking the product of the first ratio and the second sum value as a contour two-side circumscribed texture contrast fluctuation coefficient.
Further, the obtaining the suspected corner profile according to the two-side circumscribed texture contrast fluctuation coefficients of each profile includes:
and arranging the contours from small to large according to the fluctuation coefficients of the external-cut texture contrast ratios at the two sides, and taking the contours corresponding to the fluctuation coefficients of the external-cut texture contrast ratios at the two sides of the preset proportion as suspected corner contours.
Further, the constructing the significant score of each suspected corner contour relative to the other suspected corner contours according to the gradient value sequence of each suspected corner contour and the other suspected corner contours includes:
For each suspected corner contour, acquiring gradient values of each pixel point of the suspected corner contour, and arranging the gradient values according to the sequence of the pixel points to form a gradient value sequence of the suspected corner contour to acquire the average value of all elements in the gradient value sequence of the suspected corner contour; calculating the absolute value of the difference value of the average value of the suspected corner outline and the rest suspected corner outlines, and when the absolute value of the difference value of the suspected corner outlines is smaller than or equal to 0.1, the significant fraction of the suspected corner outlines relative to the corners of the suspected corner outlines is 1;
Calculating the product of the average value of the suspected corner profile and the rest of the suspected corner profiles, calculating the difference value between-1 and a preset value larger than zero, and calculating the sum value of-1 and the preset value larger than zero, wherein when the product of the suspected corner profiles is larger than the difference value and smaller than the sum value, the significant fraction of the suspected corner profile relative to the corners of the suspected corner profiles is 1;
otherwise, the significant fraction of the suspected corner profile relative to the corners of the remaining suspected corner profiles is 0.1.
Further, the constructing the edge profile saliency of each suspected edge profile according to the edge saliency score of each suspected edge profile relative to other suspected edge profiles and the external-tangent texture contrast fluctuation coefficient of two sides of the suspected edge profile includes:
And calculating the sum value of the edge significance scores of the suspected edge contours relative to the other suspected edge contours, marking the sum value as a first sum value, calculating the absolute value of the difference value of the external-tangent texture contrast fluctuation coefficients of the two sides of the suspected edge contours and the other suspected edge contours, calculating the sum value of all the absolute values of the difference values of the suspected edge contours, marking the sum value as a second sum value, calculating the sum value of the second sum value and a preset value larger than zero, marking the sum value as a third sum value, and taking the ratio of the first sum value to the third sum value as the edge contour significance of the suspected edge contours.
Further, the marking of the position of the medium plate by using Boosting algorithm according to the edge profile saliency, to realize the visual positioning of the medium plate in the lifting tool clamping process, includes:
The input of the Boosting algorithm is the 4 outlines with the maximum saliency of the edge outline of the medium plate image, and the output of the Boosting algorithm is the marking position of the medium plate.
The invention has at least the following beneficial effects:
According to the method, the outline in the medium plate is identified, textures on two sides of the outline are analyzed, and the gray value and the long run factor are combined to calculate the external tangent texture contrast on two sides of the outline at the pixel points, so that the degree that the textures on two sides of the outline at each pixel point on the outline are similar to gray distribution is estimated; constructing two-side circumscribed texture contrast fluctuation coefficients of the contour according to the two-side circumscribed texture contrast of the contour at the pixel points on the contour, and estimating the probability that the contour is the true boundary contour of the medium plate, wherein the two-side circumscribed texture contrast fluctuation coefficients are used for estimating the similarity degree of textures at two sides of the whole contour; further, a suspected boundary contour set is obtained through the contrast fluctuation coefficients of the circumscribed textures at the two sides, so that the contours with higher probability can be analyzed in a targeted manner; the method comprises the steps of constructing a corner contour saliency degree based on a gradient value sequence of a suspected boundary contour and two side circumscribed texture contrast fluctuation coefficients, analyzing the saliency degree of the suspected boundary contour, facilitating extraction of a characteristic contour closest to the actual boundary of the medium plate from a suspected boundary contour set, and improving reliability of acquiring the actual boundary contour of the medium plate; the machine learning algorithm is adopted to carry out visual positioning on the medium plate, so that the defect that the surface stripes of the medium plate are easy to be mistakenly identified as the real boundaries in the existing method is overcome, and the accuracy of positioning the medium plate is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of a visual positioning method for a medium plate lifting appliance clamping process provided by the invention;
fig. 2 is a schematic diagram of a corner profile saliency acquisition process.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a visual positioning method for the medium plate lifting appliance clamping process according to the invention, which is provided by combining the accompanying drawings and the preferred embodiment, and the specific implementation, structure, characteristics and effects thereof are described in detail below. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a visual positioning method for a medium plate lifting appliance clamping process, which is specifically described below with reference to the accompanying drawings.
The invention provides a visual positioning method for a medium plate lifting appliance clamping process, in particular to a visual positioning method for a medium plate lifting appliance clamping process, referring to fig. 1, which comprises the following steps:
And S001, shooting the medium plate by adopting a CMOS camera, and preprocessing the shot image.
The clamping process of the medium plate lifting appliance is as follows: according to the thickness of the hooked medium plate, the pressing plate device is firstly adjusted to be at the same level, and the distance between the surface of the pressing plate and the upper surface of the hook is set to be larger than the thickness of the hooked medium plate; performing visual positioning on the medium plate, and moving the lifting appliance after determining the position; when the lifting appliance slowly falls down and the pressing plate contacts the hooked medium plate, opening the opening and closing device for controlling the hooks, and gradually opening two rows of hooks to the maximum size; lifting the lifting appliance, gradually folding the two rows of hooks and clamping the medium plate; and (3) placing the clamped medium plate at a designated position until the two rows of hooks are opened to the maximum, hanging the padlock of the opening and closing device to a locking state, keeping the two rows of hooks open, and lifting the lifting appliance. In the clamping process, the image of the medium plate is required to be shot for visual positioning, the opening and closing device is positioned at the center position below the lifting appliance, and the CMOS camera is arranged on the side surface of the opening and closing device to collect the image of the medium plate. In the process of measuring the medium plate by utilizing machine vision, the accuracy of a measurement result is closely related to the accuracy of extracting the medium plate image information, and the distortion of the medium plate image needs to be removed before the medium plate image is visually positioned. According to the embodiment, the undistort library function in the OpenCV is called, and the parameters of the calibration camera are combined to perform distortion compensation in the horizontal and vertical directions on the medium plate image, so that the distortion correction of the medium plate image is realized. The parameter acquisition and distortion correction of the calibration camera are known techniques, and the description of this embodiment is omitted. And after the distortion correction is completed, carrying out gray conversion on the medium plate image to obtain a medium plate gray image G.
Step S002, based on the texture similarity degree of the two sides of the outline, constructing the external-cut texture contrast ratio of the two sides of the outline and the external-cut texture contrast fluctuation coefficient of the two sides, further extracting the suspected corner outline, and constructing the corner outline saliency of the suspected corner outline.
In the production process of the medium plate, firstly, the billet raw material is required to be heated, then the billet is pressed into a rough shape through rough rolling equipment, and finally continuous rolling is carried out for a plurality of times through a roller way, so that the required plate thickness is obtained, and the surface smoothness of the medium plate is ensured. During hot rolling of medium plates, "steel sheet scale" formed from oxides and other impurities, also known as steel slag, is produced on the surface. The steel slag is produced because the surface of the steel sheet may react with slag, iron slag on rolls, etc. during the smelting and rolling processes to form oxidized slag skin. In the process of clamping the medium plate lifting appliance, the real boundary of the medium plate needs to be accurately identified, and the formation of steel slag can cause interference to the boundary identification. The texture features on both sides of the steel slag profile in image G are analyzed as follows.
Firstly, smoothing an image G by adopting a Gaussian filter technology to weaken pixel noise, then detecting the edge of the image by using a Canny edge detection algorithm, and marking the edge image obtained by the processing as a Line. And then carrying out corner detection on the image Line by adopting a Shi-Tomasi corner detection algorithm, connecting the corners along the edge of the image, and regarding the connecting Line between the two corners as a contour. The gaussian filtering technique, the Canny edge detection algorithm and the Shi-Tomasi corner detection algorithm are known techniques, and the description of this embodiment is omitted. The two sides of the steel slag profile have larger difference, the internal texture of the steel slag is complex, ravines are more, and the external is relatively smooth. And calculating a normal line at each pixel point in the contour image by using a least square method, wherein the direction of the normal line is tangential to and perpendicular to the contour curve. The method of least squares calculates the normal line as a well-known technique, and this embodiment will not be described in detail.
The method comprises the steps of distinguishing two sides of a contour at each pixel point by utilizing a normal direction at each pixel point, taking a pixel point po on the contour as a tangent point, respectively making circumscribed circles at two sides of the contour, setting a radius of the circle, wherein a radius of the circle is 5 in the embodiment, marking the circumscribed circle at the right side of the contour along the normal direction as a circumscribed circle C1, marking the circumscribed circle at the other side as a circumscribed circle C2, analyzing texture characteristics by calculating a gray scale run matrix, taking the circumscribed circle C1 as an example, firstly calculating a gray scale run matrix at four angles of 0 o、45o、90o、135o in the area of the circumscribed circle C1, then respectively calculating long run factors of the four run matrices, calculating an average value of the long run factors to be L (C1), wherein the long run factors reflect the texture complex characteristics in the area, and the smaller the value is, the more complex the description of texture. The calculation method of the gray scale run matrix and the long run factor is a known technique, and the embodiment will not be described in detail.
Calculating the contrast of circumscribed textures on two sides of the contour, wherein the expression is as follows:
In the method, in the process of the invention, Representing contour both sides circumscribed texture contrast at pixel point po on contour,/>Representing the difference coefficient of the two-side long run factors at the pixel point po on the contour, nc1 represents the number of pixels in the area of the circumscribed circle C1,The gray value of the pixel point i in the area of the circumscribed circle C1 is represented, and μc1 represents the average gray value of all the pixel points in the area of the circumscribed circle C1. Nc2 represents the number of pixels in the circumscribed circle C2 region,/>Represents the gray value of the pixel j in the area of the circumscribed circle C2, and μC2 represents the gray value average value of all the pixels in the area of the circumscribed circle C2,/>Representing an exponential function with a base of natural constant, L (C1) and L (C2) represent the mean of the long run factors in the region of the circumscribed circles C1 and C2, respectively.
If the gully in the region of the circumscribed circle C1 is more abundant, the gray value of the pixel point in the region of the circumscribed circle C1 is more in order, the larger the variation of the gray value is, the further the gray value is from the average value,The larger; if the pixel gray value in the circumscribed circle C2 area is smoother, the gray value is closer to the average value, and the gray value is/(is)The smaller; if the texture similarity in the areas of the circumscribed circles C1 and C2 is lower, the ratio of the average values of the long run factors in the areas of the circumscribed circles C1 and C2 is far from 1, and the difference coefficient/>The larger; when the probability that the contour corresponding to the pixel point po belongs to the steel slag part is larger, the external tangent texture contrast ratio/>, of the two sides of the contour at the pixel point poThe larger.
In addition to the influence of steel slag on boundary judgment during hot rolling, the heat treatment process may have uneven temperature distribution, which may cause non-uniformity of the sheet. Thus, if the rolling operation is not accurate enough during the rolling process, uneven surfaces of the plate may be caused, and streaks may occur. The two sides of the stripe profile have the characteristic of larger grain difference, but the stripe profile is relatively neat, and the real boundary of the medium plate is in a straight state, so that the bending degree of the profile is further analyzed.
The contrast of the external textures on both sides of the contour at each pixel point on each contour is calculated, then each contour is divided into a plurality of windows according to the length of eta pixels, and the setting implementation of eta can be selected by the user, and eta is 20 in the embodiment. The contour two sides of all pixel points in each window are circumscribed to form a texture contrast sequence of the window, which is recorded as. According to the texture contrast sequence, constructing a two-sided circumscribed texture contrast fluctuation coefficient, wherein the expression is as follows:
In the method, in the process of the invention, Representing the two-sided circumscribed texture contrast fluctuation coefficient of the contour l, sum () represents the summation operation,/>Representing the number of windows within the contour l,/>Representing local texture similarity of the alpha-th window in the contour l, sigma () represents the computation variance operation, DTW () represents the DTW distance,/>And/>Respectively representing texture contrast sequences for the alpha-th window and the beta-th window,Local texture similarity sequence representing local texture similarity of all windows in contour l and composed according to window sequence,/>Representation sequence/>The mean value of all elements in the interior. The calculation of the DTW distance is a known technique, and the embodiment will not be described in detail.
The tortuosity of the contour has randomness, when the tortuosity of the contour l is more irregular, the difference of the texture contrast sequences of the window in the contour l is larger, when the tortuosity of the contour l is more regular, the local texture similarity change is more stable, the texture contrast sequences of the window in the contour l are more similar, the DTW distance of the texture contrast sequences of the window is smaller, so thatAnd/>The smaller the two sides of the contour l circumscribe the texture contrast fluctuation coefficient/>The smaller the profile l, the greater the probability of belonging to a relatively regular stripe or true boundary profile in the medium plate.
After the hot rolling is finished, quenching treatment is required to be carried out on the medium plate, when the temperatures of different parts are uneven, the medium plate can shrink unevenly, thermal deformation is caused, the geometric shape and the dimensional stability of the medium plate can be affected, and the real boundary of the medium plate can be slightly deformed. The medium plate is standard rectangle, and four angles of medium plate are easier to dispel the heat for the middle part, and the deformation volume is less than other positions, therefore, this embodiment combines the contour feature near four angles to carry out visual localization.
The hot-rolled streaks in the interior of the medium plate are displayed in the image G in a right-angle state, and the identification of the right angle is easy to cause erroneous positioning, so that the identification is required based on the texture on both sides of the square outline and the characteristics of thermal deformation. In accordance with the above-described analysis,The smaller the value is, the more likely the value belongs to the corner profile, in order to realize the targeted analysis of the profile with higher probability of belonging to the true boundary of the medium plate, the two sides of all profiles are firstly arranged according to the order from small to large, the profile corresponding to the value of ω before extraction is taken as the suspected corner profile, and the value practitioner of ω can select the value of ω by himself, in this embodiment ω is 20%. And (3) for each suspected corner contour, acquiring gradient values of each pixel point of the suspected corner contour, and arranging gradient value sequences forming the suspected corner contour according to the sequence of the pixel points, wherein the gradient value sequences are marked as E. Then constructing the edge profile saliency, wherein the expression is:
In the method, in the process of the invention, Corner profile saliency representing suspected corner profile x,/>Representing the significant fraction of the edge of the suspected edge profile x relative to the suspected edge profile y, epsilon being a value preset to be greater than zero,/>And/>Two side circumscribed texture contrast fluctuation coefficients respectively representing a suspected corner profile x and a suspected corner profile y,/>And/>Gradient value sequences respectively representing suspected corner profile x and suspected corner profile y,/>For the averaging operation, τ is a preset value greater than zero.
If the two suspected corner contours are more parallel, the average value of the gradient value sequences of the two suspected corner contours is nearly equal,The smaller the value, the more vertical the two suspected corner contours are, the product of the average values of the gradient value sequences of the two suspected corner contours is close to-1. If a certain suspected corner outline meets the two conditions, the sum of the significant fractions of the corners of the suspected corner outline relative to the rest suspected corner outlines is larger. In the above-mentioned process, the two sides of one corner are regarded as two independent suspected corner profiles, so that the sum of the significant fractions of the corners of the suspected corner profile x is higher when the likelihood that the suspected corner profile x is located at the corner is higher; and the closer the suspected corner profile x is to the two-sided circumscribed texture contrast fluctuation coefficients of the rest of the suspected corner profiles, namely/>, the more closelySmaller the corner profile saliency/>The larger. Corner profile saliency/>The larger the probability that the suspected corner profile x belongs to the true boundary of the medium plate is indicated to be larger. A schematic diagram of the corner profile saliency acquisition process is shown in fig. 2.
And S003, performing visual positioning on the medium plate by adopting a machine learning algorithm.
In this embodiment, a Boosting algorithm is adopted to track the target, in which the loss function is set to be a logarithmic loss function, the learning rate is set to be 0.1, the tree depth is set to be 5, and the Boosting algorithm is a known technology, and no description is given in this embodiment. The input of the algorithm is a medium plate image and the characteristic contour of the medium plate in the image, the characteristic contour is set to be 4 contours with the maximum edge contour saliency, and the output of the algorithm is the marking position of the medium plate, so that the visual positioning of the medium plate in the lifting appliance clamping process is realized.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; the technical solutions described in the foregoing embodiments are modified or some of the technical features are replaced equivalently, so that the essence of the corresponding technical solutions does not deviate from the scope of the technical solutions of the embodiments of the present application, and all the technical solutions are included in the protection scope of the present application.
Claims (5)
1. The visual positioning method for the medium plate lifting appliance clamping process is characterized by comprising the following steps of:
acquiring a medium plate image and acquiring an edge image of the medium plate image;
Detecting edge image corner points by using a corner point detection algorithm, and connecting lines between the corner points to obtain each contour; for each contour, acquiring the normal direction of each pixel point on the contour, distinguishing two sides of the contour according to the normal direction, and constructing two-side long run factor difference coefficients of each pixel point according to texture features of two sides of the contour of each pixel point; constructing contour two-side circumscribed texture contrast ratios at each pixel point according to the two-side long run factor difference coefficients at each pixel point; dividing each contour into windows according to the preset numerical value pixel length, and constructing a texture contrast sequence of each window according to the circumscribed texture contrast of the two sides of the contour at the pixel point in each window; constructing local texture similarity of each window according to the difference of texture comparison sequences of each window and other windows in the outline of each window; constructing two side circumscribed texture contrast fluctuation coefficients of each contour according to the local texture similarity of the windows in each contour; obtaining suspected corner contours according to the contrast fluctuation coefficients of the circumscribed textures at the two sides of each contour; constructing the edge significant fraction of each suspected edge profile relative to the other suspected edge profiles according to the gradient value sequences of each suspected edge profile and the other suspected edge profiles; constructing the edge profile saliency of each suspected edge profile according to the edge saliency fraction of each suspected edge profile relative to the other suspected edge profiles and the external-tangent texture contrast fluctuation coefficients of the two sides of the suspected edge profile;
Marking the position of the medium plate by utilizing a Boosting algorithm according to the edge profile saliency, and realizing visual positioning in the clamping process of the medium plate lifting tool;
The construction of the difference coefficient of the long run factors at the two sides of each pixel point according to the texture features at the two sides of the outline of each pixel point comprises the following steps:
For each pixel point, taking the pixel point as a tangent point to respectively make circumscribed circles on two sides of the outline, respectively calculating long run factors of gray scale run matrixes under four preset angles in each circumscribed circle region, respectively calculating the average value of all long run factors in each circumscribed circle region, calculating the ratio of the average value of the circumscribed circle on the right side of the outline along the normal direction to the average value of the other circumscribed circle, taking the ratio as an index of an exponential function with a natural constant as a base, calculating the absolute value of the difference between the calculated result of the exponential function and 1, and taking the absolute value of the difference as the difference coefficient of the long run factors on two sides of the pixel point;
the step of constructing the contour two-side circumscribed texture contrast ratio at each pixel point according to the two-side long run factor difference coefficient at each pixel point comprises the following steps:
For each pixel point, respectively counting the gray value average value of all the pixel points in each circumscribed circle region of the pixel point, respectively calculating the difference absolute value of the gray value of each pixel point in each circumscribed circle region and the gray value average value of each circumscribed circle, marking the difference absolute value as a first difference absolute value, respectively calculating the ratio of the first difference absolute value of each pixel point in each circumscribed circle region to the total number of the pixel points in each circumscribed circle region, respectively calculating the sum value of the ratio of all the pixel points in each circumscribed circle region, obtaining the difference absolute value of the sum values of the two circumscribed circles, marking the difference absolute value as a second difference absolute value, calculating the product of the difference coefficient of long run factors at two sides of the pixel point and the second difference absolute value, and taking the product as the contour two-side circumscribed texture contrast at the pixel point;
The construction of the two-side circumscribed texture contrast fluctuation coefficients of each contour according to the local texture similarity of the windows in each contour comprises the following steps:
For each contour, forming a local texture similarity sequence of all windows in the contour according to a window sequence, calculating the sum value of all elements in the local texture similarity sequence, marking the sum value as a first sum value, calculating the ratio of the first sum value to the number of windows in the contour, marking the ratio as a first ratio, calculating the mean value of all elements in the local texture similarity sequence, calculating the difference value of each element in the local texture similarity sequence and the mean value, calculating the variance of all elements in the local texture similarity sequence, calculating the ratio of the difference value of each element in the local texture similarity sequence to the variance, marking the ratio as a second ratio, marking the sum value of the second ratio of all elements in the local texture similarity sequence as a second sum value, and taking the product of the first ratio and the second sum value as a contour two-side circumscribed texture contrast fluctuation coefficient;
The step of constructing the edge significant score of each suspected edge profile relative to the other suspected edge profiles according to the gradient value sequence of each suspected edge profile and the other suspected edge profiles comprises the following steps:
For each suspected corner contour, acquiring gradient values of each pixel point of the suspected corner contour, and arranging the gradient values according to the sequence of the pixel points to form a gradient value sequence of the suspected corner contour to acquire the average value of all elements in the gradient value sequence of the suspected corner contour; calculating the absolute value of the difference value of the average value of the suspected corner outline and the rest suspected corner outlines, and when the absolute value of the difference value of the suspected corner outlines is smaller than or equal to 0.1, the significant fraction of the suspected corner outlines relative to the corners of the suspected corner outlines is 1;
Calculating the product of the average value of the suspected corner profile and the rest of the suspected corner profiles, calculating the difference value between-1 and a preset value larger than zero, and calculating the sum value of-1 and the preset value larger than zero, wherein when the product of the suspected corner profiles is larger than the difference value and smaller than the sum value, the significant fraction of the suspected corner profile relative to the corners of the suspected corner profiles is 1;
otherwise, the significant fraction of the corners of the suspected corner outlines relative to the corners of the other suspected corner outlines is 0.1;
The constructing the edge profile saliency of each suspected edge profile according to the edge saliency score of each suspected edge profile relative to other suspected edge profiles and the external tangent texture contrast fluctuation coefficient of two sides of the suspected edge profile comprises the following steps:
And calculating the sum value of the edge significance scores of the suspected edge contours relative to the other suspected edge contours, marking the sum value as a first sum value, calculating the absolute value of the difference value of the external-tangent texture contrast fluctuation coefficients of the two sides of the suspected edge contours and the other suspected edge contours, calculating the sum value of all the absolute values of the difference values of the suspected edge contours, marking the sum value as a second sum value, calculating the sum value of the second sum value and a preset value larger than zero, marking the sum value as a third sum value, and taking the ratio of the first sum value to the third sum value as the edge contour significance of the suspected edge contours.
2. The visual positioning method for a medium plate lifting appliance clamping process according to claim 1, wherein the construction of the texture contrast sequence of each window according to the contour two-sided circumscribed texture contrast at the pixel point in each window comprises the following steps:
And for each window, the two sides of the outline at all pixel points in the window are circumscribed with texture contrast to form a texture contrast sequence of the window.
3. The visual positioning method for the medium plate lifting appliance clamping process according to claim 1, wherein the constructing the local texture similarity of each window according to the difference of the texture contrast sequences of each window and the rest windows in the outline of each window comprises the following steps:
and for each window, calculating the DTW distance between the window and the texture contrast sequences of the other windows in the outline of the window, calculating the sum value of all the DTW distances of the window, and taking the sum value as the local texture similarity of the window.
4. The visual positioning method for a medium plate sling clamping process according to claim 1, wherein the obtaining the suspected corner profile according to the external tangent texture contrast fluctuation coefficient at two sides of each profile comprises:
and arranging the contours from small to large according to the fluctuation coefficients of the external-cut texture contrast ratios at the two sides, and taking the contours corresponding to the fluctuation coefficients of the external-cut texture contrast ratios at the two sides of the preset proportion as suspected corner contours.
5. The visual positioning method for the medium plate lifting appliance clamping process according to claim 1, wherein the marking of the position of the medium plate by using Boosting algorithm according to the edge profile saliency, the visual positioning in the medium plate lifting appliance clamping process is realized, and the visual positioning method comprises the following steps:
The input of the Boosting algorithm is the 4 outlines with the maximum saliency of the edge outline of the medium plate image, and the output of the Boosting algorithm is the marking position of the medium plate.
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