A rapid online detection method and equipment for the production of reflective glass beads

Abstract

The present invention discloses a rapid online inspection method and equipment for reflective glass bead production, relating to the field of material inspection technology. The method comprises obtaining a threshold image of the reflective glass beads using a spectral source, determining a marker point in the threshold image based on the characteristics of the reflective glass beads, collecting reflection images formed by the spectrum of two adjacent reflective glass beads in a time series, wherein the reflection images in each time series are different, determining the contact state of the two adjacent reflective glass beads based on the positions of the two marker points in the reflection image, performing a search and matching based on the threshold image features among multiple reflection image features, and determining the quality of the reflective glass beads based on the matching results. By determining the marker point during inspection based on the characteristics of the reflective glass beads, the present invention enables the positioning of the reflective glass beads on a high-speed transmission reflective glass bead production line by identifying the marker point instead of identifying the entire reflective glass bead.

Description

Technical Field

[0001] The present invention relates to the technical field of material detection, and in particular to a rapid online detection method and equipment for the production of reflective glass beads. Background Art

[0002] During the production process of reflective glass beads, the reflective glass beads produced are sticky. This makes the overall quality of the reflective glass beads adversely affected to a certain extent when they come into contact with each other. Therefore, there is an urgent need for an effective detection solution that can monitor and control the production status of reflective glass beads in real time and accurately on the production line, so as to ensure that the produced reflective glass beads meet the corresponding quality standards.

[0003] After searching, the Chinese invention patent with announcement number "CN117805131A" discloses a "glass ball detection system and method". This application places the light source device below the glass ball transmission device and the shooting device above the glass ball transmission device. Such position distribution can ensure that the top of the glass ball can be clearly photographed by the shooting device. The glass ball transmission device can control the glass ball to rotate in different directions, so that the image captured by the shooting device can cover the entire glass ball, reducing measurement errors. The data processing device is connected to the glass ball transmission device to control the glass ball to rotate in different directions. At the same time, the data processing device is also connected to the shooting device to realize automatic analysis of the image, so that the glass balls can be detected in batches.

[0004] In addition, the Chinese invention with announcement number "CN116718568A" discloses "a device and method for detecting the reflective performance of reflective materials". This application controls the variables of monitoring angle, incident angle and monitoring distance to measure the reflective coefficient under multiple states, greatly improving the detection accuracy and detection effect.

[0005] During the production and preparation of reflective glass beads, due to their huge output and the stickiness of the produced glass beads, when they come into contact with each other, they will have an adverse effect on the overall quality of the glass beads. However, the above-mentioned disclosed device methods and similar patents usually inspect the product as a whole during the actual inspection process, and do not independently inspect the relative positional relationship between products. Therefore, when faced with a large number of products, such inspection methods are not only inefficient, but also long-term inspections may lead to large errors in the inspection results and increase the actual workload of the inspection equipment. In view of this, the present invention proposes a rapid online inspection method and related equipment for the production of reflective glass beads to solve the above problems. Summary of the Invention

[0006] The object of the present invention is to provide a rapid online detection method and equipment for the production of reflective glass beads to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] First, a rapid online detection method for reflective glass bead production is proposed, comprising:

[0009] A threshold image of reflective glass beads is obtained using spectral source illumination;

[0010] Based on the characteristics of the reflective glass beads, the marking points are determined in the threshold image;

[0011] The reflection images formed by the spectrum of two adjacent reflective glass beads in the acquisition time series are different in each time series.

[0012] Based on the positions of two marking points in the reflected image, the contact state of two adjacent reflective glass beads is determined;

[0013] Among multiple reflective image features, search and match based on threshold image features, judge the quality of reflective glass beads based on the matching results, and obtain the correlation between the contact state between two adjacent reflective glass beads and the quality of the reflective glass beads;

[0014] The matching results are associated with the corresponding time series to obtain the abnormal time points of the reflective glass beads production line.

[0015] As a further preferred embodiment of the present technical solution, the method for obtaining the threshold image includes:

[0016] Collecting a primary spectral image, which is formed when qualified reflective glass beads are illuminated by the maximum imaging angle of the spectral source;

[0017] Collecting secondary spectral images, which are formed when qualified reflective glass beads are illuminated by the minimum imaging angle of the spectral source;

[0018] Gray-scaling the primary spectral image and the secondary spectral image, and obtaining a primary grayscale histogram and a secondary grayscale histogram respectively;

[0019] Stack the primary grayscale histogram and the secondary grayscale histogram, and obtain the threshold grayscale histogram;

[0020] The threshold grayscale histogram determines the threshold image based on the binarization process.

[0021] As a further preferred embodiment of the present invention, the method for determining the marking point includes:

[0022] Identify the property characteristics of reflective glass beads based on visual machines;

[0023] Collect spectral images generated by irradiating reflective glass beads with spectral sources at different angles;

[0024] According to the classification of attribute features, the corresponding matching points are located in the spectral image;

[0025] Select the two spectral images with the largest difference in spectral source illumination angle, and retrieve the positional relationship between the corresponding matching points in the two spectral images;

[0026] Based on the evaluation method, a landmark point is determined among multiple matching points.

[0027] As a further preferred embodiment of the present invention, the evaluation method includes:

[0028] Constructing a two-dimensional coordinate system to represent the specifications of the spectral image, wherein the horizontal and vertical coordinates of the two-dimensional coordinate system correspond to the size specifications of the spectral image respectively;

[0029] Mapping the spectral image into the constructed two-dimensional coordinate system;

[0030] Extract the coordinate position of each matching point in the spectral image in the two-dimensional coordinate system;

[0031] Select the two spectral images with the largest difference in spectral source illumination angle, and associate the corresponding matching points in the two spectral images;

[0032] Analyze the relationship between two corresponding matching points based on the spacing algorithm;

[0033] Based on the analysis results, the landmark points are identified among the numerous matching points.

[0034] As a further preferred embodiment of the present technical solution, the spacing algorithm includes:

[0035] ,in Indicates the spacing, Represent two points in n-dimensional space, and Represent points and point The coordinate value in the i-th dimension, Indicates the number of dimensions of the space, i represents the index of the dimension, from 1 to n, The parameter representing the Minkowski distance is a positive real number parameter, and The value range is any integer between 1 and 2. =1, is the Manhattan distance, when p=2, Degenerates to Euclidean distance.

[0036] As a further preferred embodiment of the present technical solution, the spacing algorithm includes: ,in represents the spacing, where Represent two points in n-dimensional space, Represents the covariance matrix of the data.

[0037] As a further preferred embodiment of the present technical solution, the contact state determination method includes:

[0038] According to the physical properties of reflective glass beads, the specification limits of the threshold image are set;

[0039] Based on the positioning of the marked points within the specification limits, a contrast image composed of two threshold images is obtained;

[0040] Extract the distance between two marked points from the comparison image as the discrimination distance range;

[0041] The contact status of adjacent reflective glass beads is determined by comparing the identification distance range with the distance between the two marking points in the reflection image.

[0042] If the distance between the two marking points in the reflected image is within the identification distance range, it is determined that the adjacent reflective glass beads are in contact;

[0043] If the distance between the two marking points in the reflected image exceeds the identification distance range, it is determined that the adjacent reflective glass beads are in a non-contact state.

[0044] As a further preferred embodiment of the present technical solution, the method for determining the quality of the reflective glass beads based on the matching results includes:

[0045] Construct an image standard feature library based on threshold image features;

[0046] Real-time collection of reflection image features and matching with the image standard feature library, and use of matching algorithms to obtain matching results;

[0047] Formulate quality grading rules based on the production indicators of reflective glass beads;

[0048] According to the corresponding relationship between the matching results and the quality grading rules, the quality grading of the reflective glass beads is determined;

[0049] The matching algorithm is ; They are used to represent the average pixel values ​​of image x and image y, representing the brightness. They are used to represent the pixel variance of image x and image y, respectively, to characterize the contrast. Used to represent the covariance of image x and image y, characterizing structural similarity, is a stable constant used to avoid the denominator being 0, where , , where L is the pixel dynamic range, which is 255. .

[0050] In a second aspect, to improve the above-disclosed rapid online detection method for reflective glass bead production, the present invention further provides a rapid online detection device for reflective glass bead production, wherein the rapid online detection device for reflective glass bead production utilizes the above-disclosed rapid online detection method for reflective glass bead production and comprises:

[0051] LED spectrum lamp, responsible for emitting a stable spectrum source to illuminate the reflective glass beads;

[0052] The vision machine is used to obtain threshold images and reflection images, and is responsible for collecting reflection images formed by two adjacent reflective glass beads under spectral illumination in different time series, and capturing the differences in the reflection images in each time series;

[0053] The positioning marking module accurately identifies the marking point in the acquired threshold image through the characteristics of the reflective glass beads themselves;

[0054] The contact state discriminator analyzes the positional relationship between two marking points in the reflected image to determine the contact state between two adjacent reflective glass beads;

[0055] The quality discrimination and correlation analyzer searches and matches based on threshold image features, and discriminates the quality of the reflective glass beads based on the matching results, and obtains the intrinsic correlation between the contact state between two adjacent reflective glass beads and the quality of the reflective glass beads;

[0056] The time series associator is used to associate the matching results with the corresponding time series and obtain the time points when abnormal conditions occur in the production process of the reflective glass beads production line.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] This rapid online inspection method and equipment for reflective glass beads production determines the marking points during inspection based on the characteristics of the reflective glass beads. This allows the position of the reflective glass beads to be located on a high-speed reflective glass bead production line by identifying the marking points instead of identifying the entire reflective glass beads.

[0059] In addition, by obtaining the distance between two adjacent marking points and the reflection image features of the two adjacent marking points in time series, the contact state between two adjacent reflective glass beads is determined, thereby avoiding misjudgment caused by mutual occlusion of reflective glass beads due to sticking;

[0060] At the same time, by building an image standard feature library based on threshold image features and collecting reflection image features in real time for matching with the image standard feature library, the quality of reflective glass beads can be quickly judged, ensuring detection efficiency and accuracy to a certain extent.

[0061] In addition, by obtaining the correlation between the contact status between two adjacent reflective glass beads and the quality of the reflective glass beads, strong support is provided for the quality control of the reflective glass bead production line. Finally, the matching results are associated with the corresponding time series through the time series associator to obtain the time points when abnormal conditions occur in the production process of the reflective glass bead production line, providing an important reference for troubleshooting and maintenance of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flowchart of the steps of the method disclosed in the present invention;

[0063] Figure 2 It is a simplified assembly diagram of the device disclosed in the present invention;

[0064] Figure 3 This is an auxiliary illustration of step S205.A of the present invention;

[0065] Figure 4 It is an auxiliary illustration of step S401 of the present invention. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0067] Before understanding the technical method proposed in the present invention, it should be understood that in the actual production process of reflective glass beads, since the material of the reflective glass beads is glass, two adjacent reflective glass beads will produce slight adhesion due to contact with each other after being formed and cooled. This adhesion not only affects the appearance quality of the reflective glass beads, but may also reduce their reflective performance and block the detection device from detecting the appearance of the reflective glass beads. For this reason, the present invention proposes a rapid online detection method and equipment for the production of reflective glass beads.

[0068] As a preferred implementation method, Figure 1 As shown, the present invention provides a technical solution: a rapid online detection method for reflective glass beads production, including: steps S100 to S600.

[0069] Step S100: Acquire a threshold image of reflective glass beads using a spectral source.

[0070] It should be clear that the reference Figure 2 It can be seen that there is an angle between the spectral source and the reflective glass beads.

[0071] Specifically, the method for obtaining the threshold image includes: steps S101 to S105.

[0072] Step S101: Acquire a primary spectral image.

[0073] It should be clear that the reference Figure 2 It can be seen that the primary spectral image is formed when qualified reflective glass beads are illuminated by the maximum imaging angle of the spectral source. In addition, it should be noted that qualified reflective glass beads in the present invention represent standard samples of reflective glass beads, and their physical properties, surface quality and size specifications all meet production requirements.

[0074] Step S102: Acquire a secondary spectral image.

[0075] It should be clear that the reference Figure 2 It can be seen that the secondary spectral image is formed when the qualified reflective glass beads are illuminated by the minimum imaging angle of the spectral source.

[0076] Step S103: grayscale the primary spectral image and the secondary spectral image, and obtain a primary grayscale histogram and a secondary grayscale histogram respectively.

[0077] It should be understood that the grayscale processing mentioned in step S103 is to convert the color value of each pixel in the primary spectral image and the secondary spectral image into a grayscale value by a computer, wherein the grayscale value range is usually between 0-255, where 0 represents black, 255 represents white, and the intermediate values ​​represent different degrees of gray. The primary grayscale histogram and the secondary grayscale histogram after the computer grayscale processing can reflect the grayscale distribution of qualified reflective glass beads at different imaging angles.

[0078] Step S104: stacking the primary grayscale histogram and the secondary grayscale histogram, and obtaining a threshold grayscale histogram.

[0079] It should be clear that the stacking method of the primary grayscale histogram and the secondary grayscale histogram in step S104 is to vertically superimpose the primary grayscale histogram and the secondary grayscale histogram on the grayscale value axis so that the two are displayed in the same coordinate system, thereby forming a threshold grayscale histogram. It should be added that the threshold grayscale histogram comprehensively reflects the grayscale distribution characteristics of qualified reflective glass beads at the maximum and minimum imaging angles, providing a key data basis for subsequent analysis and judgment.

[0080] Step S105: The threshold grayscale histogram is used to determine a threshold image based on the binarization process.

[0081] It should be noted that the binarization process in step S105 involves converting the grayscale values ​​in the threshold grayscale histogram into two selectable values ​​via a computer. The most common values ​​are 0 and 255, corresponding to black and white, respectively. The resulting image after this binarization process is the threshold image. This threshold image clearly shows the grayscale distribution boundaries of qualified reflective glass beads at different imaging angles, providing a key visual reference for the subsequent determination of the reflective glass bead quality.

[0082] Step S200: determining a marking point in the threshold image based on the characteristics of the reflective glass beads.

[0083] It should be noted that the characteristics of the reflective glass beads in step S200 specifically refer to the patterns inside the reflective glass beads or the built-in fillings used for decoration.

[0084] Furthermore, it should be added that, in the present invention, the method for determining the marking point in step S200 includes: steps S201 to S206.

[0085] Step S201: Identify the property characteristics of the reflective glass beads using a visual machine.

[0086] Specifically, the visual machine recognizes the property characteristics of the reflective glass beads as serving as a decorative filler.

[0087] Step S202: collecting spectral images generated by irradiating the reflective glass beads with a spectral source at different angles.

[0088] It should be understood that step S202 of the present invention collects spectral images of reflective glass beads under illumination from spectral sources at different angles, classifies them according to their properties, and accurately locates corresponding matching points in the spectral images. This paves the way for subsequent accurate determination of the various states and quality of the reflective glass beads, presents the relevant characteristics of the reflective glass beads under different lighting conditions, and provides the necessary data support for further in-depth analysis.

[0089] Step S203: locating corresponding matching points in the spectral image according to the classification of the attribute features.

[0090] Step S204: selecting two spectral images with the largest difference in spectral source illumination angles, and searching for the positional relationship between corresponding matching points in the two spectral images.

[0091] Step S205: Based on the evaluation method, determine the identification point among the multiple matching points.

[0092] It should be supplemented to step S205 that the evaluation method includes: step S205.A to step S205.F.

[0093] Step S205.A: Construct a two-dimensional coordinate system to represent the spectral image specifications.

[0094] It should be noted that the reference Figure 3 It can be seen that the horizontal and vertical coordinates of the disclosed two-dimensional coordinate system in step S205.A respectively correspond to the size specifications of the spectral image.

[0095] Step S205.B: Map the spectral image to the constructed two-dimensional coordinate system.

[0096] Step S205.C: extracting the coordinate position of each matching point in the spectral image in the two-dimensional coordinate system.

[0097] Step S205.D: Select two spectral images with the largest difference in spectral source illumination angle, and associate corresponding matching points in the two spectral images.

[0098] Step S205.E: Analyze the relationship between two corresponding matching points based on a spacing algorithm.

[0099] It should be clear that the relationship between the corresponding matching points in step S205.E can be divided into three types: close relationship, distant relationship and general relationship based on the spacing algorithm. Among them, the close relationship indicates that the two matching points are very close in spatial position, that is, the value is between 2 units, and the spectral feature similarity is high. The distant relationship is the opposite, indicating that the two matching points are relatively far apart in spatial position, that is, the value exceeds 3 units, and the spectral feature similarity is low. The general relationship is between the close relationship and the distant relationship, indicating that the two matching points have a certain degree of similarity in spatial position and spectral features, but are not particularly close or far away.

[0100] Step S205.F: Based on the analysis results, identify the landmark points among the numerous matching points.

[0101] It should be noted that, in order to enhance the accuracy of detection, step S205.F generally selects two corresponding matching points that are in close relationship as identification points for output.

[0102] As a preferred embodiment, the spacing algorithm disclosed in step S205.E includes: ,in Indicates the spacing, Represent two points in n-dimensional space, and Represent points and point The coordinate value in the i-th dimension, Indicates the number of dimensions of the space, i represents the index of the dimension, from 1 to n, The parameter representing the Minkowski distance is a positive real number parameter, and The value range is any integer between 1 and 2. =1, is the Manhattan distance, when p=2, Degenerates to Euclidean distance.

[0103] It should be noted that the reference Figure 3 It can be seen that since it is a two-dimensional coordinate system, the value of n is 2. The corresponding coordinate value and When is (1, 2) and (1, 3), let When the value is 1, =|1-1|+|2-3|=0+1=1, when When the value is 2, = ,At this point, it should be added that, since the value of the spacing is 1, the relationship between the two corresponding matching points is judged to be a close relationship.

[0104] As a preferred embodiment, in actual use, the spacing algorithm can also be ,in represents the spacing, where Represent two points in n-dimensional space, Represents the covariance matrix of the data.

[0105] It should be noted that the reference Figure 3 It can be seen that The coordinate values ​​are (1, 2) and (1, 3), then The value of , so the relationship between two corresponding matching points is judged to be a close relationship.

[0106] It should be further added that, in actual application, the first spacing algorithm focuses on calculating the spacing based on the coordinate difference between two points in n-dimensional space, so as to clarify the relative position relationship between the two points. The first spacing algorithm is intuitive and simple, and can play a good role in preliminary judgment. The second spacing algorithm introduces the covariance matrix of the data. The second spacing algorithm considers factors such as the correlation between the data to grasp the relative position and distribution characteristics of the data more carefully and accurately.

[0107] Step S300: collecting reflection images formed by spectra of two adjacent reflective glass beads in a time series.

[0108] It should be noted that, in step S300 , the reflection images in each time series are different.

[0109] Step S400: determining the contact state of two adjacent reflective glass beads based on the positions of two marking points in the reflected image.

[0110] It should be understood that the method for determining the contact state in step S400 of the present invention includes steps S401 to S406.

[0111] Step S401: setting specification limits of the threshold image according to the physical properties of the reflective glass beads.

[0112] It should be clear that the reference Figure 4 It can be seen that the physical properties in step S401 refer to the reflective range of the reflective glass beads plus the specifications of the reflective glass beads themselves, and the specification limit of the threshold image is obtained by superimposing the reflective range and the specifications themselves.

[0113] Step S402: obtaining a contrast image composed of two threshold images based on the positioning of the marking point within the specification limit.

[0114] Step S403: extracting the distance between the two marking points from the comparison image as the identification distance range.

[0115] Step S404: Determine the contact status of adjacent reflective glass beads by comparing the identification distance range with the distance between the two marking points in the reflected image.

[0116] Step S405: If the distance between the two marking points in the reflected image is within the identification distance range, it is determined that the adjacent reflective glass beads are in contact.

[0117] Step S406: If the distance between the two marking points in the reflected image exceeds the identification distance range, it is determined that the adjacent reflective glass beads are in a non-contact state.

[0118] Step S500: performing search and matching among multiple reflection image features according to the threshold image features.

[0119] It should be understood that, in step S500 , the matching result can determine the quality of the reflective glass beads and obtain the correlation between the contact state between two adjacent reflective glass beads and the quality of the reflective glass beads.

[0120] Specifically, the method for determining the quality of reflective glass beads based on the matching results includes: steps S501 to S504.

[0121] Step S501: constructing an image standard feature library based on threshold image features.

[0122] It should be noted that the threshold image features in step S501 include: geometric features (diameter, roundness), optical features (reflectivity, light intensity distribution) and texture features (surface defects and internal decorative fillings) of the reflective glass beads.

[0123] Step S502: collecting reflection image features in real time and matching them with the image standard feature library, and obtaining matching results using a matching algorithm.

[0124] Step S503: Formulate quality grading rules based on the production indicators of the reflective glass beads.

[0125] It should be noted that in step S503, the quality grading rules are divided into qualified products (matching degree greater than or equal to 90%), repaired products (matching degree between 80% and 90%) and unqualified products (matching degree less than 80%) based on the production indicators.

[0126] Step S504: performing quality grading on the reflective glass beads according to the corresponding relationship between the matching result and the quality grading rules.

[0127] As a preferred embodiment, the matching algorithm in step S502 is ; They are used to represent the average pixel values ​​of image x and image y, representing the brightness. They are used to represent the pixel variance of image x and image y, respectively, to characterize the contrast. Used to represent the covariance of image x and image y, characterizing structural similarity, is a stable constant used to avoid the denominator being 0, where , , where L is the pixel dynamic range, which is 255. .

[0128] It should be noted that the value range of the value obtained by the matching algorithm is (-1, 1). Therefore, the obtained value is matched with the quality grading rules through the percentage of the difference with the value range of (-1, 1), and then the quality grade of the reflective glass beads is determined. For example, if the obtained value is greater than or equal to the preset qualified threshold (such as the difference percentage corresponding to 0.9), it can be judged as a qualified product; if the value is within the preset restoration threshold range (such as the corresponding 0.8 to 0.9 difference percentage range), it is judged as a restoration product; if the value is less than the preset unqualified threshold (such as less than the corresponding 0.8 difference percentage), it is judged as an unqualified product.

[0129] Specifically, when the pixel average of image x and image y is 200, it represents moderate brightness; when the pixel variance of image x and image y is 50, it represents moderate contrast; when the covariance of image x and image y is 30, then , =30, = =50. Therefore , ,so , so the obtained similarity is 74.75%, which is a substandard product.

[0130] Step S600: Correlate the matching results with the corresponding time series to obtain abnormal time points of the reflective glass bead production line.

[0131] As a preferred embodiment, refer to Figure 2 It can be seen that the present invention also proposes a rapid online detection device for the production of reflective glass beads. It should be understood that the rapid online detection device for the production of reflective glass beads uses the rapid online detection method for the production of reflective glass beads disclosed above and includes:

[0132] LED spectrum lamp, responsible for emitting a stable spectrum source to illuminate the reflective glass beads;

[0133] The vision machine is used to obtain threshold images and reflection images, and is responsible for collecting reflection images formed by two adjacent reflective glass beads under spectral illumination in different time series, and capturing the differences in the reflection images in each time series;

[0134] The positioning marking module accurately identifies the marking point in the acquired threshold image through the characteristics of the reflective glass beads themselves;

[0135] The contact state discriminator analyzes the positional relationship between two marking points in the reflected image to determine the contact state between two adjacent reflective glass beads;

[0136] The quality discrimination and correlation analyzer searches and matches based on threshold image features, and discriminates the quality of the reflective glass beads based on the matching results, and obtains the intrinsic correlation between the contact state between two adjacent reflective glass beads and the quality of the reflective glass beads;

[0137] The time series associator is used to associate the matching results with the corresponding time series and obtain the time points when abnormal conditions occur in the production process of the reflective glass beads production line.

[0138] It should be added that, in actual use of the present invention, the LED spectrum lamp is installed above the reflective glass bead production line to ensure that the spectrum light source can evenly illuminate each reflective glass bead, and the vision machine is installed on one side of the LED spectrum lamp. The reflection image and threshold image are captured by a high-precision camera. The core components such as the positioning mark module, contact state discriminator, quality discriminator and correlation analyzer, and time series correlator are integrated in an intelligent control unit. The intelligent control unit is installed in the vision machine. During actual operation, the intelligent control unit can quickly process and analyze the collected image data.

[0139] In addition, it should be noted that in actual operation, when reflective glass beads pass through the production line, the LED spectrum light will emit a stable spectral source to illuminate the glass beads, and the visual machine will then capture the reflected image and threshold image. The positioning mark module will identify the characteristic mark points of each reflective glass bead, and the contact state discriminator will analyze the positional relationship of these mark points to determine the contact state between adjacent glass beads. Then, the quality discrimination and correlation analyzer will retrieve the matching results based on the threshold image features, discriminate the quality of the reflective glass beads, and deeply analyze the intrinsic correlation between the contact state and quality. Finally, the time series associator will associate these analysis results with the corresponding time series. Once an abnormality is detected on the production line, it can be immediately located at the specific time point, facilitating timely processing and adjustment by staff.

[0140] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is limited by the accompanying embodiments and their equivalents.

Claims

1. A rapid online detection method for the production of reflective glass beads, characterized in that: include: A threshold image of reflective glass beads is obtained using spectral source illumination; Based on the characteristics of the reflective glass beads, the marking points are determined in the threshold image; The reflection images formed by the spectrum of two adjacent reflective glass beads in the acquisition time series are different in each time series. Based on the positions of two marking points in the reflected image, the contact state of two adjacent reflective glass beads is determined; Among multiple reflective image features, search and match based on threshold image features, judge the quality of reflective glass beads based on the matching results, and obtain the correlation between the contact state between two adjacent reflective glass beads and the quality of the reflective glass beads; Correlate the matching results with the corresponding time series to obtain the abnormal time points of the reflective glass beads production line; Methods for determining marker points include: Identify the property characteristics of reflective glass beads based on visual machines; Collect spectral images generated by irradiating reflective glass beads with spectral sources at different angles; According to the classification of attribute features, the corresponding matching points are located in the spectral image; Select the two spectral images with the largest difference in spectral source illumination angle, and retrieve the positional relationship between the corresponding matching points in the two spectral images; Based on the evaluation method, the identification point is determined among multiple matching points; The evaluation method includes: Constructing a two-dimensional coordinate system to represent the specifications of the spectral image, wherein the horizontal and vertical coordinates of the two-dimensional coordinate system correspond to the size specifications of the spectral image respectively; Mapping the spectral image into the constructed two-dimensional coordinate system; Extract the coordinate position of each matching point in the spectral image in the two-dimensional coordinate system; Select the two spectral images with the largest difference in spectral source illumination angle, and associate the corresponding matching points in the two spectral images; Analyze the relationship between two corresponding matching points based on the spacing algorithm; Based on the analysis results, the landmark points are identified among the numerous matching points.

2. The rapid online detection method for reflective glass beads according to claim 1, characterized in that: Methods for obtaining a threshold image include: Collecting a primary spectral image, which is formed when qualified reflective glass beads are illuminated by the maximum imaging angle of the spectral source; Collecting secondary spectral images, which are formed when qualified reflective glass beads are illuminated by the minimum imaging angle of the spectral source; Gray-scaling the primary spectral image and the secondary spectral image, and obtaining a primary grayscale histogram and a secondary grayscale histogram respectively; Stack the primary grayscale histogram and the secondary grayscale histogram, and obtain the threshold grayscale histogram; The threshold grayscale histogram determines the threshold image based on the binarization process.

3. The rapid online detection method for reflective glass bead production according to claim 1, characterized in that: The spacing algorithm includes: ,in Indicates the spacing, Represent two points in n-dimensional space, and Represent points and point The coordinate value in the i-th dimension, Indicates the number of dimensions of the space, i represents the index of the dimension, from 1 to n, The parameter representing the Minkowski distance is a positive real number parameter, and The value range is any integer between 1 and 2. =1, is the Manhattan distance, when p=2, Degenerates to Euclidean distance.

4. The rapid online detection method for reflective glass beads production according to claim 1, characterized in that: The spacing algorithm includes: ,in represents the spacing, where Represent two points in n-dimensional space, Represents the covariance matrix of the data.

5. The rapid online detection method for reflective glass bead production according to claim 1, characterized in that: The contact status determination methods include: According to the physical properties of reflective glass beads, the specification limits of the threshold image are set; Based on the positioning of the marked points within the specification limits, a contrast image composed of two threshold images is obtained; Extract the distance between two marked points from the comparison image as the discrimination distance range; The contact status of adjacent reflective glass beads is determined by comparing the identification distance range with the distance between the two marking points in the reflection image. If the distance between the two marking points in the reflected image is within the identification distance range, it is determined that the adjacent reflective glass beads are in contact; If the distance between the two marking points in the reflected image exceeds the identification distance range, it is determined that the adjacent reflective glass beads are in a non-contact state.

6. The rapid online detection method for reflective glass bead production according to claim 1, characterized in that: Methods for judging the quality of reflective glass beads based on matching results include: Construct an image standard feature library based on threshold image features; Real-time collection of reflection image features and matching with the image standard feature library, and use of matching algorithms to obtain matching results; Formulate quality grading rules based on the production indicators of reflective glass beads; According to the corresponding relationship between the matching results and the quality grading rules, the quality grading of the reflective glass beads is determined; The matching algorithm is ; They are used to represent the average pixel values ​​of image x and image y, representing the brightness. They are used to represent the pixel variance of image x and image y, respectively, to characterize the contrast. Used to represent the covariance of image x and image y, characterizing structural similarity, is a stable constant used to avoid the denominator being 0, where , , where L is the pixel dynamic range, which is 255. .

7. A rapid online detection device for the production of reflective glass beads, using the rapid online detection method for the production of reflective glass beads according to any one of claims 1 to 6, characterized in that: include: LED spectrum lamp, responsible for emitting a stable spectrum source to illuminate the reflective glass beads; The vision machine is used to obtain threshold images and reflection images, and is responsible for collecting reflection images formed by two adjacent reflective glass beads under spectral illumination in different time series, and capturing the differences in the reflection images in each time series; The positioning marking module accurately identifies the marking point in the acquired threshold image through the characteristics of the reflective glass beads themselves; The contact state discriminator analyzes the positional relationship between two marking points in the reflected image to determine the contact state between two adjacent reflective glass beads; The quality discrimination and correlation analyzer searches and matches based on threshold image features, and discriminates the quality of the reflective glass beads based on the matching results, and obtains the intrinsic correlation between the contact state between two adjacent reflective glass beads and the quality of the reflective glass beads; The time series associator is used to associate the matching results with the corresponding time series and obtain the time points when abnormal conditions occur in the production process of the reflective glass beads production line.

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