CN114485409A - Raw material plate quality detection method, device and equipment and readable storage medium - Google Patents
Raw material plate quality detection method, device and equipment and readable storage medium Download PDFInfo
- Publication number
- CN114485409A CN114485409A CN202210401943.XA CN202210401943A CN114485409A CN 114485409 A CN114485409 A CN 114485409A CN 202210401943 A CN202210401943 A CN 202210401943A CN 114485409 A CN114485409 A CN 114485409A
- Authority
- CN
- China
- Prior art keywords
- slurry
- image
- raw material
- material plate
- target area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
-
- G—PHYSICS
- G02—OPTICS
- G02F—OPTICAL DEVICES OR ARRANGEMENTS FOR THE CONTROL OF LIGHT BY MODIFICATION OF THE OPTICAL PROPERTIES OF THE MEDIA OF THE ELEMENTS INVOLVED THEREIN; NON-LINEAR OPTICS; FREQUENCY-CHANGING OF LIGHT; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS
- G02F1/00—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics
- G02F1/01—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour
- G02F1/13—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour based on liquid crystals, e.g. single liquid crystal display cells
- G02F1/1306—Details
- G02F1/1309—Repairing; Testing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30121—CRT, LCD or plasma display
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Nonlinear Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Crystallography & Structural Chemistry (AREA)
- Optics & Photonics (AREA)
- Quality & Reliability (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The application discloses a raw material plate quality detection method, a raw material plate quality detection device, raw material plate quality detection equipment and a readable storage medium, and belongs to the technical field of display. According to the method and the device, the slurry parameters in the target area image of the slurry on the raw material plate coated with the slurry are obtained, whether the target area image meets the standard or not is determined based on the slurry parameters, and if the target area image meets the standard, the raw material plate is judged to be qualified. Realized in this application, obtain the image and the thick liquids parameter of thick liquids from the raw materials board, compare the thick liquids parameter and predetermine the similarity between the thick liquids parameter threshold value, confirm on the raw materials board whether the thick liquids effect of scribbling accords with the standard, the thick liquids scribbles the effect and influences the electrically conductive effect of thick liquids on the raw materials board, also influencing the quality of raw materials board simultaneously, scribble the effect through scribbling thick liquids parameter and thick liquids on the raw materials board and judge, scribble the effect and reach technological standard with the thick liquids of guaranteeing on the raw materials board, and then guaranteed the quality of raw materials board.
Description
Technical Field
The application relates to the technical field of display, in particular to a raw material plate quality detection method, a raw material plate quality detection device, raw material plate quality detection equipment and a readable storage medium.
Background
With the development of the liquid crystal display screen technology, the application scenes of the liquid crystal display screen are more extensive, the imaging of the liquid crystal display screen must depend on a polaroid, static electricity inevitably accumulates on the polaroid when the liquid crystal display screen is used, and in order to prevent the display screen from being punctured due to overlarge static electricity, slurry is required to be added on the polaroid, and the slurry is conductive silver paste generally and guides the static electricity on the polaroid to a ground wire.
Generally, when slurry is coated on a raw material plate of a polarizer, the circuit directions of different types of raw material plates are different, so that different slurry coating effects can be generated, and the shape and position of the slurry can be determined according to the circuit directions on the raw material plates.
Simultaneously, the overall shape and the specific smearing position of the slurry can influence the conductive effect of the slurry on the raw material plate, when the slurry is smeared on the raw material plate, the problems of uneven smearing and position deviation smearing of the slurry exist, the slurry smearing effect and the preset effect are different, the effect that the slurry is smeared on the raw material plate cannot be ensured to meet the technological standard, the quality of raw materials cannot be guaranteed, and the quality level of the liquid crystal display screen is uneven.
Disclosure of Invention
The application mainly aims to provide a raw material plate quality detection method, a raw material plate quality detection device, raw material plate quality detection equipment and a readable storage medium, and aims to solve the technical problem of how to improve the quality of a liquid crystal display screen.
In order to achieve the above purpose, the present application provides a raw material plate quality detection method, which includes obtaining slurry parameters in an image of a target area of slurry on a raw material plate coated with slurry;
determining whether the target area image meets a criterion based on the slurry parameters;
and if the standard is met, judging that the raw material plate is qualified.
Illustratively, the acquiring of the slurry parameters in the image of the target area of the slurry on the raw material plate coated with the slurry comprises:
acquiring a product image of a raw material plate;
determining an initial region image from the product image based on a preset ROI;
performing interference removal processing on the initial region image to obtain a target region image;
and measuring the data of the slurry in the target area image to obtain slurry parameters.
Illustratively, the determining an initial region image from the product image based on the preset ROI includes:
determining a profile of the slurry from the product image based on a preset ROI;
and extracting the image of the slurry from the product image based on the contour to obtain an initial region image.
Illustratively, the performing interference removal processing on the initial region image to obtain a target region image includes:
based on a preset scaling coefficient, amplifying the initial region image to obtain an edge image of the initial region image;
identifying interference pixel points from the edge image to obtain an interference image outside the initial region image; the interference image is composed of the interference pixel points;
and removing the interference image from the initial region image to obtain a target region image.
For example, the measuring data of the slurry in the target area image to obtain the slurry parameter includes:
scanning the target area image to obtain the distribution condition of the slurry;
and measuring the data of the slurry in the target area image based on the distribution condition to obtain slurry parameters.
Illustratively, the slurry parameters include a position parameter and a shape parameter, and the measuring data of the slurry in the target area image based on the distribution situation to obtain the slurry parameters includes:
based on the distribution condition, segmenting the shape of the target area image to obtain an image convenient for measurement and calculation;
measuring and calculating the shape of the image to obtain shape parameters of the target area image;
determining positioning points in the target area image based on the distribution condition;
and measuring and calculating the relative position between the positioning point and a preset reference point to obtain a position parameter.
Illustratively, the determining whether the target area image meets a criterion based on the slurry parameter includes:
comparing the similarity between the slurry parameters and a preset slurry parameter threshold value;
and determining whether the target area image is qualified or not based on the similarity.
Illustratively, to achieve the above object, the present application also provides a raw material sheet quality inspection apparatus, characterized in that the raw material sheet quality inspection apparatus includes:
the first acquisition module is used for acquiring slurry parameters in a target area image of slurry on the raw material plate;
a first determination module for determining whether the region image meets a criterion based on the slurry parameter;
and the first judging module is used for judging that the raw material plate coated with the slurry is qualified if the standard is met.
Illustratively, to achieve the above object, the present application also provides a raw material sheet quality inspection apparatus, characterized in that the apparatus comprises: a memory, a processor, and a raw material plate quality inspection program stored on the memory and executable on the processor, the raw material plate quality inspection program configured to implement the steps of the raw material plate quality inspection method as described above.
In an exemplary manner, to achieve the above object, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores thereon a raw material plate quality detection program, and the raw material plate quality detection program, when executed by a processor, implements the steps of the raw material plate quality detection method as described above.
With prior art, when scribbling thick liquids man-hour to the former block, there is thick liquids to scribble inhomogeneous the problem that leads to thick liquids to have shape defect to and thick liquids scribble the condition that the position compares the deviation with preset position, lead to the quality level of former block to differ, and then lead to having used the not good problem of liquid crystal display quality of substandard product former block to compare. In the application, a raw material plate quality detection method is provided, the slurry coating effect on a raw material plate is a factor influencing the quality of the raw material plate, namely the shape and the position of slurry coating can influence the quality of the raw material plate, the quality of the raw material plate is detected, namely the slurry coating effect on the raw material plate is detected, firstly, a target area image is obtained from the raw material plate coated with the slurry, slurry parameters are obtained from the target area image, secondly, the slurry parameters are analyzed and judged to obtain a judgment result, if the slurry parameters meet the standard, the target area image is judged to meet the standard, namely, the shape and the position of the slurry meet the standard, and therefore the quality of the raw material plate is judged to be qualified. Therefore, in the application, whether the coating effect of the slurry on the raw material plate meets the standard or not is judged by the method for determining whether the slurry parameters meet the standard or not, and the shape and the position of the slurry on the raw material plate are ensured to meet the standard, so that the quality of the raw material plate is ensured to meet the standard, and the quality of the liquid crystal display is ensured to meet the standard.
Drawings
FIG. 1 is a schematic flow chart of a first embodiment of a raw material sheet quality inspection method according to the present application;
FIG. 2 is a schematic flow chart illustrating a second embodiment of a method for inspecting quality of a raw material sheet according to the present application;
FIG. 3 is a schematic diagram of a positional relationship between an image of a product and an ROI according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a hardware operating environment according to an embodiment of the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
An embodiment of the present application provides a raw material plate quality detection method, and referring to fig. 1, fig. 1 is a schematic flow diagram of a first embodiment of a raw material plate quality detection method according to the present application.
While the embodiments of the present application provide examples of raw material sheet quality inspection methods, it should be noted that while a logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in an order different than that shown or described herein. For convenience of description, the following omits the execution of the steps of the subject description raw material sheet quality inspection method, which includes:
step S110, slurry parameters in the target area image of the slurry on the raw material plate coated with the slurry are obtained.
The raw material plate comprises three layers of structures, an upper-layer polaroid, a middle-layer color filter and a lower-layer thin film transistor, static electricity can be continuously accumulated in the use process of the polaroid, the excessive static electricity accumulation can cause the liquid crystal display screen to be subjected to static electricity breakdown, the slurry needs to be smeared on the raw material plate, the slurry is conductive silver paste generally, the static electricity on the polaroid plate is guided to a grounding wire through the connection of the slurry and a circuit, the circuit trends on different raw material plates are different, the shape of the slurry is different, and different modes are measured, calculated and determined according to the slurries with different shapes.
Illustratively, the line direction on the polarizer plate is L-shaped, and the conductive paste applied according to the line direction is L-shaped.
For example, the general-purpose polarizer plate has a line direction which is distributed in a dot shape, and the applied conductive paste is dot paste composed of an upper semicircle and a lower semicircle.
The target area image is obtained by processing the image of the slurry area on the raw material plate, and the quality of the image processing of the slurry area influences the detection result of the processed target area image, so that the processing precision is improved, the accuracy of the target area image is improved, and the detection result of the slurry area is improved conveniently.
Illustratively, profile selection is carried out on a product image of a raw material plate, the profile of a slurry area is determined, the profile is taken as a boundary line, image information inside the profile is extracted, a complete image of the slurry area is obtained, morphological processing is carried out on the image, burrs existing on the outer edge of the image of the slurry area are removed, and a target area image is obtained. The morphological image processing is a technology for analyzing an image by a computer to achieve a required result by using basic operations of mathematical morphology, wherein the morphological image processing refers to a series of image processing technologies for processing shape features of the image, and the morphology is to measure or extract corresponding shapes or features in an input image by using a special structural element so as to further perform image analysis and target recognition.
Illustratively, the product image of the raw material plate is subjected to luminance thresholding, which classifies all luminance values in the image into two categories, higher than a threshold value and lower than the threshold value, according to a specified luminance value. The black-white mask image generated by the method can separate ground objects with large contrast difference, a brightness threshold value is set according to different brightness of the raw material plate and the slurry due to different materials, the brightness value of the image is adjusted to obtain an image of the slurry area, and the image filtering processing is carried out on the interference area at the outer edge of the image of the slurry area to obtain an image of the target area. The image filtering, namely, the suppression of the noise of the target image under the condition of keeping the detailed features of the image as much as possible, is an indispensable operation in the image preprocessing, and the effectiveness and reliability of the subsequent image processing and analysis are directly affected by the quality of the processing effect.
The difference of the slurry distribution condition leads to the difference of the conductive effect of the slurry, and then leads to the difference of the quality of the raw material plate, the distribution condition which influences the conductive effect of the slurry has the overall shape of the slurry, the specific position of the slurry on the raw material plate, the distance between the slurry area and the polaroid, and when the target area image is detected, the slurry parameters are obtained, wherein the slurry parameters comprise shape parameters and position parameters.
Illustratively, when detecting a target area image of the dot-shaped slurry, the distribution form of the dot-shaped slurry is generally an upper semicircle and a lower semicircle, the shape of the target area image is calculated to obtain the shape parameters of the target area image, and the integrity of the target area image is determined according to the shape data.
Illustratively, when detecting a point-shaped target area image, based on a preset reference point at the position of a polarizer in a product image, setting the point-shaped slurry to be a positioning point closest to the polarizer point, obtaining a position parameter of the target area image by measuring and calculating a relative position between the positioning point and the preset reference point, and judging whether the position of the target area image has deviation according to the position parameter.
Step S120: determining whether the target area image meets a criterion based on the slurry parameters.
The slurry parameters comprise shape parameters and position parameters of slurry, different slurry parameter thresholds are preset on different raw material plates, the similarity between the slurry parameter thresholds and the slurry parameters is compared by taking the slurry parameter thresholds as a reference, whether the slurry parameters of the target area image meet the standard or not is judged according to the comparison result, and the target area image can be determined to meet the standard only if the shape parameters and the position parameters of the target area image both pass the judgment.
Illustratively, the shape parameters of the target area image include the length, width and area of the slurry, taking the dot slurry as an example, the shape of the dot slurry is two semicircles of an upper part and a lower part, the shape parameters of the dot slurry include the length, width and area of the upper semicircle and the length, width and area of the lower semicircle, and the measured shape parameters and the preset slurry parameters are compared to determine whether the shape parameters of the target area image meet the standard.
Illustratively, the position parameters of the target area image include a geometric center position parameter of the slurry, the geometric center position parameter is used for judging the deviation of the target area image in the horizontal direction and a position parameter at the highest position of the target area image, the position parameter at the highest position is used for judging the deviation of the target area image in the vertical direction, and the measured shape parameter and the preset slurry parameter are compared to judge whether the position parameter of the target area image meets the standard.
Step S130: and if the standard is met, judging that the raw material plate is qualified.
By judging whether the slurry parameters meet the standard or not, when the shape parameters and the position parameters of the target area image both meet the preset slurry parameters, the target area image is judged to meet the production requirements, and then the quality of the raw material plate is judged to meet the process production requirements, so that the quality of the liquid crystal display screen is ensured.
With prior art, when scribbling thick liquids man-hour to the former block, there is thick liquids to scribble inhomogeneous the problem that leads to thick liquids to have shape defect to and thick liquids scribble the condition that the position compares the deviation with preset position, lead to the quality level of former block to differ, and then lead to having used the not good problem of liquid crystal display quality of substandard product former block to compare. In the application, a raw material plate quality detection method is provided, the slurry coating effect on a raw material plate is a factor influencing the quality of the raw material plate, namely the shape and the position of slurry coating can influence the quality of the raw material plate, the quality of the raw material plate is detected, namely the slurry coating effect on the raw material plate is detected, firstly, a target area image is obtained from the raw material plate coated with the slurry, slurry parameters are obtained from the target area image, secondly, the slurry parameters are analyzed and judged to obtain a judgment result, if the slurry parameters meet the standard, the target area image is judged to meet the standard, namely, the shape and the position of the slurry meet the standard, and therefore the quality of the raw material plate is judged to be qualified. Therefore, in the application, whether the coating effect of the slurry on the raw material plate meets the standard or not is judged by the method for determining whether the slurry parameters meet the standard or not, and the shape and the position of the slurry on the raw material plate are ensured to meet the standard, so that the quality of the raw material plate is ensured to meet the standard, and the quality of the liquid crystal display is ensured to meet the standard.
Exemplarily, referring to fig. 2, fig. 2 is a schematic flow chart of a second embodiment of the raw material plate quality inspection method of the present application, and the second embodiment is proposed based on the above first embodiment of the raw material plate quality inspection method of the present application, and the method further includes:
step S210: acquiring a product image of a raw material plate;
the product image of the raw material plate comprises an image of the slurry, a plate image and a useless image area of the raw material plate, wherein the raw material plate image comprises the following parts: the preset positioning mark points and the preset reference points on the raw material plate, and the useless area images are used for avoiding the loss of subsequent image processing information caused by the undersize of the acquired images. When the product image of the raw material plate is obtained, the integrity of the product image is ensured, and the subsequent processing and detection of the product image are facilitated.
Illustratively, the sheet image of the raw material sheet mainly includes three images of an upper polarizer, a middle color filter, and a lower thin film transistor.
For example, a positioning mark point and a reference point are preset on three layers of plates of the raw material plate, the positioning mark point is used for image acquisition and positioning, and the reference point is used for measuring and calculating the position parameter of the image of the slurry.
Referring to fig. 3, fig. 3 is a schematic diagram of a positional relationship between a product image and a ROI, as shown in fig. 3, the product image includes, from top to bottom: polaroid, color filter and thin film transistor, thick liquids are paintd on thin film transistor and color filter, and have the distance between the top peak of thick liquids and the polaroid, and the ROI frame selection scope has included from last to down: images of polarizer lower border, color filter, thin film transistor, and paste.
Step S220: determining an initial region image from the product image based on a preset ROI;
the ROI (Region Of Interest) is a Region to be processed, which is defined in a frame, circle, ellipse, or irregular polygon form from a processed image in machine vision and image processing, and is called a Region Of Interest, which is abbreviated as ROI. The ROI is typically used for framing and subsequent correlation of the image in the framed region. When the image is selected, the image to be selected is ensured to be completely overlapped with the ROI.
Illustratively, after the product image is acquired, the image of the slurry is framed from the product image, and the framed area of the ROI area completely coincides with the image area of the slurry, specifically: and keeping the coincidence state of the selected ranges of the left, the lower and the right three borders of the ROI and the image range of the slurry, and correspondingly adjusting the upper border of the ROI according to the image height of the actual slurry.
For example, after the product image is acquired, in the image selected by the ROI frame, the border of the ROI interferes with the image of the slurry, that is, the image range of the slurry exceeds the range defined by the border of the ROI, and then the parameters selected by the ROI frame are replaced.
Illustratively, when a raw material plate is conveyed to a detection area, the raw material plate can generate deflection problems in the moving process, namely, an image of slurry of the raw material plate can deviate from the detection area, so that the image information acquired by the detection area is not accurate, in order to ensure that an image selected by an ROI frame is accurate, feature search is started, the ROI area is adjusted based on a machine vision positioning method, the ROI area is ensured to be overlapped with the image of the slurry, a positioning identification template is manufactured, an ROI is preset on the positioning identification template, the positioning identification template is used for identifying positioning mark points in a product image, the position of the positioning identification template is compensated and adjusted according to the specific positions of the positioning mark points, and the adjusted ROI frame selection area is overlapped with the image of the slurry.
Illustratively, the positioning identification template is provided with identification points at fixed positions, the position of the positioning identification template is identified, the deflection angle value between the positioning identification template and the positioning identification template is measured and calculated, the positioning identification template is adjusted according to the deflection angle value, and the identification points in the positioning identification template and the positioning identification points are ensured to keep the coincident positions, namely the preset ROI on the positioning identification template is ensured to be coincident with the image of the slurry.
Illustratively, the image of the slurry is the shape of the slurry smeared on the raw material plate, and includes the area size and the shape distribution of the slurry, the slurry includes dot-shaped slurry, L-shaped slurry and the like according to the shape classification, and taking the shape of the slurry as dot as an example, the dot-shaped slurry distribution shape is an upper semicircle and a lower semicircle, when the product image is obtained, the image of the slurry is ensured to be completely collected, that is, the shape parameter of the slurry is collected.
Illustratively, the pulp is classified according to shape, including point pulp, L-shaped pulp and the like, and taking the shape of the pulp as an L-shape as an example, the distribution shape of the L-shaped pulp is a two-part rectangle in the horizontal and vertical directions.
Illustratively, the determining an initial region image from the product image based on the preset ROI includes:
step a: determining a profile of the slurry from the product image based on a preset ROI;
illustratively, in extracting an image of the slurry, the length range of the slurry: limiting the position range of the slurry in the vertical direction in a visual field by 0-960; width range of slurry: the position range of the slurry in the horizontal direction in the visual field is limited, and is 0-1280; area range of the slurry: the unit is pixel, the lower limit is set to 1000, and the upper limit is set to 65000;
illustratively, before processing the slurry area, the number of pixels at the image height of the slurry is set to 123.
Step b: and extracting the image of the slurry from the product image based on the contour to obtain an initial region image.
Illustratively, an image of the slurry in the product image is extracted, the contour of the slurry is determined based on the image shape of the slurry, and the range outlined by the contour is used as the shape of the slurry, so as to obtain an initial area image.
Illustratively, the brightness threshold segmentation processing is performed on the image of the slurry in the product image, the brightness in the product image is adjusted based on the preset brightness threshold according to the difference between the brightness values of the slurry and the raw material plate, the brightness of the image lower than the preset brightness threshold is adjusted to be reduced, the brightness of the image higher than the preset brightness threshold is adjusted to be increased, the image of the slurry in the product image and the image of the raw material plate are segmented, and the black image in the shape of the slurry is obtained, namely the initial region image.
Step S230: and carrying out interference removal processing on the initial area image to obtain a target area image.
The method comprises the steps of obtaining a product image of a raw material plate, carrying out subsequent processing on the product image, obtaining an initial region image from the product image when ROI framing is carried out, wherein the initial region image comprises an image of slurry and an interference image on the outer side of the image, carrying out interference removing processing on the initial region image after the initial region image is collected, and obtaining a target region image, wherein the target region image comprises slurry parameters to be measured and calculated.
Illustratively, based on the preset ROI, an image of a slurry area is selected from a frame in a product image, the shape of the slurry is extracted from the image of the slurry area, and the complete slurry shape is extracted, so that the subsequent measurement and calculation of the slurry shape are facilitated, and the shape parameter of the slurry is obtained.
Illustratively, based on an initial region image, performing interference elimination processing on the initial region image, and in the process of image acquisition, inevitably generating an interference image, where the interference image is generated by pixel points outside the edge of the initial region image, and is usually burrs outside the edge of the initial region image, and based on the edge of the initial region image, removing the interference region at the edge of the initial region image to obtain a target region image.
The method comprises the following steps of processing a product image in two steps, extracting an image of slurry in the product image to obtain an initial area image, and performing interference removal processing on the initial area image to obtain a target area image.
Illustratively, the performing interference removal processing on the initial region image to obtain a target region image includes:
step c: based on a preset scaling coefficient, amplifying the initial region image to obtain an edge image of the initial region image;
for example, when performing brightness threshold segmentation on a product image, a brightness value adjustment range is defined by a range of slurry, the slurry is imaged in black, the lower limit of the brightness value is set to be 0, the upper limit of the brightness value is set to be dependent on the contrast between the slurry and the surrounding area, and the brightness value is maximum 255.
Illustratively, after the initial area image is obtained, based on a preset scaling factor, the scaling factor is used for scaling the image of the slurry, and when the preset scaling factor is larger, the detected interference image is clearer, and the effect of performing interference removal processing on the initial area image is better.
Step d: identifying interference pixel points from the edge image to obtain an interference image outside the initial region image; the interference image is composed of the interference pixel points;
illustratively, after an initial region image is obtained, the initial region image is amplified based on a preset scaling coefficient, when the initial region image is processed, burrs on the outer side of the edge of the initial region image are all judged to be interference images, the interference images formed by accumulating pixel points are identified, filtering processing is carried out on the interference images, noise of a target image is suppressed under the condition that image detail features are reserved as much as possible, and the effect of improving the image features is better when the filtering is larger.
Illustratively, when the initial region image is subjected to interference elimination processing, morphological image processing is performed on the initial region image based on the edge of the initial region image, so that image interference outside the edge of the initial region image is reduced, and the feature details of the image are improved based on computer analysis.
Step e: and removing the interference image from the initial region image to obtain a target region image.
Illustratively, the initial region image is filtered to obtain a target region image with the interference image removed.
Illustratively, morphological image processing is performed on the initial region image to obtain a target region image from which the interference image is removed.
Step S240: measuring the data of the slurry in the target area image to obtain slurry parameters;
when the target area image is scanned, the areas of the plate material and the slurry of the raw material plate are scanned, the distribution condition of the target area image is obtained, the distribution condition comprises the shape distribution and the position distribution of the target area image, and the slurry parameter can be measured based on the distribution condition.
Exemplary slurries include spot-like slurries and L-type slurries.
When the parameters of the point-shaped slurry are measured, the whole point-shaped slurry is measured to obtain the parameters of the whole position of the point-shaped slurry, the shape of the point-shaped slurry is distributed into an upper semicircle and a lower semicircle, the whole point-shaped slurry is divided into the upper semicircle and the lower semicircle, the upper semicircle and the lower semicircle are independently measured, and the divided partial parameters are measured.
For the L-shaped slurry, when the parameters of the L-shaped slurry are measured, the whole L-shaped slurry is measured to obtain the parameters of the whole position of the L-shaped slurry, the shape distribution of the L-shaped slurry can be divided into an upper part and a lower part which are a rectangle in the horizontal direction and a rectangle in the vertical direction, and the parameters of the two rectangles are measured.
The shape of the sizing agent smeared on the raw material plate can follow the change of the wiring direction on the raw material plate, different sizing agents with different shapes can be smeared on different raw material plates, the wiring positions covered are different, the position distribution of the sizing agent can also generate different changes, whether the effect of the sizing agent is good or not is determined, the shape and the position of the sizing agent are measured and calculated, and the shape parameter and the position parameter of the sizing agent are obtained.
Illustratively, the measuring data of the slurry in the target area image to obtain slurry parameters further includes:
step f: scanning the target area image to obtain the distribution condition of the slurry;
illustratively, the target area image of the L-shaped slurry is scanned to obtain the position distribution of the L-shaped slurry.
Illustratively, the target area image of the dot-shaped slurry is scanned to obtain the position distribution of the dot-shaped slurry, i.e. the specific position of the dot-shaped slurry on the raw material plate.
Step g: measuring the data of the slurry in the target area image based on the distribution condition to obtain slurry parameters;
illustratively, according to the shape distribution and the position distribution of the dot-shaped slurry, measuring the target area image of the dot-shaped slurry, and measuring the data of the dot-shaped slurry to obtain the slurry parameters of the dot-shaped slurry.
Illustratively, according to the shape distribution and the position distribution of the L-shaped slurry, measuring the target area image of the dot-shaped slurry, and measuring the data of the dot-shaped slurry to obtain the slurry parameters of the dot-shaped slurry.
Illustratively, the slurry parameters include a position parameter and a shape parameter, and the measuring data of the slurry in the target area image based on the distribution situation to obtain the slurry parameters includes:
step h: based on the distribution condition, segmenting the shape of the target area image to obtain an image convenient for measurement and calculation;
illustratively, the target area image of the dot-shaped paste is scanned to obtain the shape distribution of the dot-shaped paste, i.e. the upper and lower semicircles of the dot-shaped paste.
Illustratively, the scanning process is performed on the target area image of the L-shaped slurry to obtain the shape distribution of the L-shaped slurry, and the L-shaped slurry is divided into a horizontal direction and a vertical direction.
Step i: measuring and calculating the shape of the image to obtain the shape parameter of the target area image;
illustratively, the shape of the dot-shaped slurry is measured, the upper semicircle and the lower semicircle are respectively measured, the side length, the width and the area data of the upper semicircle and the lower semicircle are measured, the overall length and the width data of the dot-shaped slurry are measured, and the shape parameters of the dot-shaped slurry comprise the measured data.
Illustratively, the shape parameters of the horizontal part and the vertical part of the L-shaped slurry are measured and calculated respectively, the length, the width and the area data in each part and the overall length and the width of the L-shaped slurry are measured and calculated, and the shape parameters of the L-shaped slurry comprise the measured and calculated data.
Step j: determining positioning points in the target area image based on the distribution condition;
illustratively, a geometric center position parameter of the dot-shaped slurry and a top position parameter of the dot-shaped slurry are determined, and whether the dot-shaped slurry meets the requirement of covering the line trend is determined.
Illustratively, the geometric center position parameter of the L-shaped slurry and the top position parameter of the L-shaped slurry are determined, and whether the L-shaped slurry meets the requirement of covering the line trend is determined.
Step k: and measuring and calculating the relative position between the positioning point and a preset reference point to obtain a position parameter.
Illustratively, parameters of a positioning point of the dot-shaped slurry, namely a geometric center position parameter and a top position parameter of the dot-shaped slurry are obtained, and a relative position between the parameters of the positioning point and a preset reference point is measured to obtain a position parameter of the dot-shaped slurry.
Illustratively, parameters of a positioning point of the L-shaped slurry, namely a geometric center position parameter and a top position parameter of the L-shaped slurry are obtained, and a relative position between the parameters of the positioning point and a preset reference point is measured to obtain a position parameter of the L-shaped slurry.
Illustratively, after brightness threshold processing is carried out on the image, the color of the polarizer is darker than that of the color filter, a polarizer boundary line is searched from a white part to a black part in the product image, and the distance between the top highest point of the slurry and the polarizer is measured to obtain the position parameter of the slurry.
Illustratively, the target area image is measured to obtain a geometric center position parameter of the target area image, a polarizer boundary line is searched from a white part to a black part in the product image, and the distance from a left starting point of the polarizer to the geometric center position parameter of the target area image is measured to obtain a position parameter of the slurry.
In this embodiment, detect the raw materials board quality, the effect of scribbling the thick liquids is influencing the effect of deriving the static on the raw materials board, thick liquids shape and position on the raw materials board are the factor that influences the raw materials board, improve the quality of raw materials board, prevent that static from influencing the life-span of raw materials board, guarantee the thick liquids parameter on the raw materials board promptly, when shape parameter and position parameter in the thick liquids parameter have all passed through the detection, the technology that determines the thick liquids on the raw materials board accords with the standard, the quality that has guaranteed the raw materials board accords with the standard. The method comprises the steps of collecting and calculating a product image of a raw material plate, determining the ROI frame selection range in the collection process, wherein the precision of the collected image is affected by the too large and too small ROI frame selection range, limiting the frame range by the ROI by taking the image range of the slurry as a reference, ensuring that the range of the slurry and the ROI range are kept in a superposition state, processing the image of the slurry selected by the ROI frame, extracting the complete shape of the slurry, performing interference elimination processing on the outer edge side of the image of the slurry, preventing the detection precision from being reduced due to the existence of an interference area, determining whether the shape of the slurry is complete or not when the shape of the slurry is extracted, judging whether the shape of the slurry has defects or not, and facilitating subsequent detection. And meanwhile, measuring and calculating the processed image, measuring and calculating the shape parameter and the position parameter of the slurry, and determining that the slurry smearing effect on the raw material plate is qualified when the shape parameter and the position parameter are judged, so that the quality of the raw material plate is judged to be qualified. The image processing is additionally refined, the accuracy of the processed image is improved, meanwhile, subsequent judgment is facilitated, the shape and the position of the slurry are measured and calculated based on the slurry parameters, the measured and calculated result is obtained and compared with the preset slurry parameters, and the judgment on the quality of the raw material plate is more accurate.
Based on the above first embodiment and second embodiment of the method for detecting quality of a raw material plate of the present application, a third embodiment is provided, the method further comprising:
step l: comparing the similarity between the slurry parameters and a preset slurry parameter threshold value;
step m: and determining whether the target area image is qualified or not based on the similarity.
The slurry parameters comprise the shape parameters of the slurry and the position parameters of the slurry, the shape parameters and the position parameters are respectively compared with preset parameter thresholds, whether the slurry parameters meet the standard or not is judged according to the comparison result, whether the quality of the raw material plate is qualified or not is determined, and the raw material plate is judged to be qualified when the shape parameters and the position parameters of the slurry meet the standard.
For example, when the quality of the raw material plate is determined, if the shape parameter meets the standard and the position parameter does not meet the standard, the quality of the raw material is determined to be unqualified.
For example, when the quality of the raw material plate is determined, if the shape parameter does not meet the standard and the position parameter meets the standard, the quality of the raw material is determined to be unqualified.
In this embodiment, there are two kinds of parameter requirements to the raw materials board, shape parameter requirement and position parameter requirement, thick liquids are scribbled on the raw materials board, because the production error can not be avoided in the course of working, lead to thick liquids to scribble the effect and differ, and the shape and the position of thick liquids all are the effect of deriving static on the influence raw materials board, if only detect the shape parameter of thick liquids and do not go to detect position parameter, the raw materials board that leads to the position that can't discern thick liquids to produce the deviation, and the quality of raw materials board is influenced too greatly to thick liquids position skew, do not detect position parameter, lead to during partial defective products flow into subsequent production line, equally, only detect position parameter and do not go to detect shape parameter, can lead to partial defective products flow into subsequent production line in the same way. The shape parameters and the position parameters of the slurry are respectively judged, so that the effect of the slurry smeared on the raw material plate is optimal, the conductive effect of the slurry on the raw material plate is ensured, and the quality of the raw material plate is determined to be qualified.
The application further provides a raw material plate quality detection device. The raw material plate quality detection device includes:
the acquisition module is used for acquiring slurry parameters in a target area image of slurry on the raw material plate;
a determination module for determining whether the region image meets a criterion based on the slurry parameter;
and the judging module is used for judging that the raw material plate coated with the slurry is qualified if the standard is met.
Illustratively, the obtaining module includes:
the acquisition submodule is used for acquiring a product image of the raw material plate;
the first determining submodule is used for determining an initial region image from the product image based on a preset ROI;
the interference removing submodule is used for carrying out interference removing processing on the initial region image to obtain a target region image;
and the measuring submodule is used for measuring the data of the slurry in the target area image to obtain slurry parameters.
Illustratively, the first determination submodule includes:
a determining unit for determining a profile of the slurry from the product image based on a preset ROI;
and the extracting unit is used for extracting the image of the slurry from the product image based on the outline to obtain an initial region image.
Illustratively, the de-interference sub-module includes;
an amplification unit: based on a preset scaling coefficient, amplifying the initial region image to obtain an edge image of the initial region image;
the identification unit is used for identifying interference pixel points from the edge image to obtain an interference image outside the initial region image; the interference image is composed of the interference pixel points;
and the removing unit is used for removing the interference image from the initial region image to obtain a target region image.
Illustratively, the measurement submodule includes:
the scanning unit is used for scanning the target area image to obtain the distribution condition of the slurry;
and the measuring unit is used for measuring the data of the slurry in the target area image based on the distribution situation to obtain slurry parameters.
Illustratively, the measurement unit includes:
the segmentation subunit is used for segmenting the shape of the target area image based on the distribution situation to obtain an image convenient for measurement and calculation;
the first measuring and calculating subunit is used for measuring and calculating the shape of the image to obtain the shape parameter of the target area image;
a determining subunit, configured to determine, based on the distribution, a positioning point in the target region image;
and the second measuring and calculating subunit is used for measuring and calculating the relative position between the positioning point and a preset reference point to obtain a position parameter.
Illustratively, the determining module includes:
and the comparison submodule is used for comparing the similarity between the slurry parameters and a preset slurry parameter threshold.
And the second determining submodule is used for determining whether the target area image is qualified or not based on the similarity.
The specific implementation of the raw material plate quality detection apparatus of the present application is substantially the same as that of each embodiment of the raw material plate quality detection method, and is not described herein again.
In addition, this application still provides a raw materials board quality testing equipment. As shown in fig. 4, fig. 4 is a schematic structural diagram of a hardware operating environment according to an embodiment of the present application.
For example, fig. 4 is a schematic structural diagram of a hardware operating environment of the raw material plate quality inspection apparatus.
As shown in fig. 4, the raw material plate quality inspection apparatus may include a processor 401, a communication interface 402, a memory 403, and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other via the communication bus 404, and the memory 403 stores a computer program; the processor 401 is configured to implement the steps of the raw material plate quality inspection method when executing the program stored in the memory 403.
The communication bus 404 mentioned in the above raw material board quality inspection apparatus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industrial Standard Architecture (EISA) bus, or the like. The communication bus 404 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface 402 is used for communication between the raw material plate quality inspection apparatus and other apparatuses.
The Memory 403 may include a Random Access Memory (RMD) and a Non-Volatile Memory (NM), such as at least one disk Memory. Optionally, the memory 403 may also be at least one storage device located remotely from the processor 401.
The Processor 401 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
The specific implementation of the raw material plate quality detection apparatus in the present application is substantially the same as that of each embodiment of the raw material plate quality detection method described above, and is not described herein again.
Furthermore, an embodiment of the present application also provides a computer-readable storage medium, on which a raw material plate quality detection program is stored, which when executed by a processor implements the steps of the raw material plate quality detection method as described above.
The specific implementation manner of the computer-readable storage medium of the present application is substantially the same as that of each embodiment of the raw material plate quality detection method, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a computer-readable storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above, and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method described in the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. A raw material plate quality detection method is characterized by comprising the following steps:
acquiring slurry parameters in a target area image of slurry on a raw material plate coated with the slurry;
determining whether the target area image meets a criterion based on the slurry parameters;
and if the standard is met, judging that the raw material plate is qualified.
2. The method for detecting the quality of a raw material plate as claimed in claim 1, wherein the step of obtaining the slurry parameters in the target area image of the slurry on the raw material plate coated with the slurry comprises:
acquiring a product image of a raw material plate;
determining an initial region image from the product image based on a preset ROI;
performing interference removal processing on the initial region image to obtain a target region image;
and measuring the data of the slurry in the target area image to obtain slurry parameters.
3. The raw material plate quality inspection method according to claim 2, wherein the determining an initial region image from the product image based on a preset ROI includes:
determining a profile of the slurry from the product image based on a preset ROI;
and extracting the image of the slurry from the product image based on the contour to obtain an initial region image.
4. The raw material plate quality inspection method according to claim 2, wherein the performing of the interference elimination process on the initial area image to obtain the target area image comprises:
based on a preset scaling coefficient, amplifying the initial region image to obtain an edge image of the initial region image;
identifying interference pixel points from the edge image to obtain an interference image outside the initial region image; the interference image is composed of the interference pixel points;
and removing the interference image from the initial region image to obtain a target region image.
5. The raw material plate quality inspection method according to claim 2, wherein the measuring data of the slurry in the target area image to obtain slurry parameters comprises:
scanning the target area image to obtain the distribution condition of the slurry;
and measuring the data of the slurry in the target area image based on the distribution condition to obtain slurry parameters.
6. The raw material plate quality inspection method according to claim 5, wherein the slurry parameters include a position parameter and a shape parameter, and the measuring data of the slurry in the target area image based on the distribution condition to obtain the slurry parameters includes:
based on the distribution condition, segmenting the shape of the target area image to obtain an image convenient for measurement and calculation;
measuring and calculating the shape of the image convenient to measure and calculate to obtain the shape parameter of the target area image;
determining positioning points in the target area image based on the distribution condition;
and measuring and calculating the relative position between the positioning point and a preset reference point to obtain a position parameter.
7. The raw material plate quality inspection method according to claim 1, wherein said determining whether the target area image meets a criterion based on the slurry parameter comprises:
comparing the similarity between the slurry parameters and a preset slurry parameter threshold value;
and determining whether the target area image is qualified or not based on the similarity.
8. A raw material plate quality inspection device, characterized by comprising:
the first acquisition module is used for acquiring slurry parameters in a target area image of slurry on the raw material plate;
a first determination module for determining whether the region image meets a criterion based on the slurry parameter;
and the first judging module is used for judging that the raw material plate coated with the slurry is qualified if the standard is met.
9. A raw material sheet quality inspection apparatus, comprising: a memory, a processor, and a raw material plate quality inspection program stored on the memory and executable on the processor, the raw material plate quality inspection program configured to implement the steps of the raw material plate quality inspection method of any one of claims 1 to 7.
10. A computer-readable storage medium, wherein a raw material sheet quality inspection program is stored thereon, which when executed by a processor, implements the steps of the raw material sheet quality inspection method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210401943.XA CN114485409A (en) | 2022-04-18 | 2022-04-18 | Raw material plate quality detection method, device and equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210401943.XA CN114485409A (en) | 2022-04-18 | 2022-04-18 | Raw material plate quality detection method, device and equipment and readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114485409A true CN114485409A (en) | 2022-05-13 |
Family
ID=81489687
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210401943.XA Pending CN114485409A (en) | 2022-04-18 | 2022-04-18 | Raw material plate quality detection method, device and equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114485409A (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09307217A (en) * | 1996-05-13 | 1997-11-28 | Ntn Corp | Defect correcting method and device of continuous pattern |
| US5987174A (en) * | 1995-04-26 | 1999-11-16 | Hitachi, Ltd. | Image processing apparatus for vehicles |
| CN1434932A (en) * | 2000-10-19 | 2003-08-06 | 克雷奥以色列有限公司 | Nonlinear image distortion correction in printed circuit board manufacturing |
| JP2007017283A (en) * | 2005-07-07 | 2007-01-25 | Toshiba Corp | Method and apparatus for visual inspection |
| JP2007026217A (en) * | 2005-07-19 | 2007-02-01 | Ckd Corp | Testing device and testing method |
| CN101290219A (en) * | 2007-04-16 | 2008-10-22 | 安立株式会社 | Centralized management system for solder check line, management device thereof and centralized management method |
| WO2014184960A1 (en) * | 2013-05-17 | 2014-11-20 | 富士機械製造株式会社 | Inspection device, inspection method, and control device |
| CN107024487A (en) * | 2016-03-10 | 2017-08-08 | 上海帆声图像科技有限公司 | ITO electro-conductive glass detecting system and its detection method |
| CN107734955A (en) * | 2016-08-10 | 2018-02-23 | 欧姆龙株式会社 | Check device, quality control system and the recording medium of surface hookup wire |
| CN107966448A (en) * | 2017-11-17 | 2018-04-27 | 福建工程学院 | A kind of 2 dimension detection methods for PCB paste solder printing quality |
| CN112986282A (en) * | 2019-12-02 | 2021-06-18 | Juki株式会社 | Inspection apparatus and inspection method |
| CN113686899A (en) * | 2021-08-30 | 2021-11-23 | 德中（天津）技术发展股份有限公司 | Method and apparatus for optical inspection and short circuit and open circuit correction of circuit board conductive pattern |
-
2022
- 2022-04-18 CN CN202210401943.XA patent/CN114485409A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5987174A (en) * | 1995-04-26 | 1999-11-16 | Hitachi, Ltd. | Image processing apparatus for vehicles |
| JPH09307217A (en) * | 1996-05-13 | 1997-11-28 | Ntn Corp | Defect correcting method and device of continuous pattern |
| CN1434932A (en) * | 2000-10-19 | 2003-08-06 | 克雷奥以色列有限公司 | Nonlinear image distortion correction in printed circuit board manufacturing |
| JP2007017283A (en) * | 2005-07-07 | 2007-01-25 | Toshiba Corp | Method and apparatus for visual inspection |
| JP2007026217A (en) * | 2005-07-19 | 2007-02-01 | Ckd Corp | Testing device and testing method |
| CN101290219A (en) * | 2007-04-16 | 2008-10-22 | 安立株式会社 | Centralized management system for solder check line, management device thereof and centralized management method |
| WO2014184960A1 (en) * | 2013-05-17 | 2014-11-20 | 富士機械製造株式会社 | Inspection device, inspection method, and control device |
| CN107024487A (en) * | 2016-03-10 | 2017-08-08 | 上海帆声图像科技有限公司 | ITO electro-conductive glass detecting system and its detection method |
| CN107734955A (en) * | 2016-08-10 | 2018-02-23 | 欧姆龙株式会社 | Check device, quality control system and the recording medium of surface hookup wire |
| CN107966448A (en) * | 2017-11-17 | 2018-04-27 | 福建工程学院 | A kind of 2 dimension detection methods for PCB paste solder printing quality |
| CN112986282A (en) * | 2019-12-02 | 2021-06-18 | Juki株式会社 | Inspection apparatus and inspection method |
| CN113686899A (en) * | 2021-08-30 | 2021-11-23 | 德中（天津）技术发展股份有限公司 | Method and apparatus for optical inspection and short circuit and open circuit correction of circuit board conductive pattern |
Non-Patent Citations (1)
| Title |
|---|
| 刘东来 等: ""基于机器视觉的弧焊机器人焊缝识别及路径生成研究"", 《可持续制造》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112577969B (en) | Defect detection method and defect detection system based on machine vision | |
| CN108460757B (en) | Mobile phone TFT-LCD screen Mura defect online automatic detection method | |
| CN115908269B (en) | Visual defect detection method, visual defect detection device, storage medium and computer equipment | |
| CN111179243A (en) | Small-size chip crack detection method and system based on computer vision | |
| CN115205223B (en) | Visual inspection method and device for transparent object, computer equipment and medium | |
| CN108918093B (en) | Optical filter mirror surface defect detection method and device and terminal equipment | |
| CN112686858A (en) | Visual defect detection method, device, medium and equipment for mobile phone charger | |
| CN111724375B (en) | Screen detection method and system | |
| CN110007493B (en) | Method for detecting broken bright spots in liquid crystal display screen | |
| CN111815565B (en) | Wafer backside detection method, equipment and storage medium | |
| CN112669272B (en) | AOI rapid detection method and rapid detection system | |
| CN113834816A (en) | Machine vision-based photovoltaic cell defect online detection method and system | |
| CN109816627B (en) | Method for detecting weak and small defect target in ink area of plane glass element | |
| KR102242996B1 (en) | Method for atypical defects detect in automobile injection products | |
| CN116468687A (en) | Scratch defect detection method and device, storage medium and electronic equipment | |
| CN117115130A (en) | Wafer edge defect detection method and device | |
| US11068740B2 (en) | Particle boundary identification | |
| CN114998217A (en) | Method for determining defect grade of glass substrate, computer device and storage medium | |
| KR101677070B1 (en) | System and Method for Automatically Detecting a Mura Defect using Morphological Image Processing and Labeling | |
| CN118501177A (en) | Appearance defect detection method and system for formed foil | |
| CN114485409A (en) | Raw material plate quality detection method, device and equipment and readable storage medium | |
| CN114742832B (en) | Welding defect detection method for MiniLED thin plate | |
| CN116091503A (en) | Method, device, equipment and medium for discriminating panel foreign matter defects | |
| US11880969B2 (en) | Belt examination system and computer-readable non-transitory recording medium having stored belt examination program | |
| CN114511522A (en) | Automatic reagent judgment method based on fluorescence immunoassay and colloidal gold method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220513 |
|
| RJ01 | Rejection of invention patent application after publication |