CN115018823A

CN115018823A - Method and device for detecting appearance defects of adhesive tape

Info

Publication number: CN115018823A
Application number: CN202210856795.0A
Authority: CN
Inventors: 刘静兰; 孟凡彬; 杨富娟
Original assignee: Dongguan Aozhongxin Material Technology Co ltd
Current assignee: Dongguan Aozhongxin Material Technology Co ltd
Priority date: 2022-07-20
Filing date: 2022-07-20
Publication date: 2022-09-06

Abstract

The invention discloses a method and a device for detecting appearance defects of an adhesive tape, wherein the method comprises the following steps: when a defect detection instruction is detected, acquiring a real-time surface image of the target adhesive tape; calculating a gray scale value parameter corresponding to the real-time surface image based on a gray scaling algorithm according to the real-time surface image; judging whether the real-time surface image belongs to a defect image or not according to the gray scale value parameter and a preset defect detection model to obtain a first judgment result; if the first judgment result is yes, recording position information and defect information corresponding to the real-time surface image; the position information is used for marking the corresponding defect position of the real-time surface image on the target adhesive tape. Therefore, the invention has higher detection efficiency and more accurate detection result, and can effectively improve the production efficiency and the product quality.

Description

Method and device for detecting appearance defects of adhesive tape

Technical Field

The invention relates to the technical field of image detection, in particular to a method and a device for detecting appearance defects of an adhesive tape.

Background

The production of adhesive tapes is a process of coating a layer of adhesive on a substrate, in which various production defects may occur due to problems with the process or the substrate, resulting in product quality problems. In the prior art, most of the defects are detected by adopting a manual visual identification mode, but the production process of the adhesive tape is complex and has high running speed, the efficiency of manual visual identification is extremely low, and errors are easy to occur. Therefore, the existing adhesive tape appearance defect detection method has defects and needs to be solved urgently.

Disclosure of Invention

The invention aims to solve the technical problem of providing a method and a device for detecting the appearance defects of the adhesive tape, which have higher detection efficiency and more accurate detection result and can effectively improve the production efficiency and the product quality.

In order to solve the technical problem, a first aspect of the present invention discloses a method for detecting an appearance defect of an adhesive tape, the method comprising:

when a defect detection instruction is detected, acquiring a real-time surface image of the target adhesive tape;

calculating a gray scale value parameter corresponding to the real-time surface image based on a gray scale algorithm according to the real-time surface image;

judging whether the real-time surface image belongs to a defect image or not according to the gray scale value parameter and a preset defect detection model to obtain a first judgment result;

if the first judgment result is yes, recording position information and defect information corresponding to the real-time surface image; the position information is used for marking the corresponding defect position of the real-time surface image on the target adhesive tape.

As an optional implementation manner, in the first aspect of the present invention, the method further includes:

if the first judgment result is yes, determining the marking position of the target adhesive tape according to the position information corresponding to the real-time surface image;

generating a marking instruction according to the marking position; the marking instructions are for driving a paint marking device to place a paint mark on the target adhesive tape at the marked location;

and/or the presence of a gas in the gas,

if the first judgment result is yes, generating an alarm instruction; the alarm instruction is used for driving alarm equipment to carry out sound alarm and/or luminous alarm.

As an optional implementation manner, in the first aspect of the present invention, the determining whether the real-time surface image belongs to a defect image according to the gray-scale value parameter and a preset defect detection model includes:

calculating a first gray scale difference value between the gray scale value parameter and a first reference gray scale value parameter;

judging whether the real-time surface image belongs to a defect image or not according to the first gray scale difference value and a plurality of difference value intervals-defect corresponding relations included by a defect detection model;

and/or the presence of a gas in the gas,

calculating a second gray scale difference value between the gray scale value parameter and a second reference gray scale value parameter;

inputting the second gray level difference value into a trained classifier network model included in a defect detection model, and judging whether the real-time surface image belongs to a defect image or not according to an output result of the classifier network model; the classifier network model is obtained by training a training data set comprising a plurality of training gray scale difference parameters and corresponding image defect type labels.

As an optional implementation manner, in the first aspect of the present invention, before the detecting the defect detection instruction, the method further includes:

acquiring the real-time movement speed of the target adhesive tape;

judging whether the real-time movement speed meets a preset speed condition or not to obtain a second judgment result;

when the second judgment result is yes, generating the defect detection instruction; the speed condition includes the real-time movement speed being greater than a first speed threshold and/or the real-time movement speed being less than a second speed threshold.

acquiring a defect detection requirement input by a user; the defect detection requirement is used for indicating the detection requirement of the defect of the target type of adhesive tape;

determining a defect detection parameter of the defect detection model according to the defect detection requirement; the defect detection parameters are used for enabling the defect detection model to meet the detection requirements when the defect detection of the target adhesive tape is carried out.

As an alternative embodiment, in the first aspect of the present invention, the defect type of the defect image includes at least one of a skip coat glue line defect, a twill defect, a cross grain defect, a color unevenness defect, and a foreign matter defect; and/or the base material of the target adhesive tape is a PET film, a BOPP film or a PI film; and/or the color of the adhesive layer of the target adhesive tape is blue, green, transparent or red; and/or the thickness of the base material of the target adhesive tape is 6-150 um; and/or the thickness of the adhesive layer of the target adhesive tape is 2 um-150 um.

As an optional embodiment, in the first aspect of the present invention, the defect information includes at least one of a defect image, a defect position, a defect size, a defect type, and a defect number; the method further comprises the following steps:

acquiring all defect information corresponding to the target adhesive tape;

and establishing a corresponding relation between the target adhesive tape and the defect information, and storing the defect information into a database.

The second aspect of the present invention discloses an adhesive tape appearance defect detecting apparatus, comprising:

the image acquisition module is used for acquiring a real-time surface image of the target adhesive tape when the defect detection instruction is detected;

the gray scale calculation module is used for calculating a gray scale value parameter corresponding to the real-time surface image based on a gray scale algorithm according to the real-time surface image;

the defect judging module is used for judging whether the real-time surface image belongs to a defect image or not according to the gray scale value parameter and a preset defect detection model to obtain a first judging result;

the defect recording module is used for recording the position information and the defect information corresponding to the real-time surface image when the first judgment result is yes; the position information is used for marking the corresponding defect position of the real-time surface image on the target adhesive tape.

As an alternative embodiment, in the second aspect of the present invention, the apparatus further comprises a pigment marking module for performing the steps of:

and/or, the device further comprises an alarm module for executing the following steps:

As an optional implementation manner, in the second aspect of the present invention, a specific manner of determining, by the defect determining module, whether the real-time surface image belongs to a defect image according to the gray-scale parameter and a preset defect detection model includes:

and/or the presence of a gas in the gas,

As an optional implementation manner, in the second aspect of the present invention, the apparatus further includes a speed determination module, configured to perform the following steps:

acquiring the real-time movement speed of the target adhesive tape;

As an optional implementation manner, in the second aspect of the present invention, the apparatus further includes a parameter adjusting module, configured to perform the following steps:

As an alternative embodiment, in the second aspect of the present invention, the defect type of the defect image includes at least one of a skip coat glue line defect, a twill defect, a cross grain defect, a color unevenness defect, and a foreign matter defect; and/or the base material of the target adhesive tape is a PET film, a BOPP film or a PI film; and/or the color of the adhesive layer of the target adhesive tape is blue, green, transparent or red; and/or the thickness of the base material of the target adhesive tape is 6-150 um; and/or the thickness of the adhesive layer of the target adhesive tape is 2 um-150 um.

As an alternative embodiment, in the second aspect of the present invention, the defect information includes at least one of a defect image, a defect position, a defect size, a defect type, and a defect number; the device also comprises a data storage module used for executing the following steps:

acquiring all defect information corresponding to the target adhesive tape;

The third aspect of the present invention discloses another apparatus for detecting appearance defects of adhesive tapes, the apparatus comprising:

a memory storing executable program code;

a processor coupled with the memory;

the processor calls the executable program code stored in the memory to execute part or all of the steps in the adhesive tape appearance defect detection method disclosed by the first aspect of the invention.

In a fourth aspect of the present invention, a computer-readable storage medium storing a computer program for electronic data exchange is disclosed, wherein the computer program causes a computer to execute some or all of the steps in the method for detecting an apparent defect of an adhesive tape disclosed in the first aspect of the present invention.

The invention discloses a fifth aspect of the invention discloses a further adhesive tape appearance defect detection device, which comprises a control device, a pigment marking device, an adhesive tape conveying device, an image acquisition device and an alarm device, wherein the pigment marking device, the adhesive tape conveying device, the image acquisition device and the alarm device are connected to the control device; the control device is used for executing part or all of the steps in the method for detecting the appearance defects of the adhesive tape disclosed by the first aspect of the invention.

Compared with the prior art, the embodiment of the invention has the following beneficial effects:

the embodiment of the invention can fully utilize the gray scale value parameters corresponding to the real-time surface image of the adhesive tape and the defect detection model to accurately judge whether the adhesive tape has defects or not, and record when the adhesive tape has defects.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flow chart of a method for detecting an appearance defect of an adhesive tape according to an embodiment of the present invention.

Fig. 2 is a schematic structural diagram of an adhesive tape appearance defect detection apparatus according to an embodiment of the present invention.

Fig. 3 is a schematic structural diagram of another device for detecting apparent defects of adhesive tapes according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The terms "second," "second," and the like in the description and in the claims, and in the foregoing drawings, are used for distinguishing between different objects and not necessarily for describing a particular sequential order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or apparatus that comprises a list of steps or elements is not limited to those listed but may alternatively include other steps or elements not listed or inherent to such process, method, product, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

The invention discloses a method and a device for detecting appearance defects of an adhesive tape, which can make full use of gray scale value parameters and a defect detection model corresponding to a real-time surface image of the adhesive tape to accurately judge whether the adhesive tape has defects or not and record when the adhesive tape has the defects. The following are detailed below.

Example one

Referring to fig. 1, fig. 1 is a schematic flow chart of a method for detecting an appearance defect of an adhesive tape according to an embodiment of the present invention. The method for detecting the appearance defects of the adhesive tape described in fig. 1 may be applied to a data processing chip, a processing terminal, or a processing server (where the processing server may be a local server or a cloud server). As shown in fig. 1, the method for detecting the appearance defect of the adhesive tape may include the following operations:

101. and when the defect detection instruction is detected, acquiring a real-time surface image of the target adhesive tape.

Optionally, the substrate of the adhesive tape of the present invention may be a PET film, a BOPP film, or a PI film. Alternatively, the adhesive layer of the adhesive tape of the invention can be blue, green, transparent or red. Optionally, the thickness of the base material of the adhesive tape can be 6-150 um. Optionally, the adhesive layer of the adhesive tape of the invention may have a thickness of 2um to 150 um.

Alternatively, the image of the surface of the adhesive tape may be acquired by an image acquisition device disposed adjacent to the adhesive tape. Alternatively, the image capturing device may be a digital camera or an analog camera. In a preferred embodiment, the image acquisition device is a CCD (charged coupled device) camera, which is arranged on one side of the adhesive tape and is connected to the control device for transmitting the acquired surface image to the control device. Preferably, a lighting device is arranged on one side, away from the image acquisition device, of the adhesive tape, and the lighting device is used for emitting light to irradiate the adhesive tape, so that the real-time surface image of the adhesive tape acquired by the image acquisition device is a light-transmitting image, and the defect corresponding to the adhesive tape can be identified more accurately. Alternatively, the lighting device may be an LED lamp device.

Optionally, before step 101, the method may further include:

and receiving the detected product parameters input by the user, and storing the detected product parameters.

Optionally, the detected product parameter may include one or more of the work order number information, the product name information, the batch number information, and the quantity information of the target adhesive tape corresponding to the detection.

102. And calculating a gray scale value parameter corresponding to the real-time surface image based on a gray scale algorithm according to the real-time surface image.

Optionally, the real-time surface image may be converted into a grayscale image based on a graying algorithm, and then a grayscale value parameter corresponding to the real-time surface image is calculated according to a grayscale value corresponding to each pixel point in the grayscale image. Optionally, the gray-scale parameter may be a sum, an average, or a weighted average of gray-scale values corresponding to all pixel points in the gray-scale image, or may also be a sum, an average, or a weighted average of gray-scale values corresponding to all pixel points in a specific area in the gray-scale image.

103. And judging whether the real-time surface image belongs to the defect image or not according to the gray-scale value parameter and a preset defect detection model to obtain a first judgment result.

Optionally, the defect detection model may be a trained neural network model or a data analysis model, which may determine whether the real-time surface image belongs to the defect image according to the gray-scale parameter according to a data analysis algorithm. Optionally, the defect type of the defect image according to the present invention may include at least one of an under-coating layer defect, a diagonal defect, a cross grain defect, a color non-uniformity defect, and a foreign substance defect. The foreign matter defect can be mosquito and foreign matter existing on the adhesive tape or other foreign matter affecting the product quality.

104. And if the first judgment result is yes, recording the position information and the defect information corresponding to the real-time surface image.

Optionally, the position information is used to mark the corresponding defect position of the real-time surface image on the target adhesive tape. Alternatively, the position information may be determined by an adhesive tape conveying device connected to the image acquiring device, for example, the image acquiring device records time information of acquiring the real-time surface image and transmits the time information to the control device, and the control device may determine the position information according to the time information and the transmission information of the adhesive tape conveying device, for example, if the adhesive tape conveying device transmits the target adhesive tape from a first marked point at a first speed from a first time point, a product of a time difference between the time information and the first time point and the first speed may be calculated, so that a distance between a position on the target adhesive tape corresponding to the real-time surface image and the first marked point may be calculated, thereby obtaining the position information.

Therefore, the gray scale value parameters corresponding to the real-time surface image of the adhesive tape and the defect detection model can be fully utilized to accurately judge whether the adhesive tape has defects or not, and recording is carried out when the adhesive tape has defects.

As an optional implementation, the method further comprises:

and generating a marking instruction according to the marking position.

Optionally, the marking instructions are for driving a paint marking device to place a paint mark at a marked location on the target adhesive tape. Alternatively, the pigment marking device may be an ink jet device connected to the control device, and the control device may send the marking instructions to the pigment marking device to drive the pigment marking device to place the pigment marking at the marking location on the target adhesive tape. Preferably, the ink mark of the ink jet device can mark on the release film of the adhesive tape and can not be wiped off, and the ink jet speed can reach 1 target adhesive tape and control the marking within 30 s.

Preferably, the corresponding adhesive tape cutting device can be driven subsequently, the defect part on the target adhesive tape corresponding to the real-time surface image can be cut according to the pigment mark, and an instruction can be sent to inform the corresponding operator to execute the operation.

Therefore, by implementing the optional implementation mode, the marking instruction can be generated according to the position information corresponding to the real-time surface image to drive the pigment marking device to set the pigment mark at the marking position on the target adhesive tape, so that the defect of the adhesive tape can be visually marked, the defect position can be removed conveniently according to the mark, and the production efficiency and the product quality can be effectively improved.

As an optional embodiment, the method further comprises:

if the first judgment result is yes, an alarm instruction is generated.

Specifically, the alarm instruction is used for driving an alarm device to perform sound alarm and/or light alarm. Optionally, the alarm device may include an audible alarm (e.g., a buzzer) and/or a light-emitting alarm (e.g., an LED lamp), which may be connected to the control device and used by the control device to send the alarm command to the alarm device for alarming.

Therefore, by implementing the optional implementation mode, the alarm instruction can be generated to drive the alarm device to alarm, so that the operator can be reminded of the existence of the defect when the defect of the adhesive tape is found, the subsequent operator can conveniently execute corresponding operation on the defect position, such as defect removal or adhesive tape production suspension or defect reporting, and the production efficiency and the product quality can be effectively improved.

As an optional implementation manner, the determining, in step 103, whether the real-time surface image belongs to the defect image according to the gray-scale parameter and the preset defect detection model includes:

and judging whether the real-time surface image belongs to the defect image or not according to the first gray scale difference value and a plurality of difference value intervals-defect corresponding relations included by the defect detection model.

Alternatively, the first reference gray-scale value parameter may be determined through an experimental value or experience, or may be calculated through gray-scale value parameters corresponding to historical normal surface images of the adhesive tape, for example, an average value or a weighted average value of the gray-scale value parameters corresponding to the normal surface images is calculated. Preferably, the capturing environment of the normal surface image should be as similar or identical as possible to the capturing environment of the real-time surface image.

Optionally, the difference interval-defect correspondence may be used to define gray scale difference data intervals corresponding to a plurality of different types of defects, for example, gray scale difference data intervals corresponding to an under-coated glue layer defect, a twill defect, a cross grain defect, a color non-uniformity defect, and a foreign object defect may be defined respectively, so as to determine whether the real-time surface image belongs to a defect image.

Therefore, through the optional implementation mode, the first gray scale difference value between the gray scale value parameter and the first reference gray scale value parameter can be calculated, and whether the real-time surface image belongs to the defect image or not can be judged according to the first gray scale difference value and the corresponding relation between the plurality of difference value intervals and the defects included by the defect detection model, so that whether the target adhesive tape has the defects or not can be judged more accurately, the detection efficiency is higher, the detection result is more accurate, and the production efficiency and the product quality can be effectively improved.

and inputting the second gray level difference value into a trained classifier network model included in the defect detection model, and judging whether the real-time surface image belongs to the defect image or not according to an output result of the classifier network model.

Specifically, the classifier network model is obtained by training a training data set including a plurality of training gray scale difference parameters and corresponding image defect type labels. Optionally, the image defect type marking may include at least one of normal marking, missing coating glue layer defect marking, diagonal defect marking, cross grain defect marking, color non-uniformity defect marking, and foreign matter defect marking.

Therefore, by the optional implementation mode, the second gray level difference value can be input into the trained classifier network model included in the defect detection model, and whether the real-time surface image belongs to the defect image or not is judged according to the output result of the classifier network model, so that whether the target adhesive tape has defects or not can be judged more accurately, the detection efficiency is higher, the detection result is more accurate, and the production efficiency and the product quality can be effectively improved.

As an optional implementation manner, before the time when the defect detection instruction is detected in step 101, the method further includes:

acquiring the real-time movement speed of the target adhesive tape;

and when the second judgment result is yes, generating a defect detection instruction.

Specifically, the speed condition includes the real-time movement speed being greater than a first speed threshold and/or the real-time movement speed being less than a second speed threshold. Alternatively, the first speed threshold and the second speed threshold may be determined according to empirical values or experimental values and adjusted according to the effect in practical application, and generally speaking, the second speed threshold should be larger than the first speed threshold. Alternatively, the first speed threshold should be used to indicate a speed at which the target adhesive tape is in normal transport motion, while the second speed threshold should be used to indicate a limit speed at which the image capture device can normally capture real-time surface images of the target adhesive tape.

Preferably, the target adhesive tape may be provided on an adhesive tape transfer apparatus, and the adhesive tape transfer apparatus may be connected to the control apparatus and receive a transfer command of the control apparatus to transfer the target adhesive tape. Optionally, the adhesive tape conveying device may be an adhesive tape rewinding device, in an actual application scheme, an operator may open a power supply of a host of the rewinding device, feed the adhesive tape mother roll, start the machine, control the rewinding device to rewind, and start image acquisition and defect detection on the surface of the adhesive tape when the rewinding speed is adjusted to be greater than 2 m/min.

Therefore, by the optional implementation mode, the defect detection instruction can be generated to start detection when the real-time movement speed of the target adhesive tape meets the preset speed condition, so that the defect detection can be performed in a more appropriate speed environment, whether the target adhesive tape has defects or not can be judged more accurately, the detection efficiency is higher, the detection result is more accurate, and the production efficiency and the product quality can be effectively improved.

As an optional implementation, the method further comprises:

acquiring a defect detection requirement input by a user; specifically, the defect detection requirement is used to indicate a detection requirement for a defect of the target type of adhesive tape;

and determining the defect detection parameters of the defect detection model according to the defect detection requirements.

Specifically, the defect detection parameters are used to enable the defect detection model to meet the detection requirements when performing defect detection on the target adhesive tape. Optionally, when the defect detection model includes the classifier network model, the defect detection parameters may be confidence coefficient parameters for judgment of different defect types, and the confidence coefficient parameters are adjusted or determined, so that the accuracy of detecting different defect types can be adjusted. Optionally, when the defect detection model includes a plurality of difference intervals-defect correspondence relationships, the defect detection parameter may be an interval upper limit or an interval lower limit corresponding to different difference intervals, which may also adjust the precision of detecting different defect types.

Optionally, determining the defect detection parameters of the defect detection model according to the defect detection requirement may include:

determining a plurality of candidate detection parameters;

carrying out detection simulation on the defect detection model according to each candidate detection parameter to obtain a detection result;

and screening the defect detection parameters of the defect detection model from the candidate detection parameters according to the detection result and the defect detection requirement.

Specifically, for example, if a user requires to identify and remove the defect with the diameter of the missing coating adhesive layer larger than 2mm, that is, the defect with the diameter smaller than 2mm does not need to be identified, the defect detection requirement can be determined according to the defect detection requirement, and the above steps are performed to determine the defect detection parameters of the defect detection model, so that the defect detection model can automatically determine OK for the defect with the diameter smaller than 2mm without identifying, and thus, the time loss caused by misjudgment can be reduced.

Therefore, through the optional implementation mode, the defect detection parameters of the defect detection model can be determined according to the defect detection requirements input by the user, so that the defect detection of the defect detection model can better meet the requirements of the user, the detection efficiency is higher, and the production efficiency and the product quality can be effectively improved.

As an alternative embodiment, the defect information may include at least one of a defect image, a defect location, a defect size, a defect type, and a defect number.

Optionally, the method further includes:

acquiring all defect information corresponding to the target adhesive tape;

Optionally, the correspondence between the target adhesive tape and the defect information may also be a correspondence between the above-mentioned detected product parameter of the target adhesive tape and the defect information.

Optionally, the method may further include:

receiving a data viewing instruction sent by a user;

and determining corresponding target defect information from the database according to the adhesive tape information corresponding to the data checking instruction, and sending the target defect information to a user for checking.

Optionally, the method may further include:

receiving an analysis instruction of a user for a target work order;

determining all defect information corresponding to the target work order in a database;

analyzing all defect information corresponding to the target work order based on a preset defect analysis rule to obtain an analysis result corresponding to the target work order;

and sending the analysis result to a user for viewing.

Optionally, the analysis result may be an analysis report. Optionally, the defect analysis rules may be used to define classification and statistics for different types of defect information.

Therefore, through the optional implementation mode, the corresponding relation between the target adhesive tape and the defect information can be established, and the defect information is stored in the database, so that data analysis or checking and other operations can be performed subsequently by combining the stored defect information.

Example two

Referring to fig. 2, fig. 2 is a schematic structural diagram of an apparatus for detecting appearance defects of an adhesive tape according to an embodiment of the present invention. The adhesive tape appearance defect detection device described in fig. 2 may be applied to a data processing chip, a processing terminal, or a processing server (where the processing server may be a local server or a cloud server). As shown in fig. 2, the apparatus for detecting apparent defects of adhesive tape may include:

an image obtaining module 201, configured to obtain a real-time surface image of the target adhesive tape when the defect detection instruction is detected.

Optionally, the apparatus may further include a parameter input module, configured to receive the detected product parameter input by the user, and store the detected product parameter.

The gray scale calculation module 202 is configured to calculate a gray scale value parameter corresponding to the real-time surface image based on a gray scale algorithm according to the real-time surface image.

Optionally, the real-time surface image may be converted into a grayscale image based on a grayscaling algorithm, and then a grayscale value parameter corresponding to the real-time surface image is calculated according to a grayscale value corresponding to each pixel point in the grayscale image. Optionally, the gray-scale parameter may be a sum, an average, or a weighted average of gray-scale values corresponding to all pixel points in the gray-scale image, or may also be a sum, an average, or a weighted average of gray-scale values corresponding to all pixel points in a specific area in the gray-scale image.

And the defect judging module 203 is configured to judge whether the real-time surface image belongs to a defect image according to the gray-scale value parameter and a preset defect detection model, so as to obtain a first judgment result.

Optionally, the defect detection model may be a trained neural network model or a data analysis model, which may determine whether the real-time surface image belongs to the defect image according to the gray-scale parameter according to a data analysis algorithm. Optionally, the defect type of the defect image according to the present invention may include at least one of an under-coat layer defect, a diagonal defect, a cross grain defect, a color unevenness defect, and a foreign matter defect. The foreign matter defect can be mosquito and foreign matter existing on the adhesive tape or other foreign matter affecting the product quality.

And the defect recording module 204 is configured to record the position information and the defect information corresponding to the real-time surface image when the first determination result is yes.

As an optional embodiment, the apparatus further comprises a pigment marking module for performing the steps of:

and generating a marking instruction according to the marking position.

As an optional implementation, the apparatus further comprises an alarm module, configured to perform the following steps:

and if the first judgment result is yes, generating an alarm instruction.

As an optional implementation manner, the specific manner of determining, by the defect determining module 203, whether the real-time surface image belongs to the defect image according to the gray-scale value parameter and the preset defect detection model includes:

As an optional implementation manner, the apparatus further includes a speed determination module, configured to perform the following steps:

acquiring the real-time movement speed of the target adhesive tape;

As an optional implementation manner, the apparatus further includes a parameter adjusting module, configured to perform the following steps:

Specifically, the defect detection parameters are used to enable the defect detection model to meet the detection requirements when performing defect detection on the target adhesive tape. Optionally, when the defect detection model includes the classifier network model, the defect detection parameters may be confidence coefficient parameters for judgment of different defect types, and the confidence coefficient parameters are adjusted or determined, so that the accuracy of detecting different defect types can be adjusted. Optionally, when the defect detection model includes a plurality of difference interval-defect correspondence relationships, the defect detection parameter may be an interval upper limit or an interval lower limit corresponding to different difference intervals, which may also adjust the precision of detecting different defect types.

Optionally, the determining, by the parameter adjusting module, a specific manner of the defect detection parameters of the defect detection model according to the defect detection requirement may include:

determining a plurality of candidate detection parameters;

Optionally, the apparatus further includes a data saving module, configured to perform the following steps:

acquiring all defect information corresponding to the target adhesive tape;

Optionally, the apparatus may further include a data viewing module, configured to perform the following steps:

receiving a data viewing instruction sent by a user;

Optionally, the apparatus may further include a data analysis module, configured to perform the following steps:

receiving an analysis instruction of a user for a target work order;

and sending the analysis result to a user for viewing.

EXAMPLE III

Referring to fig. 3, fig. 3 is a schematic diagram illustrating another apparatus for detecting appearance defects of an adhesive tape according to an embodiment of the present invention. The adhesive tape appearance defect detection device described in fig. 3 can be applied to a data processing chip, a processing terminal or a processing server (wherein, the processing server can be a local server or a cloud server). As shown in fig. 3, the adhesive tape appearance defect detecting apparatus may include:

a memory 301 storing executable program code;

a processor 302 coupled to the memory 301;

the processor 302 calls the executable program code stored in the memory 301 for executing the steps of the method for detecting the defect on the appearance of the adhesive tape described in the first embodiment.

Example four

The embodiment of the invention discloses a computer-readable storage medium which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the adhesive tape appearance defect detection method described in the first embodiment.

EXAMPLE five

The embodiment of the invention discloses a computer program product, which comprises a non-transitory computer readable storage medium storing a computer program, wherein the computer program is operable to make a computer execute the steps of the adhesive tape appearance defect detection method described in the first embodiment.

EXAMPLE six

The embodiment of the invention discloses another adhesive tape appearance defect detection device which comprises a control device, and a pigment marking device, an adhesive tape conveying device, an image acquisition device and an alarm device which are connected to the control device. The control device is used for executing the steps of the adhesive tape appearance defect detection method described in the first embodiment. Specifically, for the technical details of the control device, the pigment marking device, the adhesive tape conveying device, the image obtaining device, the alarm device, and the like included in the apparatus, reference may be made to the corresponding description in the first embodiment, and details are not repeated herein.

While certain embodiments of the present disclosure have been described above, other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily have to be in the particular order shown or in sequential order to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, device, and non-volatile computer-readable storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.

The apparatus, the device, the nonvolatile computer readable storage medium, and the method provided in the embodiments of the present specification correspond to each other, and therefore, the apparatus, the device, and the nonvolatile computer storage medium also have similar advantageous technical effects to the corresponding method.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical blocks. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Language Description Language), traffic, pl (core unified Programming Language), HDCal, JHDL (Java Hardware Description Language), langue, Lola, HDL, laspam, hardsradware (Hardware Description Language), vhjhd (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium that stores computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.

As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.

The description has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. 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 apparatus that comprises the element.

This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

Finally, it should be noted that: the method and the device for detecting the appearance defects of the adhesive tape disclosed by the embodiment of the invention are only the preferred embodiment of the invention, and are only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting appearance defects of an adhesive tape is characterized by comprising the following steps:

calculating a gray scale value parameter corresponding to the real-time surface image based on a gray scaling algorithm according to the real-time surface image;

2. The method for detecting apparent defects in adhesive tape according to claim 1, further comprising:

and/or the presence of a gas in the gas,

3. The method for detecting the appearance defects of the adhesive tape according to claim 1, wherein the step of determining whether the real-time surface image belongs to a defect image according to the gray scale value parameter and a preset defect detection model comprises:

and/or the presence of a gas in the gas,

4. The adhesive tape appearance defect detection method according to claim 1, wherein before the time when the defect detection instruction is detected, the method further comprises:

acquiring the real-time movement speed of the target adhesive tape;

5. The method for detecting apparent defects in adhesive tape according to claim 1, further comprising:

6. The adhesive tape visual defect detection method according to claim 1, wherein the defect type of the defect image comprises at least one of a missing coating layer defect, a twill defect, a cross grain defect, a color unevenness defect, and a foreign matter defect; and/or the base material of the target adhesive tape is a PET film, a BOPP film or a PI film; and/or the color of the adhesive layer of the target adhesive tape is blue, green, transparent or red; and/or the thickness of the base material of the target adhesive tape is 6-150 um; and/or the thickness of the adhesive layer of the target adhesive tape is 2 um-150 um.

7. The adhesive tape visual defect detection method according to claim 1, wherein the defect information includes at least one of a defect image, a defect position, a defect size, a defect type, and a defect number; the method further comprises the following steps:

acquiring all defect information corresponding to the target adhesive tape;

8. The utility model provides an adhesive tape appearance imperfections detection device which characterized in that, the device includes:

the defect judging module is used for judging whether the real-time surface image belongs to a defect image or not according to the gray scale value parameter and a preset defect detection model to obtain a first judgment result;

9. The utility model provides an adhesive tape appearance imperfections detection device which characterized in that, the device includes:

a memory storing executable program code;

a processor coupled with the memory;

the processor calls the executable program code stored in the memory to execute the adhesive tape appearance defect detection method according to any one of claims 1 to 7.

10. The adhesive tape appearance defect detection device is characterized by comprising a control device, a pigment marking device, an adhesive tape conveying device, an image acquisition device and an alarm device, wherein the pigment marking device, the adhesive tape conveying device, the image acquisition device and the alarm device are connected to the control device; the control device is used for executing the adhesive tape appearance defect detection method according to any one of claims 1 to 7.