CN114708235A - Textile detection system based on artificial intelligence discernment AI - Google Patents
Textile detection system based on artificial intelligence discernment AI Download PDFInfo
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- CN114708235A CN114708235A CN202210381037.8A CN202210381037A CN114708235A CN 114708235 A CN114708235 A CN 114708235A CN 202210381037 A CN202210381037 A CN 202210381037A CN 114708235 A CN114708235 A CN 114708235A
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- 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
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- 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
- G06T7/001—Industrial image inspection using an image reference approach
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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- 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
- G06T2207/30124—Fabrics; Textile; Paper
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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Abstract
The application provides a fabrics detecting system based on artificial intelligence discernment AI belongs to fabrics and detects technical field. The textile detection system based on artificial intelligence AI identification comprises a micro control unit, a power supply module, a user interface module, a communication module, an AI identification module, a data acquisition module, a storage module and a detection terminal module, wherein the power supply module, the user interface module, the AI identification module, the data acquisition module and the storage module are all connected to the micro control unit, and the micro control unit is connected to the detection terminal module through the communication module. The artificial intelligence recognition AI technology is applied to textile detection, images are calibrated on the collected textile images, the defect evaluation values and the standard threshold interval are compared in the detection terminal module, the introduction of the new technology reduces the working intensity of personnel, reduces the poor missed detection and false detection, and improves the quality of finished products.
Description
Technical Field
The application relates to the field of textile detection, in particular to a textile detection system based on Artificial Intelligence (AI) recognition.
Background
Textile testing is a large category of tests that includes physical testing and chemical testing. At present, for the finished product detection of textile fabrics, intelligent robots for detecting defects of the textile fabrics come out in the market, and the inventor finds that the existing detection equipment has single function, lacks the application of AI identification technology, and is difficult to ensure the product quality due to missing detection and false detection.
How to invent a textile detection system based on artificial intelligence recognition AI to improve the problems becomes a problem to be solved by the technical personnel in the field.
Disclosure of Invention
In order to make up for the above deficiencies, the present application provides a textile detection system based on artificial intelligence AI recognition, which aims to solve the problems presented in the above background art.
The embodiment of the application provides a textile detection system based on Artificial Intelligence (AI) identification, which comprises a micro control unit, a power supply module, a user interface module, a communication module, an AI identification module, a data acquisition module, a storage module and a detection terminal module, wherein the power supply module, the user interface module, the AI identification module, the data acquisition module and the storage module are all connected to the micro control unit, and the micro control unit is connected to the detection terminal module through the communication module;
the textile detection system based on artificial intelligence AI identification specifically comprises the following steps:
s1: acquiring textile images, namely acquiring the image data of the textile to be detected by using a data acquisition module;
s2: calibration image: determining an ideal calibration angle by using Hough transformation based on the textile image acquired in the step S1, and rotating the image at the ideal calibration angle to obtain a calibrated image;
s3: and (3) dividing the lattice-like pattern: segmenting the calibrated image to produce a lattice-like image;
s4: comparing the obtained product image with the standard image to find out pixel points with different colors;
s5: based on the found pixel points with different colors, obtaining the average color value and the area of the defect;
s6: comparing the defect evaluation value with a standard threshold interval in the detection terminal module;
s7: and judging the product grade to finish product detection.
In a specific embodiment, this fabrics detecting system based on artificial intelligence discernment AI still includes intelligent light filling module, intelligence light filling module includes light response module and light filling lamp, light response module is including the light sensor that can detect optical fiber strength, and light filling lamp electric connection is in little the control unit, and when light intensity was less than the standard threshold interval of settlement, little the control unit control light filling lamp carried out the light filling.
In a specific implementation scheme, the textile detection system based on artificial intelligence AI identification further comprises a positioning module, wherein the positioning module can identify the IP address of the detection equipment, so that later maintenance personnel can find the textile detection equipment quickly.
In a specific embodiment, the data acquisition module comprises a plurality of groups of synchronously arranged camera components.
In a specific embodiment, the camera assembly is a plurality of cameras or a plurality of digital cameras.
In a specific embodiment, the user interface module comprises an LED display module and an alarm module, the alarm module can complete alarm according to the detection result, and the LED display module can display the alarm information of the alarm module.
In a specific embodiment, the micro control unit comprises a single chip microcomputer controller, and the single chip microcomputer controller adopts a built-in STM32F103RB single chip microcomputer chip.
In a specific embodiment, the step S6 further includes an online verification module, and the detection worker can perform online verification on the suspected defect type to accurately judge the quality of the textile.
In a specific embodiment, the AI identification module can screen the target animal data for storage or non-storage, upload or non-upload, or other classification or classification operations.
In a specific embodiment, the automatic AI identification of defects in step S6 includes a broken weft judgment, a broken hole judgment, a stain judgment, a crease judgment, a snag judgment, a scratch judgment, a broken weft judgment, a stain judgment, and a stain judgment.
Has the advantages that: the application provides a fabrics detecting system based on artificial intelligence discernment AI, be applied to the fabrics with artificial intelligence discernment AI technique and detect, utilize the collection of data acquisition module to wait to detect fabrics image data, then carry out calibration image to the fabrics image of gathering, cut apart the back image of calibration in order to produce class check image, compare the product image that will obtain with the standard image, find out the pixel point that the colour is different, and then obtain defect average color value and defect area, compare defect evaluation value and standard threshold value interval in the test terminal module, finally judge the product grade, accomplish the product detection, the introduction of new technology, personnel's working strength has been reduced, bad hourglass is examined the false retrieval, finished product quality has been improved.
Drawings
In order to more clearly explain the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a system block diagram of a textile detection system based on artificial intelligence AI identification provided in an embodiment of the present application;
fig. 2 is a flowchart provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present application are within the scope of protection of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 identical elements in a process, method, article, or apparatus that comprises the element.
Please refer to fig. 1 and 2, in which the present application provides a textile detection system based on artificial intelligence AI, fig. 1 is a system block diagram of the textile detection system based on artificial intelligence AI according to the present application, and the system block diagram includes a micro control unit, a power supply module, a user interface module, a communication module, an AI identification module, a data acquisition module, a storage module, and a detection terminal module, wherein the power supply module, the user interface module, the AI identification module, the data acquisition module, and the storage module are all connected to the micro control unit, and the micro control unit is connected to the detection terminal module through the communication module;
referring to fig. 2, fig. 2 is a flowchart of an embodiment of the present application, and the textile detection system based on artificial intelligence AI specifically includes the following steps:
s1: the method comprises the steps of collecting textile images, wherein a data collection module is used for collecting the data of the textile images to be detected, the data collection module comprises a plurality of groups of camera shooting assemblies which are arranged synchronously, the camera shooting assemblies are a plurality of cameras or a plurality of digital cameras, the cameras and the digital cameras at different positions in the multi-path camera shooting assemblies can use uniform shooting parameters, the camera shooting assemblies are not influenced by a shot object, the uniformity of the shooting image parameters is realized, and a uniform standard is provided for a subsequent computer processing system to process the images;
s2: calibration image: determining an ideal calibration angle by using Hough transformation based on the textile image acquired in the step S1, and rotating the image at the ideal calibration angle to obtain a calibrated image, wherein image calibration is established on analysis of background pixel distribution in the components of the binarized textile image;
s3: and (3) dividing the lattice-like pattern: segmenting the calibrated image to generate a grid-like image, wherein the grid-like image may include a plurality of similar clustering centers due to the randomness of the data, and the minimum value of the similar clustering centers is respectively selected as a threshold value, for example, the minimum value is arranged in a descending order, and the absolute value of the difference between the two elements is calculated;
s4: comparing the obtained product image with the standard image to find out pixel points with different colors;
s5: obtaining the average color value of the defect and the defect area based on the found pixel points with different colors, wherein the sum of the number of the defect points can be obtained by finding the number of the pixel points with different colors, and then the defect area is obtained, and the defect area is the sum of the number of the pixel points which are the defect points in the image;
s6: comparing the defect evaluation value with a standard threshold interval in the detection terminal module;
s7: judging the grade of a product, and finishing product detection, wherein when the artificial intelligence identification AI-based textile detection system is used, the product is judged to be a defective product as long as the evaluation value of one defect in the product is greater than or equal to a set threshold value; when all the defect evaluation values of the product are less than the set threshold value, the product is judged to be a qualified product.
What need to explain, still include intelligent light filling module, intelligence light filling module includes light response module and light filling lamp, light response module is including the optical line sensors that can detect optical fiber strength, and light filling lamp electric connection is in little the control unit, and when light intensity was less than the standard threshold interval of setting for, little the control unit light filling lamp of control carries out the light filling, is favorable to improving image acquisition data's accuracy today.
In some specific embodiments, the fabric product detection device further comprises a positioning module, wherein the positioning module can identify the IP address of the detection device, so that later maintenance personnel can find the fabric product detection device quickly.
When the user interface module is specifically set, the user interface module comprises an LED display module and an alarm module, the alarm module can complete alarm according to a detection result, and the LED display module can display alarm information of the alarm module.
It should be noted that the micro control unit includes a single chip microcomputer controller, the single chip microcomputer controller adopts a built-in STM32F103RB single chip microcomputer chip, and the power supply and the principle of the single chip microcomputer controller are clear to those skilled in the art, and will not be described in detail herein.
The step S6 further includes an online verification module, which allows a detection worker to perform online verification of the suspected defect type and accurately determine the quality of the textile.
The AI identification module can screen target animal data and then store or not store, upload or not upload, or perform other classification or classification operations, and images which are not in a detection range can be deleted by screening, so that local memory is saved, data transmission load is reduced, and improvement of textile detection efficiency is facilitated.
The automatic AI identification contents of the defects in the step S6 include broken weft judgment, broken hole judgment, stain judgment, crease judgment, snag judgment, scratch judgment, broken weft judgment, stain judgment and stain judgment, the identification items are various, and the intelligent detection of the defects is realized through the AI technology.
This fabrics detecting system based on artificial intelligence discernment AI's theory of operation: the method comprises the steps of firstly utilizing a data acquisition module to acquire textile image data to be detected, then calibrating an acquired textile image, segmenting the calibrated image to generate a lattice-like image, comparing an acquired product image with a standard image, finding out pixel points with different colors, further acquiring a defect average color value and a defect area, comparing a defect evaluation value with a standard threshold interval in a detection terminal module, finally judging the product grade, completing product detection, introducing a new technology, reducing the working strength of personnel, reducing the defective missing detection and false detection, and improving the product quality of finished products.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A textile detection system based on artificial intelligence identification (AI) is characterized by comprising a micro control unit, a power supply module, a user interface module, a communication module, an AI identification module, a data acquisition module, a storage module and a detection terminal module, wherein the power supply module, the user interface module, the AI identification module, the data acquisition module and the storage module are all connected to the micro control unit, and the micro control unit is connected to the detection terminal module through the communication module;
the textile detection system based on artificial intelligence AI identification specifically comprises the following steps:
s1: acquiring textile images, namely acquiring the image data of the textile to be detected by using a data acquisition module;
s2: calibration image: determining an ideal calibration angle by using Hough transformation based on the textile image acquired in the step S1, and rotating the image at the ideal calibration angle to obtain a calibrated image;
s3: and (3) dividing the lattice-like pattern: segmenting the calibrated image to produce a lattice-like image;
s4: comparing the obtained product image with the standard image to find out pixel points with different colors;
s5: based on the found pixel points with different colors, obtaining the average color value and the area of the defect;
s6: comparing the defect evaluation value with a standard threshold interval in the detection terminal module;
s7: and judging the product grade to finish the product detection.
2. The textile detection system based on artificial intelligence discernment AI of claim 1, characterized in that, still includes intelligent light filling module, intelligent light filling module includes light sensing module and light filling lamp, light sensing module includes the light sensor that can detect optical fiber intensity, light filling lamp electric connection in little the control unit, when light intensity is less than the standard threshold interval of settlement, little the control unit control light filling lamp carries out the light filling.
3. The AI-based textile detection system of claim 1, further comprising a location module that can identify the IP address of the detection device to facilitate later maintenance personnel to quickly find the textile detection device.
4. The AI-based textile detection system of claim 1, wherein the data collection module comprises a plurality of sets of synchronized camera modules.
5. The AI-based textile detection system according to claim 4, wherein the camera assembly is a plurality of cameras or a plurality of digital cameras.
6. The artificial intelligence AI-based textile detection system according to claim 1, wherein the user interface module comprises an LED display module and an alarm module, the alarm module can complete alarm according to the detection result, and the LED display module can display the alarm information of the alarm module.
7. The artificial intelligence AI-based textile detection system according to claim 1, wherein the micro control unit includes a single chip microcomputer controller, and the single chip microcomputer controller employs a built-in STM32F103RB single chip microcomputer chip.
8. The artificial intelligence AI-based textile detection system according to claim 1, further comprising an online verification module in step S6, wherein the detection worker can perform online verification on the suspected defect type to accurately determine the quality of the textile.
9. The artificial intelligence AI-based textile detection system of claim 1, wherein the AI identification module is capable of performing operations of storing or not storing, uploading or not uploading, or other classification or classification processes after screening target animal data.
10. The AI-based textile detection system according to claim 1, wherein the automatic AI identification of defects in step S6 comprises weft breakage determination, hole breakage determination, stain determination, crease determination, snag determination, scratch determination, weft breakage determination, color stain determination, and stain determination.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210381037.8A CN114708235A (en) | 2022-04-12 | 2022-04-12 | Textile detection system based on artificial intelligence discernment AI |
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| CN202210381037.8A CN114708235A (en) | 2022-04-12 | 2022-04-12 | Textile detection system based on artificial intelligence discernment AI |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117893467A (en) * | 2023-12-06 | 2024-04-16 | 江苏新丝路纺织科技有限公司 | A method for identifying textile defect types |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117893467A (en) * | 2023-12-06 | 2024-04-16 | 江苏新丝路纺织科技有限公司 | A method for identifying textile defect types |
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