CN112967223A - Artificial intelligence-based textile detection system, method and medium - Google Patents
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Abstract
The invention discloses a textile detection system based on artificial intelligence, which comprises: the image acquisition module is used for acquiring an image of the textile to be detected; the image processing module is used for preprocessing the image of the textile to be detected to obtain a processed textile image; the intelligent analysis module establishes a textile knowledge map, a textile sample is adopted to train a convolutional neural network model, a textile test set is adopted to test the convolutional neural network model to obtain a trained detection model, a processed textile image to be detected is input into the trained detection model to be detected, a textile prediction result is obtained, and the prediction result is reversely transmitted to the convolutional neural network model to perform data updating; and the result output module is used for outputting the prediction result. The textile detection system based on artificial intelligence can carry out overall and automatic detailed detection on textiles, has high detection rate and low false alarm rate, and reduces labor cost.
Description
Technical Field
The invention relates to the technical field of textile detection, in particular to a textile detection system, method and medium based on artificial intelligence.
Background
At present, the manufacturing industry mostly adopts the traditional quality inspection means, and the traditional quality inspection means faces various challenges, especially the quality inspection of textiles, such as: a large amount of manpower is required for quality inspection, the labor cost is high, the work attraction is attractive, and the labor recruitment is difficult; the quality inspection level completely depends on the personal ability and stability of the inspection worker, so that fine flaws of the textile cannot be discovered; the traditional quality inspection equipment has low accuracy, high false alarm rate and poor flexibility; quality inspection data are not recorded, and cannot be deeply analyzed, and the process improvement cannot be helped.
Disclosure of Invention
Aiming at the defects in the prior art, the embodiment of the invention provides a textile detection system, method and medium based on artificial intelligence, which can automatically detect the quality of textiles, have high accuracy and reduce the labor cost.
In a first aspect, an embodiment of the present invention provides an artificial intelligence-based textile detection system, including: an image acquisition module, an image processing module, an intelligent analysis module and a result output module, wherein,
the image acquisition module is used for acquiring an image of the textile to be detected;
the image processing module is used for preprocessing the image of the textile to be detected to obtain a processed textile image;
the intelligent analysis module is used for establishing a textile knowledge map, training a convolutional neural network model by using a textile sample, testing the convolutional neural network model by using a textile test set to obtain a trained detection model, inputting a processed textile image to be detected into the trained detection model for detection to obtain a textile prediction result, and reversely transmitting the prediction result to the convolutional neural network model for data updating;
and the result output module is used for outputting a textile prediction result.
In a second aspect, an embodiment of the present invention provides an artificial intelligence-based textile detection method, including the following steps:
establishing a knowledge graph of the detected textile;
training a convolutional neural network model by using a textile sample, and testing the convolutional neural network model by using a textile test set to obtain a trained detection model;
collecting a textile image to be detected;
preprocessing a textile image to be detected to obtain a processed textile image to be detected;
inputting the processed textile image to be detected into a trained detection model for detection to obtain a prediction result, and reversely transmitting the prediction result to a convolutional neural network model for data updating;
and outputting the prediction result of the textile.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored, the computer program including program instructions, which, when executed by a processor, cause the processor to execute the method described in the above embodiment.
The invention has the beneficial effects that:
the textile detection system, method and medium based on artificial intelligence provided by the embodiment of the invention can be used for integrally and automatically detecting textiles in detail, the detection level of high detection rate and low false alarm rate is high, the detection capability is continuously improved along with the accumulation of detection data, excessive manual intervention is not needed, and the labor cost is reduced.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a block diagram illustrating an artificial intelligence based textile detection system according to a first embodiment of the present invention;
fig. 2 shows a flowchart of an artificial intelligence based textile detection method according to a second embodiment of the present invention.
Detailed Description
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 some, not all, embodiments of the present invention. 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.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
As shown in fig. 1, a first embodiment of the present invention provides a structural block diagram of an artificial intelligence based textile detection system, which includes: the system comprises a digital optical module, an image acquisition module, an image processing module, an intelligent analysis module and a result output module, wherein the image acquisition module is used for acquiring an image of a textile to be detected; the image processing module is used for preprocessing the image of the textile to be detected to obtain a processed textile image; the intelligent analysis module is used for establishing a textile knowledge map, training a convolutional neural network model by using a textile sample, testing the convolutional neural network model by using a textile test set to obtain a trained detection model, inputting a processed textile image to be detected into the trained detection model for detection to obtain a textile prediction result, and reversely transmitting the prediction result to the convolutional neural network model for data updating; and the result output module is used for outputting a textile prediction result. The system can detect the quality of various textiles, and the textiles comprise products such as cloth, shoelaces, towels and clothes.
In this embodiment, the intelligent analysis module includes a knowledge graph establishing unit, and the knowledge graph establishing unit obtains data of the detected textiles through a plurality of data access methods to establish a textile knowledge graph database. The data of the detected textiles can be automatically collected through the web crawler, the data of the detected textiles can also be accessed through a data transmission mode, and the applicability of the artificial intelligence technology in the field of textiles is enhanced by collecting sufficient data to establish a textile knowledge map database.
The knowledge graph establishing unit comprises a data preprocessing unit, an entity extracting unit, a relation extracting unit and a data storage unit, wherein the data preprocessing unit is used for filtering, sorting and cleaning collected textile data and converting non-text data into text data; the entity extraction unit is used for extracting entities and entity attributes in the data from the text data; the relation extraction unit is used for judging the relation between the entities; the data storage unit is used for carrying out data processing on the entity and the entity attribute which need to be stored, forming a corresponding key value and storing the key value into the database. By constructing different knowledge maps, the quality of the textile or the flaw of the textile can be detected.
In this embodiment, the intelligent analysis module includes a convolutional neural network model training unit, the convolutional neural network model training unit obtains an image of an objectionable textile, and labels an effective region where an objectionable feature on the image of the objectionable textile is located, where the effective region includes objectionable feature pixel points; and obtaining a training sample according to the image of the bad textile, the effective area information and the defect type information corresponding to the bad characteristics, and training the convolutional neural network model by using the training sample. For example: in the shoelace quality detection process, the plastic at the end part of the shoelace is completely wrapped on the shoelace to be a normal product, if part of the plastic at the end part is not wrapped on the shoelace, the plastic is a defective product, an image of the defective product is used as a training sample and is input into a convolutional neural network model for training, and therefore the defective product is identified.
The intelligent analysis module further comprises a detection unit, the detection unit detects the treated textile to be detected by using the trained detection model, and marks the detected defects.
The image acquisition module utilizes a CCD camera or a camera to shoot an image of a textile to be detected, the image acquisition module transmits the acquired image to the image processing module, the image processing module carries out preprocessing on the textile image to be detected, and the image preprocessing method comprises graying, geometric transformation and image enhancement to obtain a processed image. The intelligent analysis module adopts a textile sample to train a convolutional neural network model to obtain a trained detection model, inputs the processed textile image to be detected into the trained detection model for detection to obtain a prediction result, and reversely transmits the prediction result to the convolutional neural network model for data updating; and the result output module is used for outputting the prediction result. A large number of samples are used for training a convolutional neural network model, so that the textile prediction result is more and more accurate, and more textile products can be identified.
The textile detection system based on artificial intelligence provided by the embodiment of the invention realizes the integrated and automatic detailed detection of textiles, has high detection rate and low false alarm rate, and the detection capability is continuously improved along with the accumulation of detection data, so that excessive manual intervention is not needed, and the labor cost is reduced.
In the first embodiment, an artificial intelligence based textile detection system is provided, and correspondingly, the application also provides an artificial intelligence based textile detection method. Please refer to fig. 2, which is a flowchart illustrating an artificial intelligence based textile detection method according to a second embodiment of the present invention. Since the method embodiment is basically similar to the device embodiment, the description is simple, and the relevant points can be referred to the partial description of the device embodiment. The method embodiments described below are merely illustrative.
As shown in fig. 2, there is shown a flow chart of an artificial intelligence based textile detection method provided by a second embodiment of the present invention, which is applicable to the system described in the first embodiment, the method includes the following steps:
and S1, establishing a knowledge map for detecting the textile.
Specifically, the specific method for establishing the knowledge graph for detecting the textile comprises the following steps:
filtering, sorting and cleaning the collected textile data, and converting the non-text data into text data;
extracting entities and entity attributes in the data from the text data;
determining the relationship between the entities;
and carrying out data processing on the entity and the entity attribute to be stored to form a corresponding key value and storing the key value into the database.
And S2, training the convolutional neural network model by adopting the textile sample, and testing the convolutional neural network model by adopting a textile test set to obtain a trained detection model.
Specifically, the step of training the convolutional neural network model by using the textile sample to obtain the trained detection model specifically comprises the following steps:
acquiring an image of a defective textile, and labeling an effective region where a defective feature on the image of the defective textile is located, wherein the effective region comprises a defective feature pixel point;
and obtaining a training sample according to the image of the bad textile, the effective area information and the defect type information corresponding to the bad characteristics, and training the convolutional neural network model by using the training sample.
And S3, acquiring the textile image to be detected.
Specifically, a CCD camera or a camera is used for shooting an image of the textile to be detected.
And S4, preprocessing the textile image to be detected to obtain a processed textile image to be detected.
Specifically, the textile image to be detected is preprocessed, and the preprocessing method comprises graying, geometric transformation and image enhancement to obtain a processed image.
And S5, inputting the processed textile image to be detected into the trained detection model for detection to obtain a textile prediction result, and reversely transmitting the prediction result to the convolutional neural network model for data updating.
And S6, outputting the prediction result of the textile.
The textile detection method based on artificial intelligence provided by the embodiment of the invention realizes the integrated and automatic detailed detection of textiles, has high detection rate and low false alarm rate, continuously improves the detection capability along with the accumulation of detection data, does not need excessive manual intervention, and reduces the labor cost.
There is also provided in the third embodiment of the present invention a computer-readable storage medium storing a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method described in the second embodiment above.
The computer readable storage medium may be an internal storage unit of the terminal described in the foregoing embodiments, such as a hard disk or a memory of the terminal. The computer readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of the terminal. The computer-readable storage medium is used for storing the computer program and other programs and data required by the terminal. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the terminal and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed terminal and method can be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. A textile detection system based on artificial intelligence, comprising: an image acquisition module, an image processing module, an intelligent analysis module and a result output module, wherein,
the image acquisition module is used for acquiring an image of the textile to be detected;
the image processing module is used for preprocessing the image of the textile to be detected to obtain a processed image of the textile to be detected;
the intelligent analysis module is used for establishing a textile knowledge map, training a convolutional neural network model by using a textile sample, testing the convolutional neural network model by using a textile test set to obtain a trained detection model, inputting a processed textile image to be detected into the trained detection model for detection to obtain a textile prediction result, and reversely transmitting the prediction result to the convolutional neural network model for data updating;
and the result output module is used for outputting a textile prediction result.
2. The system of claim 1, wherein the intelligent analysis module comprises a knowledge graph building unit that obtains textile data via a plurality of data access methods to build a textile knowledge graph database.
3. The system of claim 2, wherein the knowledge-graph establishing unit comprises a data preprocessing unit, an entity extraction unit, a relationship extraction unit, and a data storage unit,
the data preprocessing unit is used for filtering, sorting and cleaning the collected textile data and converting the non-text data into text data;
the entity extraction unit is used for extracting entities and entity attributes in the data from the text data;
the relationship extraction unit is used for judging the relationship between the entities;
the data storage unit is used for carrying out data processing on the entity and the entity attribute to be stored, forming a corresponding key value and storing the key value into the database.
4. The system of claim 2, wherein the intelligent analysis module comprises a convolutional neural network model training unit, the convolutional neural network model training unit acquires an image of an objectionable textile, and labels an effective area where an objectionable feature on the image of the objectionable textile is located, the effective area comprising objectionable feature pixel points;
and obtaining a training sample according to the image of the bad textile, the effective area information and the defect type information corresponding to the bad characteristics, and training the convolutional neural network model by using the training sample.
5. The system of claim 4, wherein the intelligent analysis module further comprises a detection unit, and the detection unit detects the processed textile to be detected by using a trained detection model and marks the detected defects.
6. A textile detection method based on artificial intelligence is characterized by comprising the following steps:
establishing a knowledge graph of the detected textile;
training a convolutional neural network model by using a textile sample, and testing the convolutional neural network model by using a textile test set to obtain a trained detection model;
collecting a textile image to be detected;
preprocessing a textile image to be detected to obtain a processed textile image to be detected;
inputting the processed textile image to be detected into a trained detection model for detection to obtain a textile prediction result, and reversely transmitting the prediction result to a convolutional neural network model for data updating;
and outputting the prediction result of the textile.
7. The method of claim 6, wherein the specific method for establishing the knowledge-map of the test textile comprises:
filtering, sorting and cleaning the collected textile data, and converting the non-text data into text data;
extracting entities and entity attributes in the data from the text data;
determining the relationship between the entities;
and carrying out data processing on the entity and the entity attribute to be stored to form a corresponding key value and storing the key value into the database.
8. The method of claim 7, wherein training the convolutional neural network model with the textile sample to obtain the trained detection model specifically comprises:
acquiring an image of a defective textile, and labeling an effective region where a defective feature on the image of the defective textile is located, wherein the effective region comprises a defective feature pixel point;
and obtaining a training sample according to the image of the bad textile, the effective area information and the defect type information corresponding to the bad characteristics, and training the convolutional neural network model by using the training sample.
9. A computer-readable storage medium, storing a computer program comprising program instructions, which when executed by a processor cause the processor to perform the method of any of claims 6-8.
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