CN112967224A - Electronic circuit board detection system, method and medium based on artificial intelligence - Google Patents
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Abstract
The invention discloses an electronic circuit board detection system based on artificial intelligence, which comprises: the digital optical module controls a light source to irradiate the electronic circuit board to be detected in a digital mode; the visual detection module is used for acquiring an image of the electronic circuit board to be detected under the irradiation of a light source under the control of a digital mode; the image processing module is used for preprocessing an image of the electronic circuit board to be detected to obtain a processed image of the electronic circuit board; the intelligent analysis module is used for establishing a knowledge map, training and testing the convolutional neural network model to obtain a trained detection model, inputting the processed electronic circuit board image to be detected into the trained detection model for detection to obtain a 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 the prediction result. The system can carry out overall and automatic detailed detection on the electronic circuit board, has high detection rate and low false alarm rate, and reduces labor cost.
Description
Technical Field
The invention relates to the technical field of electronic circuit board detection, in particular to an electronic circuit board 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 electronic circuit boards, 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 detection workers, so that the quality of the electronic circuit board is unstable; 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 an electronic circuit board detection system, method and medium based on artificial intelligence, which can automatically detect the quality of an electronic circuit board, have high accuracy and reduce the labor cost.
In a first aspect, an electronic circuit board detection system based on artificial intelligence provided in an embodiment of the present invention includes: a digital optical module, a visual detection module, an image processing module, an intelligent analysis module and a result output module, wherein,
the digital optical module controls a light source to irradiate the electronic circuit board to be detected in a digital mode;
the visual detection module is used for acquiring an image of the electronic circuit board to be detected under the irradiation of a light source under the control of a digital mode;
the image processing module is used for preprocessing an image of the electronic circuit board to be detected to obtain a processed image of the electronic circuit board;
the intelligent analysis module is used for establishing a knowledge map, training a convolutional neural network model by adopting an electronic circuit board sample, testing the convolutional neural network model by adopting an electronic circuit board test set to obtain a trained detection model, inputting the processed electronic circuit board image to be detected into the trained detection model for detection to obtain a 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 prediction result.
In a second aspect, an electronic circuit board detection method based on artificial intelligence provided in an embodiment of the present invention includes the following steps:
establishing a knowledge graph of the electronic circuit board to be detected;
training a convolutional neural network model by adopting an electronic circuit board sample, and testing the convolutional neural network model by adopting an electronic circuit board test set to obtain a trained detection model;
acquiring an electronic circuit board image to be detected, which is acquired under the irradiation of a light source under the control of a digital mode;
preprocessing an electronic circuit board image to be detected to obtain a processed electronic circuit board image to be detected;
inputting the processed electronic circuit board 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 a prediction result.
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 electronic circuit board detection system, the method and the medium based on artificial intelligence provided by the embodiment of the invention have the advantages that the electronic circuit board is integrally and automatically detected in detail, the detection level of high detection rate and low false alarm rate is realized, 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.
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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 a first embodiment of an artificial intelligence-based electronic circuit board detection system according to the present invention;
fig. 2 shows a flowchart of an artificial intelligence based electronic circuit board inspection 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 electronic circuit board detection system based on artificial intelligence, where the system includes: the system comprises a digital optical module, a visual detection module, an image processing module, an intelligent analysis module and a result output module, wherein the digital optical module controls a light source to irradiate an electronic circuit board to be detected in a digital mode; the visual detection module is used for acquiring an image of the electronic circuit board to be detected under the irradiation of a light source under the control of a digital mode; the image processing module is used for preprocessing an image of the electronic circuit board to be detected to obtain a processed image of the electronic circuit board; the intelligent analysis module is used for establishing a knowledge map, training a convolutional neural network model by adopting an electronic circuit board sample, testing the convolutional neural network model by adopting an electronic circuit board test set to obtain a trained detection model, inputting the processed electronic circuit board image to be detected into the trained detection model for detection to obtain a 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 prediction result.
In this embodiment, the digital optical module controls a light source wavelength, an entrance angle, an irradiation area, and/or a refraction angle. The light source is adjusted through the digital optical module, the light source wavelength, the irradiation angle, the cut-in angle, the irradiation area and/or the refraction angle which are suitable for the electronic circuit board are found, so that the visual detection module can not reflect light when shooting, the image of the electronic circuit board collected by the visual module can clearly reflect different defects of the electronic circuit board, the intelligent analysis module can clearly identify defects of the electronic circuit board, such as the problems of insufficient soldering, missing soldering and the like of the electronic circuit board, and the product detection effect is improved.
In this embodiment, the intelligent analysis module includes a knowledge graph establishing unit, and the knowledge graph establishing unit obtains data for detecting the electronic circuit board by a plurality of data access methods to establish a knowledge graph database. The data of the electronic circuit board can be automatically collected and detected through the web crawler, the data of the electronic circuit board can also be accessed and detected through a data transmission mode, and the application of the artificial intelligence technology in the field of the electronic circuit board is enhanced by collecting sufficient data to establish a 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 the collected electronic circuit board 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 electronic circuit board can be detected or the flaws of the electronic circuit board 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 a bad electronic circuit board, and labels an effective region where a bad feature on the image of the bad electronic circuit board is located, where the effective region includes bad feature pixel points; and obtaining a training sample according to the image of the bad electronic circuit board, 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. The intelligent analysis module further comprises a detection unit, the detection unit detects the processed electronic circuit board to be detected by using the trained detection model, and marks the detected defects.
The vision detection module shoots an image of the electronic circuit board to be detected by using the CCD camera, the vision detection module transmits the collected image to the image processing module, the image processing module preprocesses the image of the electronic circuit board to be detected, and the preprocessing method comprises graying, geometric transformation and image enhancement to obtain a processed image. The intelligent analysis module adopts an electronic circuit board sample to train a convolutional neural network model, adopts an electronic circuit board test set to test the convolutional neural network model to obtain a trained detection model, inputs the processed electronic circuit board image to be detected into the trained detection model to detect to obtain a prediction result, and reversely transmits the prediction result to the convolutional neural network model to update data; and the result output module is used for outputting the prediction result. The convolutional neural network model is trained through a large number of samples, so that the prediction result is more and more accurate.
The electronic circuit board detection system based on artificial intelligence provided by the embodiment of the invention realizes the overall and automatic detailed detection of the electronic circuit board, has the detection levels of high detection rate and low false alarm rate, and the detection capability is continuously improved along with the accumulation of detection data without excessive manual intervention, thereby reducing the labor cost.
In the first embodiment, an electronic circuit board detection system based on artificial intelligence is provided, and correspondingly, the application further provides an electronic circuit board detection method based on artificial intelligence. Please refer to fig. 2, which is a flowchart illustrating an artificial intelligence based electronic circuit board inspection 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 flowchart of an artificial intelligence based electronic circuit board detection method provided by a second embodiment of the present invention, which is applicable to the system described in the first embodiment, and the method includes the following steps:
and S1, establishing a knowledge graph for detecting the electronic circuit board.
Specifically, the specific method for establishing the knowledge graph for detecting the electronic circuit board comprises the following steps:
filtering, sorting and cleaning the collected electronic circuit board data, and converting 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 electronic circuit board sample, and testing the convolutional neural network model by adopting the electronic circuit board test set to obtain the trained detection model.
Specifically, the training of the convolutional neural network model by using the electronic circuit board sample specifically includes:
acquiring an image of a bad electronic circuit board, and marking an effective area where a bad feature on the image of the bad electronic circuit board is located, wherein the effective area comprises bad feature pixel points;
and obtaining a training sample according to the image of the bad electronic circuit board, 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 electronic circuit board image to be detected, which is acquired under the irradiation of the light source under the digital control.
Specifically, a CCD camera is used for shooting an image of the electronic circuit board to be detected. The digital optical module is used for controlling the wavelength, the cut-in angle, the irradiation area and/or the refraction angle of the light source, so that the visual detection module can not reflect light when shooting, images of the electronic circuit board acquired by the visual module can clearly show different defects of the electronic circuit board, the intelligent analysis module can conveniently and clearly identify flaws of the electronic circuit board, such as problems of insufficient soldering, missing soldering and the like of the electronic circuit board, and the product detection effect is improved.
And S4, preprocessing the image of the electronic circuit board to be detected to obtain a processed image of the electronic circuit board to be detected.
Specifically, an electronic circuit board 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 electronic circuit board image to be detected into the trained detection model for detection to obtain a prediction result, and reversely transmitting the prediction result to the convolutional neural network model for data updating.
And S6, outputting the prediction result.
The electronic circuit board detection method based on artificial intelligence provided by the embodiment of the invention realizes the overall and automatic detailed detection of the electronic circuit board, has the detection levels of high detection rate and low false alarm rate, and the detection capability is continuously improved along with the accumulation of detection data without excessive manual intervention, thereby reducing 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 (10)
1. An electronic circuit board detection system based on artificial intelligence, comprising: a digital optical module, a visual detection module, an image processing module, an intelligent analysis module and a result output module, wherein,
the digital optical module controls a light source to irradiate the electronic circuit board to be detected in a digital mode;
the visual detection module is used for acquiring an image of the electronic circuit board to be detected under the irradiation of a light source under the control of a digital mode;
the image processing module is used for preprocessing an image of the electronic circuit board to be detected to obtain a processed image of the electronic circuit board to be detected;
the intelligent analysis module is used for establishing a knowledge map, training a convolutional neural network model by adopting an electronic circuit board sample, testing the convolutional neural network model by adopting an electronic circuit board test set to obtain a trained detection model, inputting the processed electronic circuit board image to be detected into the trained detection model for detection to obtain a 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 prediction result.
2. The system of claim 1, wherein the digital light module controls a light source wavelength, an entrance angle, an illumination area, and/or a refraction angle.
3. The system of claim 2, wherein the intelligent analysis module comprises a knowledge graph establishing unit that obtains electronic circuit board data via a plurality of data access methods to establish a knowledge graph database.
4. The system of claim 3, 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 electronic circuit board 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.
5. The system of claim 1, wherein the intelligent analysis module comprises a convolutional neural network model training unit, wherein the convolutional neural network model training unit acquires an image of a defective electronic circuit board and labels an effective area where a defective feature on the image of the defective electronic circuit board is located, and the effective area comprises defective feature pixel points;
and obtaining a training sample according to the image of the bad electronic circuit board, 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.
6. The system of claim 5, wherein the intelligent analysis module further comprises a detection unit, and the detection unit detects the processed electronic circuit board to be detected by using a trained detection model and marks the detected defects.
7. An electronic circuit board detection method based on artificial intelligence is characterized by comprising the following steps:
establishing a knowledge graph of the electronic circuit board to be detected;
training a convolutional neural network model by adopting an electronic circuit board sample, and testing the convolutional neural network model by adopting an electronic circuit board test set to obtain a trained detection model;
acquiring an electronic circuit board image to be detected, which is acquired under the irradiation of a light source under the control of a digital mode;
preprocessing an electronic circuit board image to be detected to obtain a processed electronic circuit board image to be detected;
inputting the processed electronic circuit board 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 a prediction result.
8. The method of claim 7, wherein the specific method of establishing the knowledge-map of the test electronic circuit board comprises:
filtering, sorting and cleaning the collected electronic circuit board data, and converting 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.
9. The method of claim 7, wherein the training of the convolutional neural network model with the electronic circuit board samples specifically comprises:
acquiring an image of a bad electronic circuit board, and marking an effective area where a bad feature on the image of the bad electronic circuit board is located, wherein the effective area comprises bad feature pixel points;
and obtaining a training sample according to the image of the bad electronic circuit board, 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.
10. 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 7-9.
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| CN116840693A (en) * | 2023-06-30 | 2023-10-03 | 深圳市盛弘新能源设备有限公司 | Charge and discharge test control method and system based on artificial intelligence |
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Application publication date: 20210615 |