CN113808067A - Circuit board detection method, visual detection equipment and device with storage function - Google Patents
Circuit board detection method, visual detection equipment and device with storage function Download PDFInfo
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Abstract
The application discloses a circuit board detection method, a visual detection device and a device with a storage function, wherein the circuit board detection method comprises the following steps: acquiring an image to be detected, wherein the image to be detected comprises a circuit board to be detected, and at least one mounting area for mounting a component to be detected is arranged on the circuit board to be detected; extracting a characteristic image of the installation area from the image to be detected; and predicting whether the installation area is provided with the component to be tested or not by the pre-trained recognition model based on the characteristic image. The circuit board detection method provided by the application can judge whether components are installed in the installation area on the circuit board.
Description
Technical Field
The present disclosure relates to the field of circuit board technologies, and in particular, to a circuit board detection method, a visual inspection apparatus, and a device with a storage function.
Background
In recent years, as electronic technology has been developed, circuit boards have been rapidly developed as important components of electronic technology, wherein whether components are mounted on the circuit boards correctly or not is one of important factors determining the quality of the circuit boards.
The inventor of the application finds that in the process of installing components on a circuit board, due to the diversity of circuit board types, the difference of incoming materials of different components and the error of operators, the phenomenon of missing components on the circuit board can occur, so that the defective rate of the circuit board is increased, and serious influence is brought to enterprises and factories.
Disclosure of Invention
The technical problem mainly solved by the application is to provide a circuit board detection method, a visual detection device and a device with a storage function, and whether components are installed in a mounting area on a circuit board can be judged.
In order to solve the technical problem, the application adopts a technical scheme that: provided is a circuit board detection method, comprising the following steps: acquiring an image to be detected, wherein the image to be detected comprises a circuit board to be detected, and at least one mounting area for mounting a component to be detected is arranged on the circuit board to be detected; extracting a characteristic image of the installation area from the image to be detected; and predicting whether the component to be tested is installed in the installation area or not by a pre-trained recognition model based on the characteristic image.
In order to solve the above technical problem, another technical solution adopted by the present application is: there is provided a visual inspection apparatus comprising a processor, a memory and a communication circuit, the processor being coupled to the memory and the communication circuit respectively, the processor implementing the steps of the above method by executing program data in the memory.
In order to solve the above technical problem, another technical solution adopted by the present application is: there is provided an apparatus having a storage function, storing program data executable by a processor to implement the steps in the above method.
The beneficial effect of this application is: the circuit board detection method provided by the application predicts whether the component to be detected is installed in the installation area on the circuit board to be detected or not by utilizing the pre-trained recognition model based on the obvious difference between the characteristic image of the installation area provided with the component to be detected and the characteristic image of the installation area not provided with the component to be detected, and can detect whether the condition that the circuit board to be detected has the component to be detected which is not installed is detected.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a circuit board inspection method according to the present application;
FIG. 2 is a schematic flow chart diagram illustrating another embodiment of a circuit board inspection method according to the present application;
FIG. 3 is a schematic diagram of a configuration file structure;
FIG. 4 is a schematic flow chart diagram illustrating another embodiment of a circuit board inspection method according to the present application;
FIG. 5 is a schematic flow chart illustrating a variation of an image under test according to an embodiment;
FIG. 6 is a schematic partial flow chart diagram of another embodiment of a circuit board inspection method according to the present application;
FIG. 7 is a schematic flow chart illustrating training of a recognition model according to an embodiment;
FIG. 8 is a schematic flow chart subsequent to FIG. 7;
FIG. 9 is a schematic structural diagram of an embodiment of the vision inspection apparatus of the present application;
fig. 10 is a schematic structural diagram of an embodiment of the device with a storage function according to the present application.
Detailed Description
The technical solutions in 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 only a part of the embodiments of the present application, and not all 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 application.
Referring to fig. 1, fig. 1 is a schematic flow chart diagram of an embodiment of a circuit board detection method according to the present application, where the detection method includes:
s110: acquiring an image to be detected, wherein the image to be detected comprises a circuit board to be detected, and at least one mounting area for mounting a component to be detected is arranged on the circuit board to be detected.
In an application scenario, the vision inspection apparatus obtains an image to be inspected by shooting a circuit board to be inspected, for example, a camera is installed on the vision inspection apparatus, the circuit board to be inspected is shot by the camera, or in order to improve the definition of the image to be inspected, a vision inspection system composed of hardware such as an industrial camera, a lens, a coaxial light source and a photoelectric sensor is installed on the vision inspection apparatus, and the circuit board to be inspected is shot by the vision inspection system to obtain the image to be inspected. In another application scenario, the visual inspection device does not shoot the circuit board to be detected, but directly receives the image to be detected sent by other devices.
The circuit board to be tested in the image to be tested may be the whole circuit board to be tested or may be a part of the circuit board to be tested, for example, when only whether the component to be tested is installed in the installation area of the local area on the circuit board to be tested, the obtained image to be tested may only include the local area corresponding to the circuit board to be tested.
At least one mounting area on the circuit board to be tested can be a mounting area for mounting the same type of components to be tested, and can also be a mounting area for mounting different types of components to be tested.
S120: and extracting a characteristic image of the installation area from the image to be detected.
A feature image including features of the mounting area is extracted from the image to be measured using, for example, an image extraction technique.
The extracted feature image may include features of a plurality of mounting areas simultaneously or may include features of a single mounting area only.
S130: and predicting whether the installation area is provided with the component to be tested or not by the pre-trained recognition model based on the characteristic image.
The characteristic image of the mounting area provided with the component to be tested is obviously different from the characteristic image of the mounting area not provided with the component to be tested, so whether the component to be tested is mounted in the mounting area is predicted by using the recognition model based on the difference between the characteristic image and the characteristic image.
When the extracted feature images comprise the features of a plurality of installation areas, the feature images are input into the recognition model, the recognition model can predict whether the plurality of installation areas are provided with the components to be tested, when the extracted feature images only comprise the features of a single installation area, the extracted feature images can be input into the recognition model in sequence, and the recognition model can predict whether each installation area is provided with the components to be tested in sequence.
As can be seen from the above, in the embodiment, the pre-trained recognition model is used to predict whether the component to be tested is installed in the installation area on the circuit board to be tested based on the obvious difference between the feature image of the installation area where the component to be tested is installed and the feature image of the installation area where the component to be tested is not installed, so that whether the component to be tested is neglected to be installed in the circuit board to be tested can be detected.
Referring to fig. 2, fig. 2 is a schematic flow chart of another embodiment of the circuit board inspection method of the present application. The circuit board detection method comprises the following steps:
s210: the method comprises the steps of obtaining an image to be detected, wherein the image to be detected comprises a circuit board to be detected, at least one mounting area where components to be detected need to be mounted is arranged on the circuit board to be detected, and the mounting area is used for mounting a single component to be detected.
Unlike the above embodiments, the mounting area can mount only one device under test at most.
In other embodiments, the mounting area may mount a plurality of components to be tested at the same time, which is not limited herein.
S220: and carrying out segmentation processing on the image to be detected to obtain a sub image to be detected containing a single installation area.
S230: and extracting the image in the designated area in the sub image to be detected to obtain a characteristic image corresponding to the installation area in the sub image to be detected.
Specifically, the designated area in the sub image to be measured is a pre-designated partial area in the sub image to be measured, and the features in the partial area can reflect the overall features of the sub image to be measured.
S240: and predicting whether the component to be tested is installed in the installation area or not by a pre-trained recognition model based on the characteristic image.
In the embodiment, the characteristic image of the installation area is not directly extracted from the image to be detected, but the image to be detected is firstly segmented to obtain the sub image to be detected only containing a single installation area, then the image in the designated area is extracted from the sub image to be detected to obtain the characteristic image corresponding to the installation area, namely, the sub image to be detected is directly processed to obtain the characteristic image corresponding to the installation area in the sub image to be detected.
In an application scene, when an image to be detected is segmented, a high-precision positioning vision technology is used for segmentation.
Specifically, during segmentation, each mounting area in an image to be measured is positioned with high precision, and then is segmented, for example, a configuration file corresponding to a circuit board to be measured is generated in advance, positions of the mounting areas on the circuit board to be measured (for example, coordinate ranges of the mounting areas) are stored in the configuration file, and then each mounting area is positioned with high precision according to the configuration file, and then is segmented.
In an application scenario, the step S230 of extracting the image in the designated area in the sub-image to be measured specifically includes: and extracting the image at the appointed jack in the sub image to be detected.
Specifically, the mounting area is provided with jacks for mounting a component to be tested, the number of the jacks may be one, two or more, wherein a plurality of jacks on the mounting area are preset as designated jacks, and the designated jacks may be all jacks in the mounting area or part of jacks in the mounting area.
When the component to be detected is not installed in the installation area, obvious jacks exist in the sub-image to be detected, and when the component to be detected is installed in the installation area, the jacks do not exist in the sub-image to be detected due to the covering of the component to be detected, so that in order to guarantee the accurate prediction of a subsequent recognition model, an image at a specified jack in the sub-image to be detected is extracted to obtain a feature image corresponding to the installation area in the sub-image to be detected, namely, the subsequent recognition model predicts by using the general features of the jacks.
In the application scene, the step of extracting the image at the designated jack in the sub image to be measured to obtain the characteristic image corresponding to the installation area in the sub image to be measured includes:
a: and acquiring the pre-stored center coordinates and radius of the specified jack.
B: and arranging a circumscribed rectangular frame capable of framing the appointed jack on the sub-image to be detected according to the circle center coordinate and the radius of the appointed jack.
C: and extracting the image in the circumscribed rectangular frame.
D: and splicing the images in the circumscribed rectangular frame to obtain a characteristic image.
Specifically, the circle center coordinates and the radius of a designated jack in a designated sub image to be detected are obtained, then an external rectangular frame for framing the designated jack is determined, and then images in the external rectangular frame are extracted, wherein one designated jack corresponds to one external rectangular frame, and when the number of the designated jacks is more than two, the number of the external rectangular frames is also more than two, so that the images in the external rectangular frames are spliced, and the characteristic images corresponding to the installation areas in the sub images to be detected are obtained.
In other application scenarios, the extracted image may also be an image inside a circumscribed circle covering the designated jack, which is not limited herein.
In the present embodiment, the coordinates of the center of the circle and the radius of the designated jack are stored in a preset configuration file. That is, the center coordinates and the radius of the designated jack corresponding to each mounting area are set and stored in the visual inspection apparatus.
Meanwhile, the step A specifically comprises the following steps: and searching corresponding circle center coordinates and radius from the configuration file according to the component names which are pre-allocated to the components to be mounted in the mounting area.
Referring to fig. 3, the configuration file stores the component name of the component to be tested to be mounted in each mounting area, and the center coordinates and the radius of the designated jack in the mounting area, and after the characteristic image is obtained, the corresponding center coordinates and the radius are searched from the configuration file according to the component name of the component to be tested corresponding to each mounting area.
For example, in the application scenario of fig. 3, "RY 3" is the name of the component to be tested, "Hole 1" is the first designated socket in the mounting area corresponding to the component to be tested, and "Hole 2" is the second designated socket in the mounting area corresponding to the component to be tested. That is, in the application scenario of fig. 3, the images at two designated sockets in the sub-images to be measured are extracted to obtain corresponding feature images.
In other application scenarios, the corresponding circle center coordinates and radius can be searched in the configuration file according to the position of each installation area.
Specifically, the configuration file stores, in addition to the center coordinates and the radius of the designated jack, the position of each mounting area on the circuit board to be tested (for example, the coordinate position of the center of the mounting area), then in the obtained feature image, the position of the mounting area corresponding to the feature image on the circuit board to be tested is obtained by using, for example, a high-precision positioning technology, and then the corresponding center coordinates and the radius are searched in the configuration file according to the position.
Referring to fig. 4, fig. 4 is a schematic flow chart of another embodiment of the circuit board inspection method of the present application. The circuit board detection method comprises the following steps:
s310: the method comprises the steps of obtaining an image to be detected, wherein the image to be detected comprises a circuit board to be detected, at least one mounting area where components to be detected need to be mounted is arranged on the circuit board to be detected, and the mounting area is used for mounting a single component to be detected.
S320: and carrying out segmentation processing on the image to be detected to obtain a sub image to be detected containing a single installation area.
S330: and extracting images at two jacks with the farthest distance in the sub-images to be detected.
S340: and splicing the images at the two jacks with the farthest distance to obtain a characteristic image.
S350: and predicting whether the installation area is provided with the component to be tested or not by the pre-trained recognition model based on the characteristic image.
Referring to fig. 5, at least one mounting area 101 where a component 111 to be tested needs to be mounted is disposed on a circuit board to be tested in an image 100 to be tested, only one component 111 to be tested can be mounted in each mounting area 101, in an application scenario of fig. 5, a part of the mounting areas 101 are mounted with the component 111 to be tested, and a part of the mounting areas 101 are not mounted with the component 111 to be tested, where the mounting areas 101 mounted with the component 111 to be tested are shown by dotted lines, and the mounting areas 101 not mounted with the component 111 to be tested are shown by solid lines.
After the image 100 to be detected is obtained, the image 100 to be detected is segmented to obtain a plurality of sub images 110 to be detected, then the images at the first jack 102 and the second jack 103 in the sub images 110 to be detected are respectively extracted, then the two images are spliced to obtain a characteristic image 120, and finally whether the component 111 to be detected is installed in the installation area 101 is predicted based on the characteristic image 120.
Of course, in other embodiments, images at other jacks in the sub-test image may be extracted to obtain the feature image, for example, an image of a jack located at the center of the mounting area in the sub-test image is extracted to obtain the feature image, and in short, the application is not limited to extracting the image at a specific jack in the sub-test image to obtain the feature image.
Referring to fig. 6, fig. 6 is a schematic partial flow chart of another embodiment of the circuit board inspection method of the present application. In this embodiment, before the step of predicting whether the component to be tested is mounted in the mounting area based on the feature image by using the recognition model trained in advance, the method further includes:
s410: and acquiring a first sample image, wherein the first sample image comprises a sample circuit board, and a sample component is installed in the installation area of the sample circuit board.
S420: and acquiring a second sample image, wherein the second sample image comprises a sample circuit board, and a sample element is not installed in the installation area of the sample circuit board.
S430: and training the recognition model by taking one of the first sample image and the second sample image as a positive sample and taking the other one of the first sample image and the second sample image as a negative sample, wherein at least one mounting area is arranged on the sample circuit board.
The method for acquiring the first sample image and the second sample image is the same as the method for acquiring the sample image to be measured in the above embodiment, and details are not repeated here.
The sample circuit board and the circuit board to be tested can be the same in type or different in type, and the type of the sample component and the type of the component to be tested can be the same or different.
And one or more than two mounting areas are arranged on the sample circuit board.
In an application scenario, when the recognition model is trained, after a first sample image is acquired, the first sample image is taken as a positive sample and marked as '1', after a second sample image is acquired, the second sample image is taken as a negative sample and marked as '0', then an algorithm is used for performing two-class training on the positive sample and the negative sample to obtain the recognition model, and in an application scenario, the algorithm of a resnet basic network is used for performing two-class training on the positive sample and the negative sample to obtain the recognition model.
Specifically, in the training process, the recognition model is obtained based on the difference between the first sample image and the second sample image.
In an application scenario, each mounting area is used to mount a single sample component, and before step S430, the first sample image is segmented to obtain a first sub-image to be measured including the single mounting area, and the second sample image is segmented to obtain a second sub-image to be measured including the single mounting area, where step S430 specifically includes: and taking one of the first sub image to be detected and the second sub image to be detected as a positive sample, and taking the other one of the first sub image to be detected and the plurality of second sub images to be detected as a negative sample to train the recognition model.
In the application scenario, the first sample image and the second sample image are respectively segmented, the segmented first sub-image to be detected is used as a positive sample, and the segmented second sub-image to be detected is used as a negative sample to be trained to obtain the identification model, so that the complexity of data processing can be reduced, the difficulty of generating the identification model is reduced, and the accuracy of trained classification can be ensured.
In the application scenario, before training of the recognition model, images in a specified area in a first sub-image to be detected are extracted to obtain a first feature image, images in a specified area in a second sub-image to be detected are extracted to obtain a second feature image, and then the recognition model is obtained by training with the first feature image as a positive sample and the second feature image as a negative sample.
Specifically, the designated area in the first sub image to be measured is a pre-designated partial area in the first sub image to be measured, the features in the partial area can reflect the overall features of the first sub image to be measured, the designated area in the second sub image to be measured is a pre-designated partial area in the second sub image to be measured, and the features in the partial area can reflect the overall features of the second sub image to be measured.
When a first feature image corresponding to the first sub image to be detected is extracted, an image at a specified jack in the first sub image to be detected, for example, an image at two jacks with the farthest distance in the first sub image to be detected is extracted, and when a second feature image corresponding to the second sub image to be detected is extracted, an image at a specified jack in the second sub image to be detected, for example, an image at two jacks with the farthest distance in the second sub image to be detected is extracted.
Specifically, the process of extracting the image at the designated socket in the first sub-image to be measured and the process of extracting the image at the designated socket in the second sub-image to be measured are the same as the process of extracting the image at the designated socket in the sub-image to be measured in step S230, which may be referred to the above embodiment specifically, and are not described herein again.
When the jack images are extracted, the external rectangular images of the jacks are extracted, and the external rectangular images are spliced.
Specifically, the step of extracting an image at a designated socket in a first sub-image to be measured to obtain a first feature image includes: acquiring circle center coordinates and radius of a specified jack in a pre-stored first sub image to be detected; setting a circumscribed rectangular frame capable of framing the specified jack on the first sub-image to be detected according to the circle center coordinate and the radius of the specified jack; extracting an image in the circumscribed rectangular frame; splicing the images in the circumscribed rectangular frame to obtain a first characteristic image; the step of extracting the image at the designated jack in the second sub image to be detected to obtain a second characteristic image comprises the following steps: acquiring circle center coordinates and radius of a specified jack in a pre-stored second sub image to be detected; setting a circumscribed rectangular frame capable of framing the designated jack on the second sub-image to be detected according to the circle center coordinate and the radius of the designated jack; extracting an image in the circumscribed rectangular frame; and splicing the images in the circumscribed rectangular frame to obtain a second characteristic image.
How to extract the circumscribed rectangle image of the jack can be referred to the above related parts, and is not described herein again.
For a better understanding of the training process of the recognition model, the following description is made in conjunction with fig. 8 and 9.
After the first sample image 200 is obtained, the first sample image 200 is divided into a plurality of first sub-images to be detected 210, then images at two jacks 202 and 203 which are farthest away in the first sub-images to be detected 210 are extracted, and the images at the two jacks 202 and 203 are spliced to obtain a first characteristic image 220 corresponding to an installation area in the first sub-images to be detected 210; after the second sample image 300 is obtained, the second sample image 300 is divided into a plurality of second sub-images to be measured 310, then images at two jacks 302 and 303 farthest away in the second sub-images to be measured 310 are extracted, and the images at the two jacks 302 and 303 are spliced to obtain a second characteristic image 320 corresponding to the installation area in the second sub-image to be measured 310.
Then, the first feature image 220 is labeled as "1", the second feature image 320 is labeled as "0", and the first feature image 220 is used as a positive sample and the second feature image 320 is used as a negative sample to train to obtain the recognition model.
When the recognition model predicts a feature image extracted from an image to be detected, 0 or 1 is output, when the output is 0, it indicates that a component to be detected is not installed in the installation area, when the output is 1, it indicates that the component to be detected is installed in the installation area, and in an application scene, corresponding probabilities are output while 0 or 1 is output, for example, the output result of the recognition model is 0 and 98%, which indicates that the probability of the component to be detected being not installed in the installation area is 98%.
It can be understood that, when the recognition model is trained, if the label corresponding to the positive sample is "yes" and the label corresponding to the negative sample is "no", the recognition model outputs "yes" or "no" in the prediction.
In other application scenarios, the first sample image may be used as a negative sample, the second sample image may be used as a positive sample, and the recognition model is obtained through training.
In the above embodiment, the training process of the whole recognition model is independent of the types of the mounting areas and the sample components, so that the trained recognition model can predict whether components are mounted in various types of mounting areas, the universality of the recognition model is realized, the recognition model does not need to be retrained any more no matter how the components to be detected on the circuit board to be detected are updated, the workload of development can be reduced, the difficulty of the whole detection method is reduced, an independent model does not need to be established for each type of components to be detected, and the calculation amount and the cost of a processor can be reduced.
In any one of the above embodiments, in order to prompt an operator in time when it is determined that the component to be tested is not mounted in the mounting region, the circuit board detection method further includes: when the mounting area of the circuit board to be tested is predicted not to be mounted with the component to be tested, prompt information is sent, and the prompt information can be voice prompt information, light prompt information or a combination of the voice prompt information and the light prompt information, which is not limited herein.
Meanwhile, when the mounting area of the circuit board to be tested is predicted to be not provided with the component to be tested, in order to enable an operator to determine which mounting area is not provided with the component to be tested, the sent information also carries identification information of the mounting area which is not provided with the component to be tested, and the identification information can be the component name of the corresponding component to be tested or the position of the corresponding component to be tested on the circuit board to be tested.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an embodiment of the visual inspection apparatus of the present application. The visual inspection apparatus 800 includes a processor 810, a memory 820 and a communication circuit 830, the processor 810 is coupled to the memory 820 and the communication circuit 830, respectively, and the processor 810 implements the steps of any of the above methods by executing the program data in the memory 820, wherein the detailed methods can refer to the above embodiments and are not described herein again.
In the actual operation process, the visual inspection apparatus 800 may perform inspection on each circuit board to be inspected in the operation process, perform spot inspection on the circuit boards to be inspected at a certain time interval, or perform inspection on a specific circuit board to be inspected after receiving an instruction sent by a user.
Referring to fig. 10, fig. 10 is a device with a storage function of the present application, the device with a storage function 900 stores program data 910, and the program data 910 can be executed by a processor to implement steps of any one of the methods, where detailed methods can be referred to the above embodiments and are not described herein again.
The device 900 with a storage function may be a server, a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In summary, the circuit board detection method provided by the application can automatically judge whether the component to be detected is installed in the installation area of the circuit board to be detected, is simple, and can achieve the purposes of reducing cost and improving the popularization.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (15)
1. A circuit board detection method is characterized by comprising the following steps:
acquiring an image to be detected, wherein the image to be detected comprises a circuit board to be detected, and at least one mounting area for mounting a component to be detected is arranged on the circuit board to be detected;
extracting a characteristic image of the installation area from the image to be detected;
and predicting whether the component to be tested is installed in the installation area or not by a pre-trained recognition model based on the characteristic image.
2. The method of claim 1, wherein the mounting area is used for mounting a single component to be tested.
3. The method according to claim 2, wherein the step of extracting the feature image of the mounting area from the image to be tested comprises:
carrying out segmentation processing on the image to be detected to obtain a sub image to be detected containing a single installation area;
and extracting the image in the designated area in the sub image to be detected to obtain the characteristic image corresponding to the installation area in the sub image to be detected.
4. The method according to claim 3, wherein the step of extracting the image in the designated area in the sub image to be tested to obtain the feature image corresponding to the installation area in the sub image to be tested comprises:
and extracting the image at the appointed jack in the sub image to be detected so as to obtain the characteristic image corresponding to the installation area in the sub image to be detected.
5. The method of claim 4, wherein the step of extracting the image at the designated socket in the sub image to be tested to obtain the feature image corresponding to the mounting area in the sub image to be tested comprises:
extracting images at two jacks with the farthest distance in the sub images to be detected;
and splicing the images at the two farthest sockets to obtain the characteristic image.
6. The method of claim 4, wherein the step of extracting the image at the designated socket in the sub image to be tested to obtain the feature image corresponding to the mounting area in the sub image to be tested comprises:
obtaining the prestored circle center coordinate and radius of the specified jack;
setting a circumscribed rectangular frame capable of framing the specified jack on the sub-image to be detected according to the circle center coordinate and the radius of the specified jack;
extracting the image in the circumscribed rectangular frame;
and splicing the images in the circumscribed rectangular frame to obtain the characteristic image.
7. The method according to claim 6, wherein the circle center coordinates and the radius of the specified jack are stored in a preset configuration file;
the step of obtaining the pre-stored center coordinates and radius of the specified jack comprises:
and searching the corresponding circle center coordinate and radius from the configuration file according to the component name pre-allocated to the component to be mounted in the mounting area.
8. The method according to claim 1, wherein before the step of predicting whether the component to be tested is mounted on the mounting area based on the feature image by using a pre-trained recognition model, the method further comprises:
obtaining a first sample image, wherein the first sample image comprises a sample circuit board, and a sample component is installed in a mounting area of the sample circuit board;
acquiring a second sample image, wherein the second sample image comprises the sample circuit board, and the sample component is not installed in the installation area of the sample circuit board;
training the recognition model by taking one of the first sample image and the second sample image as a positive sample and taking the other of the first sample image and the second sample image as a negative sample;
wherein the sample circuit board is provided with at least one of the mounting regions.
9. The method of claim 8, wherein the mounting section is configured to mount a single sample component,
before the step of training the recognition model by using one of the first sample image and the second sample image as a positive sample and using the other of the first sample image and the second sample image as a negative sample, the method further includes:
performing segmentation processing on the first sample image to obtain a first sub-image to be detected containing a single installation area;
performing segmentation processing on the second sample image to obtain a second sub-image to be detected containing a single installation area;
the step of training the recognition model with one of the first sample image and the second sample image as a positive sample and the other of the first sample image and the second sample image as a negative sample includes:
and training the recognition model by taking one of the first sub-image to be detected and the second sub-image to be detected as a positive sample and taking the other one of the first sub-images to be detected and the second sub-images to be detected as a negative sample.
10. The method of claim 9, wherein the step of training the recognition model with one of the first sub-image to be measured and the second sub-image to be measured as a positive sample and the other of the first sub-images to be measured and the second sub-images to be measured as a negative sample comprises:
extracting an image in a designated area in the first sub image to be detected to obtain a first characteristic image;
extracting an image in a designated area in the second sub image to be detected to obtain a second characteristic image;
and training the recognition model by taking the first characteristic image as a positive sample and the second characteristic image as a negative sample.
11. The method of claim 10,
the step of extracting the image in the designated area in the first sub image to be detected to obtain a first characteristic image includes:
extracting an image at a designated jack in the first sub image to be detected to obtain the first characteristic image;
the step of extracting the image in the designated area in the second sub image to be detected to obtain a second characteristic image includes:
and extracting the image at the designated jack in the second sub image to be detected to obtain the second characteristic image.
12. The method of claim 11,
the step of extracting the image at the designated jack in the first sub image to be detected to obtain the first characteristic image comprises:
extracting images at two jacks with the farthest distance in the first sub image to be detected;
splicing images at two jacks with the farthest distance in the first sub image to be detected to obtain a first characteristic image;
the step of extracting the image at the designated jack in the second sub image to be detected to obtain the second characteristic image comprises the following steps:
extracting images at two jacks with the farthest distance in the second sub image to be detected;
and splicing the images at the two jacks with the farthest distance in the second sub image to be detected to obtain the second characteristic image.
13. The method of claim 11,
the step of extracting the image at the designated jack in the first sub image to be detected to obtain the first characteristic image comprises:
acquiring the circle center coordinates and the radius of the specified jack in the first sub-image to be detected which are stored in advance;
setting a circumscribed rectangular frame capable of framing the specified jack on the first sub-image to be detected according to the circle center coordinate and the radius of the specified jack;
extracting the image in the circumscribed rectangular frame;
splicing the images in the circumscribed rectangular frame to obtain the first characteristic image;
the step of extracting the image at the designated jack in the second sub image to be detected to obtain the second characteristic image comprises the following steps:
acquiring the circle center coordinates and the radius of the specified jack in the second sub-image to be detected which are stored in advance;
setting a circumscribed rectangular frame capable of framing the specified jack on the second sub-image to be detected according to the circle center coordinate and the radius of the specified jack;
extracting the image in the circumscribed rectangular frame;
and splicing the images in the circumscribed rectangular frame to obtain the second characteristic image.
14. A visual inspection apparatus comprising a processor, a memory and a communication circuit, the processor being coupled to the memory and the communication circuit respectively, the processor implementing the steps of the method of any one of claims 1-13 by executing program data in the memory.
15. An apparatus having a memory function, wherein program data are stored, which program data are executable by a processor to perform the steps of the method according to any of claims 1-13.
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