CN116245808A - Workpiece defect detection method and device, electronic equipment and storage medium - Google Patents
Workpiece defect detection method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application discloses a workpiece defect detection method, a workpiece defect detection device, electronic equipment and a storage medium, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a first workpiece image of a workpiece to be measured; inputting the first workpiece image into a defect detection model to obtain first defect information output by the defect detection model; wherein inputting the first workpiece image to the defect detection model, obtaining the first defect information output by the defect detection model further comprises: performing mask processing on a mask region of the first workpiece image to obtain a second workpiece image, wherein the mask region of the first workpiece image is determined based on a target defect distribution density map; performing defect detection on the second workpiece image to obtain first defect information; the defect detection model is obtained based on sample image sets and corresponding sample labels, and the target defect distribution density map is used for representing the relative density of each defect type distribution in the sample image sets. The method can accurately position the mask region in the image.
Description
Technical Field
The application belongs to the technical field of image processing, and particularly relates to a workpiece defect detection method, a device, electronic equipment and a storage medium.
Background
With the development of image processing technology and the upgrade of visual hardware, the defect detection method of machine vision gradually replaces manual detection, is verified and widely applied in the industrial production detection link, and the machine vision detection technology is a non-contact automatic detection technology, has the advantages of safety, reliability, high detection precision, long-time operation in a complex production environment and the like, and is an important technology for realizing industrial intellectualization and automation.
The machine vision detection firstly converts a shooting target into an image signal through a machine vision device and transmits the image signal to a special image processing system, the image signal is converted into a digital signal according to information such as pixel distribution, brightness and color, the image processing system carries out various operations on the signals to obtain characteristics of the target, the detection is carried out, and then the actions of other devices of the production line are controlled according to the detection result.
Machine vision defect detection technology is becoming more and more popular, and a common deep learning segmentation model is used for realizing vision detection, so that when an acquired image is large, image detection and model training are both very time-consuming, and usually, an area which does not need to be detected in an original image is masked so as to reduce an image analysis area.
Therefore, how to determine the mask area and improve the detection efficiency is a technical problem to be solved.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the application provides a workpiece defect detection method, device, electronic equipment and storage medium, which can accurately determine the mask region of an image, effectively improve the detection efficiency and avoid the interference of the mask region on the detection effect.
In a first aspect, the present application provides a method for detecting a defect in a workpiece, the method comprising:
acquiring a first workpiece image of a workpiece to be measured;
inputting the first workpiece image into a defect detection model to obtain first defect information output by the defect detection model;
wherein, inputting the first workpiece image to a defect detection model, obtaining the first defect information output by the defect detection model further includes:
performing mask processing on a mask region of the first workpiece image to obtain a second workpiece image, wherein the mask region of the first workpiece image is determined based on a target defect distribution density map;
performing defect detection on the second workpiece image to obtain the first defect information;
the defect detection model is obtained based on sample image sets and corresponding sample labels, and the target defect distribution density map is used for representing the relative density of each defect category distribution in the sample image sets.
According to the workpiece defect detection method, the relative density rule of the image defect type distribution is analyzed through the target defect distribution density map, the mask region in the image is accurately positioned, the defect detection efficiency is effectively improved, and the mask region is prevented from interfering with the detection result.
According to an embodiment of the present application, after obtaining the first defect information output by the defect detection model, the method further includes:
obtaining a first defect distribution density map based on the first defect information, wherein the first defect distribution density map is used for representing the relative density of each defect type distribution in the first workpiece image;
obtaining a defect detection result of the workpiece to be detected based on the first defect information under the condition that the defect distribution density of the first defect distribution density map is determined to be larger than a defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map;
and retraining the defect detection model under the condition that the defect detection result of the workpiece to be detected is incorrect.
According to an embodiment of the present application, after the obtaining a first defect distribution density map based on the first defect information, the method further includes:
Obtaining defective products of a production line to which the workpiece to be tested belongs under the condition that the defect distribution density of the first defect distribution density map is less than or equal to the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map;
and under the condition that the defective rate is larger than a defective rate threshold value, redetermining the defect distribution density of the first defect distribution density map.
According to one embodiment of the present application, after the obtaining the defect detection result of the workpiece to be tested, the method further includes:
checking the production process of the workpiece to be detected under the condition that the defect detection result of the workpiece to be detected is correct;
and regenerating the target defect distribution density map under the condition that the production process of the workpiece to be detected is determined to be correct.
According to an embodiment of the present application, the determining that the defect distribution density of the first defect distribution density map is greater than the defect distribution density threshold of the corresponding type of defect in the target defect distribution density map includes:
acquiring first defect coordinate information of the first defect distribution density map, and acquiring target defect coordinate information of corresponding types of defects in a target defect distribution density map;
Determining a defect coordinate difference value based on the first defect coordinate information and the target defect coordinate information;
and under the condition that the defect coordinate difference value is determined to be larger than a target threshold value, determining that the defect distribution density of the first defect distribution density map is larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map.
According to one embodiment of the application, the target defect distribution density map is generated by:
respectively inputting a plurality of sample images of the sample image set into the defect detection model for training to obtain a plurality of sample defect information output by the defect detection model, wherein the plurality of sample defect information corresponds to the plurality of sample images one by one;
the target defect distribution density map is generated based on the plurality of sample images and the plurality of sample defect information including category information and location information of various defects in the sample images.
In a second aspect, the present application provides a workpiece defect detection apparatus, the apparatus comprising:
the acquisition module is used for acquiring a first workpiece image of the workpiece to be detected;
the processing module is used for inputting the first workpiece image into a defect detection model to obtain first defect information output by the defect detection model;
Wherein, inputting the first workpiece image to a defect detection model, obtaining the first defect information output by the defect detection model further includes:
performing mask processing on a mask region of the first workpiece image to obtain a second workpiece image, wherein the mask region of the first workpiece image is determined based on a target defect distribution density map;
performing defect detection on the second workpiece image to obtain the first defect information;
the defect detection model is obtained based on sample image sets and corresponding sample labels, and the target defect distribution density map is used for representing the relative density of each defect category distribution in the sample image sets.
According to the workpiece defect detection device, the relative density rule of the image defect type distribution is analyzed through the target defect distribution density map, the mask region in the image is accurately positioned, the defect detection efficiency is effectively improved, and the mask region is prevented from interfering with the detection result.
In a third aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method for detecting a workpiece defect according to the first aspect when executing the computer program.
In a fourth aspect, the present application provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a workpiece defect detection method as described in the first aspect above.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements a method of workpiece defect detection as described in the first aspect above.
Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, wherein:
FIG. 1 is a schematic flow chart of a method for detecting defects of a workpiece according to an embodiment of the present disclosure;
FIG. 2 is a second flow chart of a method for detecting defects of a workpiece according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a workpiece defect detecting device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
Fig. 5 is a hardware schematic of an electronic device according to an embodiment of the present application.
Detailed Description
Technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of the protection of the present application.
The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged, as appropriate, such that embodiments of the present application may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type and not limited to the number of objects, e.g., the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/", generally means that the associated object is an "or" relationship.
The workpiece defect detection method, the workpiece defect detection device, the electronic equipment and the readable storage medium provided by the embodiment of the application are described in detail below through specific embodiments and application scenes thereof with reference to the accompanying drawings.
The workpiece defect detection method can be applied to a terminal, and can be specifically executed by hardware or software in the terminal.
The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet having a touch sensitive surface (e.g., a touch screen display and/or a touch pad). It should also be appreciated that in some embodiments, the terminal may not be a portable communication device, but rather a desktop computer having a touch-sensitive surface (e.g., a touch screen display and/or a touch pad).
In the following various embodiments, a terminal including a display and a touch sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse, and joystick.
The execution main body of the workpiece defect detection method provided by the embodiment of the present application may be an electronic device or a functional module or a functional entity capable of implementing the workpiece defect detection method in the electronic device, where the electronic device mentioned in the embodiment of the present application includes, but is not limited to, a mobile phone, a tablet computer, a camera, a wearable device, and the like, and the workpiece defect detection method provided by the embodiment of the present application is described below by taking the electronic device as an execution main body as an example.
As shown in fig. 1, the workpiece defect detection method includes: step 110 and step 120.
The workpiece to be detected refers to a workpiece needing defect detection.
In this step, an industrial vision system may be disposed on the industrial production line, and the industrial vision system may include a camera, a light source, an acquisition card, and other devices, and image data of the workpiece to be measured is acquired by the industrial vision system, so as to obtain a first workpiece image of the workpiece to be measured.
In the actual generation process, the workpiece to be detected can be a workpiece such as a lithium battery, a screen, a rod piece and the like processed by a production process, and the first workpiece image of the workpiece to be detected, which is acquired by an industrial vision system, can be a color image or a gray image.
And 120, inputting the first workpiece image into the defect detection model to obtain first defect information output by the defect detection model.
The defect detection model is obtained based on a sample image set and a corresponding sample label, and is used for detecting defect positions and defect categories in the image.
In this step, the first workpiece image is input to a defect detection model, and defect detection is performed by the defect detection model, and the obtained first defect information includes information representing the defect position and defect type of the first workpiece image.
It can be understood that the sample image set includes a plurality of sample images, each sample image has a defect with certain data, and each defect can be manually marked with a corresponding sample label, so that the defect detection model learns the relationship between the characteristics of the defect on the sample image and the labels.
In this embodiment, step 120, inputting the first workpiece image into the defect detection model, and obtaining the first defect information output by the defect detection model further includes:
performing mask processing on a mask region of the first workpiece image to obtain a second workpiece image, wherein the mask region of the first workpiece image is determined based on a target defect distribution density map;
and performing defect detection on the second workpiece image to obtain first defect information.
The target defect distribution density map is used for representing the relative density of each defect type distribution in the sample image set.
In actual execution, a first workpiece image is input into a defect detection model, mask processing is performed on a mask region of the first workpiece image by the defect detection model, and then defect detection is performed on the image after mask processing, so that first defect information of the workpiece to be detected is obtained.
The target defect distribution density map is used for representing the relative density of each defect type distribution in the sample image set, and can accurately reflect the relative density rule of the defect type distribution in the image.
For example, the image a includes a first region, a second region, and a third region, and the image a has four defects of a first defect, a second defect, a third defect, and a fourth defect, the first defect and the second defect are densely distributed in the first region, the relative densities of the first defect and the second defect distributed in the first region are large, the third defect and the fourth defect are densely distributed in the third region, and the relative densities of the third defect and the fourth defect distributed in the third region are large.
According to the relative density rule of defect type distribution in the image reflected by the target defect distribution density map, it can be determined that the second region in the image a is a region of defect-free distribution.
The defect detection model determines a mask region of the first workpiece image according to the target defect distribution density map, and can accurately judge a defect-free region in the image according to the relative density rule of image defect category distribution represented by the target defect distribution density map, so that the defect-free region is automatically generated into the mask region for mask processing.
In this embodiment, the defect detection model accurately determines a defect-free region (i.e., a mask region) in the image according to the target defect distribution density map, performs mask processing on the mask region to obtain a second workpiece image, performs defect detection on the second workpiece image to obtain first defect information, effectively improves defect detection efficiency, and avoids interference of the mask region with a detection result.
According to the workpiece defect detection method provided by the embodiment of the application, the relative density rule of the image defect category distribution is analyzed through the target defect distribution density map, the mask region in the image is accurately positioned, the defect detection efficiency is effectively improved, and the mask region is prevented from interfering with the detection result.
In some embodiments, after obtaining the first defect information output by the defect detection model in step 120, the workpiece defect detection method may further include:
obtaining a first defect distribution density map based on the first defect information;
obtaining a defect detection result of the workpiece to be detected based on the first defect information under the condition that the defect distribution density of the first defect distribution density map is determined to be larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map;
and under the condition that the defect detection result of the workpiece to be detected is incorrect, retraining the defect detection model.
The first defect distribution density map is used for representing the relative density of each defect type distribution in the first workpiece image.
The first defect information comprises information representing defect positions and defect categories of the first workpiece image, the defect distribution positions of all the categories in the first workpiece image can be obtained according to the first defect information, and then the relative density of the defect category distribution is determined, so that a first defect distribution density map is obtained.
In this embodiment, the comparison is performed according to the first defect distribution density map and the target defect distribution density map, and it is determined whether the relative density rule of the defect type distribution represented by the first defect distribution density map conforms to the relative density rule of the defect type distribution represented by the target defect distribution density map.
In actual implementation, the relative density of each defect distribution in the first defect distribution density map is compared with the defect distribution density threshold of the corresponding type of defect in the target defect distribution density map.
For example, the first defect distribution density map includes four 5 defects of the first defect, the second defect, the third defect and the fourth defect, and the relative density of the first defect distribution in the first defect distribution density map is compared with the first defect distribution in the target defect distribution density map
Comparing the defect distribution density threshold of a defect; and comparing the relative density of the second defect distribution in the first defect distribution density map with the defect distribution density threshold of the second defect in the target defect distribution density map.
It should be noted that the defect distribution density threshold value of each defect in the target defect distribution density map may be preset
The setting may be made according to a defect distribution density range of each defect of each sample image in the sample image set. 0 in this embodiment, it is determined that the defect distribution density of the first defect distribution density map is greater than the target defect distribution density map
The defect distribution density threshold value of the corresponding type of defects in the first defect distribution density map, namely the defect distribution density of any defect in the first defect distribution density map is larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map, shows that the relative density rule of the defect type distribution represented by the first defect distribution density map does not accord with the relative density rule of the defect type distribution represented by the target defect distribution density map.
5 in some embodiments, determining that the defect distribution density of the first defect distribution density map is greater than the target defect distribution density
The defect distribution density threshold value of the corresponding type of defects in the graph can be automatically pre-warned to prompt a user that the relative density rule of the defect type distribution of the current workpiece to be detected does not accord with the relative density rule of the defect type distribution represented by the target defect distribution density graph.
Under the condition that the defect distribution density of the first defect distribution density map is determined to be larger than the defect distribution density threshold value of the corresponding type defect 0 in the target defect distribution density map, obtaining a defect detection result of the workpiece to be detected based on the first defect information,
and carrying out manual judgment or other model judgment on the defect detection result of the workpiece to be detected so as to judge the detection condition of the defect detection model.
In this embodiment, it is determined that the defect detection result of the workpiece to be detected is incorrect, which indicates that the defect detection performed by the defect detection model is incorrect, and the defect detection model needs to be retrained, and the defect detection model is iteratively updated.
5 in some embodiments, after obtaining the first defect distribution density map based on the first defect information, the workpiece is defective
The detection method may further include:
under the condition that the defect distribution density of the first defect distribution density map is less than or equal to the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map, the defective product rate of the production line of the workpiece to be detected is obtained;
and under the condition that the defective rate is larger than the defective rate threshold value, the defect 0 distribution density of the first defect distribution density map is redetermined.
In this embodiment, it is determined that the defect distribution density of the first defect distribution density map is less than or equal to the defect distribution density threshold of the corresponding type of defect in the target defect distribution density map, that is, the defect distribution density of all types of defects in the first defect distribution density map is less than or equal to the defect distribution density threshold of the corresponding type of defect in the target defect distribution density map, which indicates that the relative density rule of the defect type distribution represented by the first defect distribution density map conforms to the relative density rule of the defect type distribution represented by the target defect distribution density map.
And under the condition that the relative density of defect type distribution represented by the first defect distribution density chart accords with a rule, detecting the defective rate of a production line to which the workpiece to be detected belongs, wherein the defective rate refers to the ratio of defective products in a certain time period to all products, and is a key index for measuring the production quality.
Determining that the defective product rate is greater than a defective product rate threshold value, indicating that the defective product rate accounts for all products in the period of producing the workpiece to be detected, wherein the probability that the workpiece to be detected is defective is high, determining the defect distribution density of the first defect distribution density map again, comparing the defect distribution density of the first defect distribution density map with the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map, and determining the relative density rule of the defect type distribution of the workpiece to be detected again.
In some embodiments, after obtaining the defect detection result of the workpiece to be detected, the workpiece defect detection method may further include:
checking the production process of the workpiece to be detected under the condition that the defect detection result of the workpiece to be detected is correct;
and regenerating a target defect distribution density map under the condition that the production process of the workpiece to be detected is determined to be correct.
In this embodiment, it is determined that the defect detection result of the workpiece to be detected is correct, which indicates that the defect detection performed by the defect detection model is correct, and the defect detection model does not need to be retrained, and the production process of the workpiece to be detected needs to be checked to determine whether the relative density of the defect type distribution of the workpiece to be detected does not conform to the corresponding rule due to the production process error of the workpiece to be detected.
When the production process of the workpiece to be measured is judged to be correct, the relative density rule of the defect type distribution represented by the target defect distribution density chart may not be comprehensive or accurate, the target defect distribution density chart is regenerated, and the relative density rule of the defect type distribution represented by the target defect distribution density chart is corrected.
In actual execution, a first workpiece image and a first defect distribution density map of a workpiece to be detected can be introduced into a sample training set, and the relative density rule of defect category distribution represented by a target defect distribution density map is supplemented.
In some embodiments, determining that the defect distribution density of the first defect distribution density map is greater than the defect distribution density threshold of the corresponding type of defect in the target defect distribution density map may include:
Acquiring first defect coordinate information of a first defect distribution density map, and acquiring target defect coordinate information of a corresponding type of defect in a target defect distribution density map;
determining a defect coordinate difference value based on the first defect coordinate information and the target defect coordinate information;
and under the condition that the difference value of the defect coordinates is larger than the target threshold value, determining that the defect distribution density of the first defect distribution density map is larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map.
Acquiring first defect coordinate information of each defect in the first defect distribution density map, acquiring corresponding types of defects in the target defect distribution density map, solving the difference value of the coordinate information of the same type of defects in the first defect distribution density map and the target defect distribution density map, and judging whether the defect distribution density of the first defect distribution density map is larger than a defect distribution density threshold value of the corresponding types of defects in the target defect distribution density map according to the defect coordinate difference value.
It can be understood that the defects are distributed at different positions of the workpiece to be detected, the first defect coordinate information and the target defect coordinate information both comprise a plurality of coordinate values, the defect coordinate difference value of the first defect coordinate information and the target defect coordinate information can be the standard deviation of the plurality of coordinate values, and if the standard deviation is larger than the target threshold value, whether the defect distribution density of the first defect distribution density map is larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map is judged, and the early warning is triggered.
In some embodiments, the target defect distribution density map is generated by:
respectively inputting a plurality of sample images of a sample image set into a defect detection model for training to obtain a plurality of sample defect information output by the defect detection model, wherein the plurality of sample defect information corresponds to the plurality of sample images one by one;
a target defect distribution density map is generated based on a plurality of sample images and a plurality of sample defect information including category information and position information of various defects in the sample images.
In this embodiment, each coordinate of each sample image is traversed, the sum of the numbers of certain defects including the coordinate point in each sample image is counted, and then the number of the sample images in the sample image set is divided by the sum of the numbers of the certain defects to obtain the distribution relative density of the defects, and all defect types in the sample image set are traversed to generate a target defect distribution density map.
A specific embodiment is described below.
As shown in fig. 2, the left flow box is a training phase of the defect detection model, and the right flow box is an application phase of the defect detection model.
The training phase comprises the following steps:
step one, carrying out defect labeling on a sample image, training a defect detection model, and testing the defect detection model.
And secondly, performing image defect detection by using a defect detection model, generating a target defect distribution density map, setting a condition for judging a defect-free region according to the target defect distribution density map, judging the defect-free region, and automatically generating a mask region.
And thirdly, judging whether the mask area meets the production line requirement, resetting the judging condition of the non-defective area when the mask area does not meet the production line requirement, regenerating a target defect distribution density map, judging the non-defective area, and automatically generating the mask area.
And when the mask area meets the production line requirement, continuing to finish the training work of the defect detection model.
The application stage comprises the following steps:
and fourthly, deploying a defect detection model and a corresponding target defect distribution density map on a production line, and setting an early warning threshold value for judging defect distribution abnormality, namely a defect distribution density threshold value of each kind of defect in the target defect distribution density map.
And fifthly, detecting a production line, namely detecting whether the defect distribution density of the workpiece to be detected is normal or not when a generation period is reached, namely determining whether the defect distribution density of a defect distribution density map of the workpiece to be detected is larger than a defect distribution density threshold value of corresponding types of defects in a target defect distribution density map or not.
And when the defect distribution density of the first defect distribution density map of the workpiece to be detected is larger than the defect distribution density threshold value of the corresponding 5 types of defects in the target defect distribution density map, namely, the defect distribution density of the workpiece to be detected is abnormal, early warning is carried out.
And step six, after early warning is executed, carrying out image analysis according to the defect detection result of the workpiece to be detected, and retraining the defect detection model when the detection result is incorrect.
And step seven, judging whether the production line generation process is normal or not when the detection result is correct.
Under the condition of normal production process, generating a new defect distribution density map for the workpiece to be tested, and judging the defect distribution 0 density threshold again.
If the production process is abnormal, after the production process is modified, curve detection of other workpieces is continuously executed.
According to the workpiece defect detection method, analysis is carried out through the distribution relative density rule of the image defect types, a mask region is automatically generated, and a result which does not accord with the distribution relative density rule of the defect types in the detection process can be obtained
Early warning is carried out, the reliability of the detection result is confirmed in time, the user is helped to detect the production process in time, and the production efficiency of the production line is effectively improved by 5 liters.
According to the workpiece defect detection method provided by the embodiment of the application, the execution main body can be a workpiece defect detection device. In the embodiment of the present application, a workpiece defect detection device performs a workpiece defect detection method by using a workpiece defect detection device as an example, and the workpiece defect detection device provided in the embodiment of the present application is described.
The embodiment of the application also provides a workpiece defect detection device.
0 as shown in fig. 3, the workpiece defect detecting apparatus includes:
an acquiring module 310, configured to acquire a first workpiece image of a workpiece to be measured;
the processing module 320 is configured to input the first workpiece image to the defect detection model, and obtain first defect information output by the defect detection model;
wherein inputting the first workpiece image to the defect detection model, obtaining the first defect information 5 output by the defect detection model further comprises:
performing mask processing on a mask region of the first workpiece image to obtain a second workpiece image, wherein the mask region of the first workpiece image is determined based on a target defect distribution density map;
performing defect detection on the second workpiece image to obtain first defect information;
the defect detection model is obtained based on sample image sets and corresponding sample labels, and a target defect 0 distribution density map is used for representing the relative density of each defect type distribution in the sample image sets.
According to the workpiece defect detection device provided by the embodiment of the application, the relative density rule of the image defect category distribution is analyzed through the target defect distribution density map, the mask region in the image is accurately positioned, the defect detection efficiency is effectively improved, and the mask region is prevented from interfering with the detection result.
In some embodiments, the processing module 320 is further configured to obtain a first defect distribution density map based on the first defect information after obtaining the first defect information output by the defect detection model, where the first defect distribution density map is used to characterize the relative density of each defect class distribution in the first workpiece image;
obtaining a defect detection result of the workpiece to be detected based on the first defect information under the condition that the defect distribution density of the first defect distribution density map is determined to be larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map;
and under the condition that the defect detection result of the workpiece to be detected is incorrect, retraining the defect detection model.
In some embodiments, the processing module 320 is further configured to, after obtaining the first defect distribution density map based on the first defect information, in a case where it is determined that the defect distribution density of the first defect distribution density map is less than or equal to the defect distribution density threshold of the corresponding type of defect in the target defect distribution density map,
Obtaining defective product rate of a production line to which a workpiece to be tested belongs;
and under the condition that the defective rate is larger than the defective rate threshold value, the defect distribution density of the first defect distribution density map is redetermined.
In some embodiments, the processing module 320 is further configured to, after obtaining the defect detection result of the workpiece to be tested, check a production process of the workpiece to be tested if it is determined that the defect detection result of the workpiece to be tested is correct;
and regenerating a target defect distribution density map under the condition that the production process of the workpiece to be detected is determined to be correct.
In some embodiments, the processing module 320 is configured to obtain first defect coordinate information of the first defect distribution density map, and obtain target defect coordinate information of a corresponding type of defect in the target defect distribution density map;
determining a defect coordinate difference value based on the first defect coordinate information and the target defect coordinate information;
and under the condition that the difference value of the defect coordinates is larger than the target threshold value, determining that the defect distribution density of the first defect distribution density map is larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map.
In some embodiments, the processing module 320 is configured to generate the target defect distribution density map by:
Respectively inputting a plurality of sample images of a sample image set into a defect detection model for training to obtain a plurality of sample defect information output by the defect detection model, wherein the plurality of sample defect information corresponds to the plurality of sample images one by one;
a target defect distribution density map is generated based on a plurality of sample images and a plurality of sample defect information including category information and position information of various defects in the sample images.
The workpiece defect detection device in the embodiment of the application can be an electronic device, or can be a component in the electronic device, such as an integrated circuit or a chip. The electronic device may be a terminal, or may be other devices than a terminal. By way of example, the electronic device may be a mobile phone, tablet computer, notebook computer, palm computer, vehicle-mounted electronic device, mobile internet appliance (Mobile Internet Device, MID), augmented reality (augmented reality, AR)/Virtual Reality (VR) device, robot, wearable device, ultra-mobile personal computer, UMPC, netbook or personal digital assistant (personal digital assistant, PDA), etc., but may also be a server, network attached storage (Network Attached Storage, NAS), personal computer (personal computer, PC), television (TV), teller machine or self-service machine, etc., and the embodiments of the present application are not limited in particular.
The workpiece defect detection device in the embodiment of the application may be a device with an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which is not specifically limited in the embodiments of the present application.
The workpiece defect detection device provided in the embodiment of the present application can implement each process implemented by the method embodiments of fig. 1 to 2, and in order to avoid repetition, a description is omitted here.
In some embodiments, as shown in fig. 4, the embodiment of the present application further provides an electronic device 400, including a processor 401, a memory 402, and a computer program stored in the memory 402 and capable of running on the processor 401, where the program when executed by the processor 401 implements the processes of the workpiece defect detection method embodiment described above, and the same technical effects can be achieved, and for avoiding repetition, a description is omitted herein.
The electronic device in the embodiment of the application includes the mobile electronic device and the non-mobile electronic device.
Fig. 5 is a schematic hardware structure of an electronic device implementing an embodiment of the present application.
The electronic device 500 includes, but is not limited to: radio frequency unit 501, network module 502, audio output unit 503, input unit 504, sensor 505, display unit 506, user input unit 507, interface unit 508, memory 509, and processor 510.
Those skilled in the art will appreciate that the electronic device 500 may further include a power source (e.g., a battery) for powering the various components, and that the power source may be logically coupled to the processor 510 via a power management system to perform functions such as managing charging, discharging, and power consumption via the power management system. The electronic device structure shown in fig. 5 does not constitute a limitation of the electronic device, and the electronic device may include more or less components than shown, or may combine certain components, or may be arranged in different components, which are not described in detail herein.
The input unit 504, in this embodiment, a camera, is configured to obtain a first workpiece image of a workpiece to be measured;
a processor 510, configured to input a first workpiece image to the defect detection model, and obtain first defect information output by the defect detection model;
wherein inputting the first workpiece image to the defect detection model, obtaining the first defect information output by the defect detection model further comprises:
performing mask processing on a mask region of the first workpiece image to obtain a second workpiece image, wherein the mask region of the first workpiece image is determined based on a target defect distribution density map;
performing defect detection on the second workpiece image to obtain first defect information;
The defect detection model is obtained based on sample image sets and corresponding sample labels, and the target defect distribution density map is used for representing the relative density of each defect type distribution in the sample image sets.
According to the electronic equipment provided by the embodiment of the application, the relative density rule of the image defect type distribution is analyzed through the target defect distribution density map, the mask region in the image is accurately positioned, the defect detection efficiency is effectively improved, and the mask region is prevented from interfering with the detection result.
In some embodiments, the processor 510 is further configured to obtain a first defect distribution density map based on the first defect information, where the first defect distribution density map is used to characterize a relative density of each defect class distribution in the first workpiece image;
obtaining a defect detection result of the workpiece to be detected based on the first defect information under the condition that the defect distribution density of the first defect distribution density map is determined to be larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map;
and under the condition that the defect detection result of the workpiece to be detected is incorrect, retraining the defect detection model.
In some embodiments, the processor 510 is further configured to, in the event that it is determined that the defect distribution density of the first defect distribution density map is less than or equal to the defect distribution density threshold for the corresponding type of defect in the target defect distribution density map,
Obtaining defective product rate of a production line to which a workpiece to be tested belongs;
and under the condition that the defective rate is larger than the defective rate threshold value, the defect distribution density of the first defect distribution density map is redetermined.
In some embodiments, the processor 510 is further configured to check a production process of the workpiece to be tested if it is determined that the defect detection result of the workpiece to be tested is correct;
and regenerating a target defect distribution density map under the condition that the production process of the workpiece to be detected is determined to be correct.
In some embodiments, the processor 510 is further configured to obtain first defect coordinate information of the first defect distribution density map, and obtain target defect coordinate information of a corresponding type of defect in the target defect distribution density map;
determining a defect coordinate difference value based on the first defect coordinate information and the target defect coordinate information;
and under the condition that the difference value of the defect coordinates is larger than the target threshold value, determining that the defect distribution density of the first defect distribution density map is larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map.
In some embodiments, processor 510 is further configured to generate a target defect distribution density map by:
respectively inputting a plurality of sample images of a sample image set into a defect detection model for training to obtain a plurality of sample defect information output by the defect detection model, wherein the plurality of sample defect information corresponds to the plurality of sample images one by one;
A target defect distribution density map is generated based on a plurality of sample images and a plurality of sample defect information including category information and position information of various defects in the sample images.
It should be appreciated that in embodiments of the present application, the input unit 504 may include a graphics processor (Graphics Processing Unit, GPU) 5041 and a microphone 5042, with the graphics processor 5041 processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The display unit 506 may include a display panel 5061, and the display panel 5061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 507 includes at least one of a touch panel 5071 and other input devices 5072. Touch panel 5071, also referred to as a touch screen. Touch panel 5071 may include two parts, a touch detection device and a touch controller. Other input devices 5072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein.
The memory 509 may be used to store software programs as well as various data. The memory 509 may mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area may store an operating system, application programs or instructions (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like. Further, the memory 509 may include volatile memory or nonvolatile memory, or the memory 509 may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM), static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (ddr SDRAM), enhanced SDRAM (Enhanced SDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRRAM). Memory 509 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.
The embodiment of the present application further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements each process of the above workpiece defect detection method embodiment, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.
Wherein the processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes computer readable storage medium such as computer readable memory ROM, random access memory RAM, magnetic or optical disk, etc.
The embodiment of the application also provides a computer program product, which comprises a computer program, and the computer program realizes the workpiece defect detection method when being executed by a processor.
Wherein the processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes computer readable storage medium such as computer readable memory ROM, random access memory RAM, magnetic or optical disk, etc.
The embodiment of the application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled with the processor, and the processor is used for running a program or an instruction, so that each process of the workpiece defect detection method embodiment can be implemented, the same technical effect can be achieved, and in order to avoid repetition, the description is omitted here.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, chip systems, or system-on-chip chips, etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solutions of the present application may be embodied essentially or in a part contributing to the prior art in the form of a computer software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are also within the protection of the present application.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A method of workpiece defect detection, comprising:
acquiring a first workpiece image of a workpiece to be measured;
inputting the first workpiece image into a defect detection model to obtain first defect information output by the defect detection model;
Wherein, inputting the first workpiece image to a defect detection model, obtaining the first defect information output by the defect detection model further includes:
performing mask processing on a mask region of the first workpiece image to obtain a second workpiece image, wherein the mask region of the first workpiece image is determined based on a target defect distribution density map;
performing defect detection on the second workpiece image to obtain the first defect information;
the defect detection model is obtained based on sample image sets and corresponding sample labels, and the target defect distribution density map is used for representing the relative density of each defect category distribution in the sample image sets.
2. The method according to claim 1, wherein after obtaining the first defect information output by the defect detection model, the method further comprises:
obtaining a first defect distribution density map based on the first defect information, wherein the first defect distribution density map is used for representing the relative density of each defect type distribution in the first workpiece image;
obtaining a defect detection result of the workpiece to be detected based on the first defect information under the condition that the defect distribution density of the first defect distribution density map is determined to be larger than a defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map;
And retraining the defect detection model under the condition that the defect detection result of the workpiece to be detected is incorrect.
3. The method according to claim 2, wherein after the obtaining a first defect distribution density map based on the first defect information, the method further comprises:
obtaining defective products of a production line to which the workpiece to be tested belongs under the condition that the defect distribution density of the first defect distribution density map is less than or equal to the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map;
and under the condition that the defective rate is larger than a defective rate threshold value, redetermining the defect distribution density of the first defect distribution density map.
4. The method according to claim 2, characterized in that after the obtaining of the defect detection result of the workpiece to be detected, the method further comprises:
checking the production process of the workpiece to be detected under the condition that the defect detection result of the workpiece to be detected is correct;
and regenerating the target defect distribution density map under the condition that the production process of the workpiece to be detected is determined to be correct.
5. The method according to claim 2, wherein determining that the defect distribution density of the first defect distribution density map is greater than the defect distribution density threshold of the corresponding type of defect in the target defect distribution density map comprises:
acquiring first defect coordinate information of the first defect distribution density map, and acquiring target defect coordinate information of corresponding types of defects in a target defect distribution density map;
determining a defect coordinate difference value based on the first defect coordinate information and the target defect coordinate information;
and under the condition that the defect coordinate difference value is determined to be larger than a target threshold value, determining that the defect distribution density of the first defect distribution density map is larger than the defect distribution density threshold value of the corresponding type of defects in the target defect distribution density map.
6. The method of any one of claims 1-5, wherein the target defect distribution density map is generated by:
respectively inputting a plurality of sample images of the sample image set into the defect detection model for training to obtain a plurality of sample defect information output by the defect detection model, wherein the plurality of sample defect information corresponds to the plurality of sample images one by one;
The target defect distribution density map is generated based on the plurality of sample images and the plurality of sample defect information including category information and location information of various defects in the sample images.
7. A workpiece defect detection apparatus, comprising:
the acquisition module is used for acquiring a first workpiece image of the workpiece to be detected;
the processing module is used for inputting the first workpiece image into a defect detection model to obtain first defect information output by the defect detection model;
wherein, inputting the first workpiece image to a defect detection model, obtaining the first defect information output by the defect detection model further includes:
performing mask processing on a mask region of the first workpiece image to obtain a second workpiece image, wherein the mask region of the first workpiece image is determined based on a target defect distribution density map;
performing defect detection on the second workpiece image to obtain the first defect information;
the defect detection model is obtained based on sample image sets and corresponding sample labels, and the target defect distribution density map is used for representing the relative density of each defect category distribution in the sample image sets.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of workpiece defect detection as claimed in any of claims 1-6 when the program is executed by the processor.
9. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the workpiece defect detection method according to any of claims 1-6.
10. A computer program product comprising a computer program which, when executed by a processor, implements the workpiece defect detection method as claimed in any of claims 1-6.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117173174A (en) * | 2023-11-02 | 2023-12-05 | 合肥喆塔科技有限公司 | Liquid crystal panel defect aggregation mode identification method, device and storage medium |
| CN117635528A (en) * | 2023-09-20 | 2024-03-01 | 上海朋熙半导体有限公司 | Method, device and equipment for accurately detecting wafer defects and readable medium |
| CN118610115A (en) * | 2024-08-08 | 2024-09-06 | 西安奕斯伟材料科技股份有限公司 | Defect detection method, device, equipment and medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117635528A (en) * | 2023-09-20 | 2024-03-01 | 上海朋熙半导体有限公司 | Method, device and equipment for accurately detecting wafer defects and readable medium |
| CN117173174A (en) * | 2023-11-02 | 2023-12-05 | 合肥喆塔科技有限公司 | Liquid crystal panel defect aggregation mode identification method, device and storage medium |
| CN118610115A (en) * | 2024-08-08 | 2024-09-06 | 西安奕斯伟材料科技股份有限公司 | Defect detection method, device, equipment and medium |
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