CN111912846A - Machine vision-based surface defect and edge burr detection method - Google Patents
Machine vision-based surface defect and edge burr detection method Download PDFInfo
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Abstract
The invention discloses a surface defect and edge burr detection method based on machine vision, which comprises the following steps: acquiring a keycap image to be detected; detecting a keycap surface area in a keycap image to be detected; if the surface area of the keycap is qualified, detecting a two-dimensional outline area of the keycap in the keycap image to be detected; and if the burrs in the two-dimensional outline area of the keycap exceed the allowable value, judging that the keycap to be detected corresponding to the keycap image is a defective product. Determining an image area to be detected by acquiring an image containing a keycap to be detected as a keycap image to be detected, and dividing a keycap surface area and a keycap two-dimensional outline area; the corresponding detection method is adopted for different detection areas, so that the detection accuracy of the defects is guaranteed, edge burrs which do not exceed the standard requirement are allowed, the misjudgment rate is greatly reduced, and the problem of high misjudgment rate of the traditional visual identification is effectively solved.
Description
Technical Field
The invention relates to the technical field of visual inspection of flexible automatic production assembly equipment, in particular to a surface defect and edge burr detection method based on machine vision.
Background
In terms of the current usage habit of personal computers, keyboards are one of the indispensable input devices for inputting characters, symbols or numbers. Moreover, for example, consumer electronic products in contact with daily life or large processing equipment used in the industry, a key structure is required to be provided as an input device for operating the electronic products and the processing equipment.
Before assembling the keyboard key cap, need carry out the defective products to the key cap and detect, there is the deformation to the outward appearance of key cap generally, and there is not mar, indentation in the surface, and the edge has the broken edge unfilled corner etc. and requires comparatively highly, but allows the edge to have micro burr. By adopting the traditional visual identification, the edge burrs can be mistakenly considered as the defects, and qualified products are judged as defective products, so that the problem of high misjudgment rate is caused.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a method for detecting surface defects and edge burrs based on machine vision, so as to solve the problem in the prior art that when a keycap is detected by using traditional vision recognition, edge burrs are mistaken for defects, and a qualified product is determined as a defective product, so that the false determination rate is high.
The embodiment of the invention provides a surface defect and edge burr detection method based on machine vision, which comprises the following steps:
acquiring a keycap image to be detected;
detecting a keycap surface area in a keycap image to be detected;
if the surface area of the keycap is qualified, detecting a two-dimensional outline area of the keycap in the keycap image to be detected;
and if the burrs in the two-dimensional outline area of the keycap exceed the allowable value, judging that the keycap to be detected corresponding to the keycap image is a defective product.
Optionally, after acquiring the image of the keycap to be detected, the method further includes:
acquiring a background area threshold characteristic of a keycap image to be detected;
and dividing the keycap image to be detected into a surface area of the keycap to be detected and a two-dimensional outline area between the keycap to be detected and the background.
Optionally, detecting a key cap surface area in the key cap image to be detected comprises:
extracting the detection result characteristics of the keycap surface area through digital image processing and pattern recognition technology;
comparing the detection result characteristics with the characteristics in the sample defect library;
and if the comparison result of the surface area of the keycap is a defective product, ending the detection.
Optionally, after acquiring the image of the keycap to be detected, the method further includes:
calculating a binarization threshold value of a keycap image to be detected through a data histogram, filtering interference points in an identification region, and eliminating signal noise through smoothing; wherein the identification region comprises a key cap surface area and a key cap two-dimensional outline area.
Optionally, before acquiring an image of the keycap to be detected, the method further includes:
and pre-storing the specification data of the keycaps to be detected and the image detection range corresponding to the specification data of the keycaps to be detected in the database.
Optionally, the storing in the sample defect library is performed by using a binary file, and before comparing the detection result features with the features in the sample defect library, the method further includes:
and carrying out cluster analysis, image extraction and normalization processing on the binary file.
Optionally, detecting a two-dimensional outline area of the keycap in the keycap image to be detected includes:
searching N points forward from the last one in the data of the background area, and calculating the maximum value MAX _ Roller of the searched N points in the Z-axis direction;
starting from the last one of the data of the background area, the search is continued until the first condition is satisfied:
wherein Z (k-1) and Z (k) respectively represent Z-axis coordinate values of a k-1 point and a k point, and Height represents the noise level between pole piece data points; n and k are both natural numbers more than or equal to 1;
when a first condition is met, the kth point is an edge point;
according to the pole piece edge point of the previous frame data point, adopting a weighted average algorithm to predict the edge point of the next frame data, and the calculation method is
Wherein, the pole piece edge estimated values of the ith frame and the (i +1) th frame are respectively, X (i +1) is the pole piece edge measured value of the (i +1) th frame, and beta is a prediction coefficient; i is a natural number of 1 or more.
Optionally, detecting the two-dimensional outline area of the keycap in the keycap image to be detected further includes:
determining whether the size of the burr exceeds an allowable value or not according to the edge point information;
if the value exceeds the allowable value, it is determined as a defective product.
Optionally, determining whether the size of the spur exceeds an allowable value according to the edge point information includes:
calculating the size of all horizontal burrs by the following formula
H is to beHComparing with a glitch threshold;
when h is generatedHAnd (4) judging that the keycap to be detected is a defective product if the keycap to be detected is larger than the burr threshold value, namely the size of the horizontal burr exceeds an allowable value.
The embodiment of the invention provides a surface defect and edge burr detection method based on machine vision, which comprises the steps of dividing a detection area of a target image into a surface detection area and a two-dimensional outline area, extracting detection result characteristics of the surface area to be detected through digital image processing and pattern recognition technology in the surface detection area, comparing the detection result characteristics with standard defect characteristics, and judging whether the surface of a keycap to be detected has defects. And determining edge points between the keycap area and the background area according to the obtained two-dimensional profile data of the edge of the keycap to be detected, rapidly identifying whether the size of the burr is within an allowable range through the information of the edge points, and detecting whether the edge burr exceeds a standard. The method comprises the steps of determining an image area to be detected by acquiring an image containing a keycap to be detected as a keycap image to be detected, and dividing a keycap surface area and a keycap two-dimensional outline area; the corresponding detection method is adopted for different detection areas, so that the detection accuracy of the defects is guaranteed, edge burrs which do not exceed the standard requirement are allowed, the misjudgment rate is greatly reduced, and the problem of high misjudgment rate of the traditional visual identification is effectively solved.
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The features and advantages of the present invention will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the invention in any way, and in which:
FIG. 1 is a flow chart illustrating a method for machine vision based surface defect and edge burr detection in an embodiment of the present invention;
fig. 2 is a block diagram of a machine vision-based surface defect and edge burr detection terminal according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a surface defect and edge burr detection method based on machine vision, as shown in fig. 1, comprising the following steps:
and step S10, acquiring the keycap image to be detected.
In this embodiment, the keycap image to be detected includes the keycap image to be detected and the background area.
And step S20, detecting the key cap surface area in the key cap image to be detected.
In this embodiment, the key cap surface area is detected first, and if the detection result is not good, the key cap is a defective product, and the detection is terminated.
If the key cap surface area is qualified, step S30 is executed to detect the two-dimensional outline area of the key cap in the key cap image to be detected.
In this embodiment, on the premise that the detection result of the surface area of the keycap is qualified, the two-dimensional outline area of the keycap is detected.
If the burr in the two-dimensional outline area of the keycap exceeds the allowable value, step S40 is executed to determine that the keycap to be detected corresponding to the detected keycap image is a defective product. Otherwise, step S50 is executed to determine that the keycap to be detected corresponding to the detected keycap image is a qualified product.
In the embodiment, the keycaps with the burr sizes within the allowable range are judged to be qualified products; otherwise, it is a defective product.
In a specific embodiment, step 20 and step S30 use two detection methods for detection: and for the surface detection area, extracting the detection result characteristics of the surface area to be detected through digital image processing and pattern recognition technology, comparing the detection result characteristics with the standard defect characteristics, and judging whether the surface of the keycap to be detected has defects. And determining edge points between the keycap area and the background area according to the obtained two-dimensional profile data of the edge of the keycap to be detected, rapidly identifying whether the size of the burr is within an allowable range through the information of the edge points, and detecting whether the edge burr exceeds a standard.
The method comprises the steps of determining an image area to be detected by acquiring an image containing a keycap to be detected as a keycap image to be detected, and dividing a keycap surface area and a keycap two-dimensional outline area; the corresponding detection method is adopted for different detection areas, so that the detection accuracy of the defects is guaranteed, edge burrs which do not exceed the standard requirement are allowed, the misjudgment rate is greatly reduced, and the problem of high misjudgment rate of the traditional visual identification is effectively solved.
As an optional implementation manner, after step S10, the method further includes:
and step S11, acquiring the threshold characteristic of the background area of the keycap image to be detected.
And step S12, dividing the keycap image to be detected into a surface area of the keycap to be detected and a two-dimensional outline area between the keycap to be detected and the background.
In this embodiment, under the irradiation of the light source, the occupied area of the surface of the keycap is brighter, the brightness is more uniform, and the background area is darker. The boundary and the two-dimensional contour region are determined according to the threshold feature.
As an alternative embodiment, step S20 includes:
and step S21, extracting the detection result characteristics of the key cap surface area through digital image processing and pattern recognition technology.
And step S22, comparing the detection result characteristics with the characteristics in the sample defect library.
And if the comparison result of the surface area of the keycap is a defective product, ending the detection.
In this embodiment, image recognition is actually a classification process, and in order to identify the class to which an image belongs, it is necessary to distinguish it from other images of different classes. Therefore, the detection result characteristics are compared with the characteristics in the defect library, and as long as one of the detection result characteristics is consistent with the characteristics in the sample defect library, the surface area of the keycap is judged to have the defect, and the keycap is a defective product.
As an optional implementation manner, after step S10, the method further includes: calculating a binarization threshold value of a keycap image to be detected through a data histogram, filtering interference points in an identification region, and eliminating signal noise through smoothing; wherein the identification region comprises a key cap surface area and a key cap two-dimensional outline area.
In the embodiment, the keycap image to be detected is preprocessed in the above manner, so that interference points and signal noise are removed.
As an optional implementation manner, before step S10, the method further includes: and pre-storing the specification data of the keycaps to be detected and the image detection range corresponding to the specification data of the keycaps to be detected in the database.
In this embodiment, because there is the key cap of different specification sizes on the market, consequently prestore the image detection scope that different specification sizes key cap correspond in the database: and using the recognition camera as a center, and appointing a range to be measured according to the size of the keycap to be measured. By appointing the range to be detected, the keycap image to be detected is cut in advance, so that the detection calculation process is simplified.
As an optional implementation manner, the sample defect library is stored in a binary file, and before comparing the detection result features with the features in the sample defect library, the method further includes: and carrying out cluster analysis, image extraction and normalization processing on the binary file.
In this embodiment, in the development of database application projects, some binary image data are often used, and the path link method and the memory flow method are mainly used for storing and reading the display image data. The path linking method is to store the image files in a fixed path, and only store the path and the name of the image files in the database, and the method has small database capacity, high access speed and poor safety; the memory flow method is to store binary data in a database directly, is very convenient for data sharing, has relatively high safety, and is commonly used when the image capacity is not very large. Therefore, before comparing the detection result features with the features in the sample defect library, the binary file needs to be subjected to cluster analysis, image extraction, and then normalization processing.
As an alternative embodiment, step S30 includes:
searching N points forward from the last one in the data of the background area, and calculating the maximum value MAX _ Roller of the searched N points in the Z-axis direction;
starting from the last one of the data of the background area, the search is continued until the first condition is satisfied:
wherein Z (k-1) and Z (k) respectively represent Z-axis coordinate values of a k-1 point and a k point, and Height represents the noise level between pole piece data points; n and k are both natural numbers more than or equal to 1;
when a first condition is met, the kth point is an edge point;
according to the pole piece edge point of the previous frame data point, adopting a weighted average algorithm to predict the edge point of the next frame data, and the calculation method is
Wherein, the pole piece edge estimated values of the ith frame and the (i +1) th frame are respectively, X (i +1) is the pole piece edge measured value of the (i +1) th frame, and beta is a prediction coefficient; i is a natural number of 1 or more.
As an optional implementation manner, step S30 further includes:
determining whether the size of the burr exceeds an allowable value or not according to the edge point information;
if the value exceeds the allowable value, it is determined as a defective product.
As an alternative embodiment, determining whether the size of the spur exceeds the allowable value according to the edge point information includes:
calculating the size of all horizontal burrs by the following formula
H is to beHComparing with a glitch threshold;
when h is generatedHAnd (4) judging that the keycap to be detected is a defective product if the keycap to be detected is larger than the burr threshold value, namely the size of the horizontal burr exceeds an allowable value.
The embodiment of the present invention also provides a machine vision-based surface defect and edge burr detection terminal, as shown in fig. 2, the detection terminal may include a processor 21 and a memory 22, where the processor 21 and the memory 22 may be connected by a bus or by other means, and fig. 2 illustrates the connection by the bus.
The processor 21 may be a Central Processing Unit (CPU). The Processor 21 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or any combination thereof.
The memory 22, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 21 executes the non-transitory software programs, instructions and modules stored in the memory 22 to execute various functional applications and data processing of the processor, namely, to implement the machine vision-based surface defect and edge burr detection method in the above method embodiment.
The memory 22 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 21, and the like. Further, the memory 22 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 22 may optionally include memory located remotely from the processor 21, and these remote memories may be connected to the processor 21 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 22 and, when executed by the processor 21, perform a machine vision based surface defect and edge burr detection method as in the embodiment shown in fig. 1.
The specific details of the detection terminal may be understood by referring to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (9)
1. A surface defect and edge burr detection method based on machine vision is characterized by comprising the following steps:
acquiring a keycap image to be detected;
detecting the surface area of the keycap in the image of the keycap to be detected;
if the key cap surface area is qualified, detecting a key cap two-dimensional outline area in the key cap image to be detected;
and if the burrs in the two-dimensional outline area of the keycap exceed the allowable value, judging that the keycap to be detected corresponding to the detected keycap image is a defective product.
2. The machine vision-based surface defect and edge burr detection method of claim 1, further comprising, after acquiring an image of a keycap to be inspected:
acquiring a background area threshold characteristic of the keycap image to be detected;
and dividing the keycap image to be detected into a surface area of the keycap to be detected and a two-dimensional outline area between the keycap to be detected and the background.
3. The machine-vision-based surface defect and edge burr detection method of claim 1, wherein detecting a key cap surface area in the key cap image to be detected comprises:
extracting the detection result characteristics of the keycap surface area through digital image processing and pattern recognition technology;
comparing the detection result characteristics with characteristics in a sample defect library;
and if the comparison result of the keycap surface area is a defective product, ending the detection.
4. The machine vision-based surface defect and edge burr detection method of claim 1, further comprising, after acquiring an image of a keycap to be inspected:
calculating a binarization threshold value of the keycap image to be detected through a data histogram, filtering interference points in an identification region, and eliminating signal noise through smoothing; wherein the identification region comprises the key cap surface area and the key cap two-dimensional outline area.
5. The machine vision-based surface defect and edge burr detection method of claim 1, further comprising, prior to acquiring an image of a keycap to be inspected:
and pre-storing the specification data of the keycaps to be detected and the image detection range corresponding to the specification data of the keycaps to be detected in a database.
6. The machine-vision-based surface defect and edge burr detection method of claim 3, wherein the sample defect library is stored in a binary file, and further comprising, before comparing the detection result features with features in the sample defect library:
and carrying out cluster analysis, image extraction and normalization processing on the binary file.
7. The machine vision-based surface defect and edge burr detection method of claim 1, wherein detecting a two-dimensional outline area of a keycap in the keycap image to be detected comprises:
searching N points forward from the last one in the data of the background area, and calculating the maximum value MAX _ Roller of the searched N points in the Z-axis direction;
searching forward, starting from the last of the data of the background area, until a first condition is satisfied:
wherein Z (k-1) and Z (k) respectively represent Z-axis coordinate values of a k-1 point and a k point, and Height represents the noise level between pole piece data points; n and k are both natural numbers more than or equal to 1;
when the first condition is satisfied, the kth point is an edge point;
according to the pole piece edge point of the previous frame data point, adopting a weighted average algorithm to predict the edge point of the next frame data, and the calculation method is
Wherein, the pole piece edge estimated values of the ith frame and the (i +1) th frame are respectively, X (i +1) is the pole piece edge measured value of the (i +1) th frame, and beta is a prediction coefficient; i is a natural number of 1 or more.
8. The machine-vision-based surface defect and edge burr detection method of claim 7, wherein detecting a two-dimensional outline area of a keycap in the keycap image to be detected further comprises:
determining whether the size of the burr exceeds an allowable value or not according to the edge point information;
if the value exceeds the allowable value, it is determined as a defective product.
9. The machine-vision-based surface defect and edge burr detection method of claim 8, wherein determining whether a size of a burr exceeds an allowable value based on the edge point information comprises:
calculating the size of all horizontal burrs by the following formula
H is to beHComparing with a glitch threshold;
when h is generatedHAnd if the size of the horizontal burr exceeds the allowable value, judging that the keycap to be detected is a defective product.
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| CN117031052B (en) * | 2023-10-09 | 2024-01-09 | 广州市普理司科技有限公司 | Single printed matter front and back vision detection control system |
| CN117392133A (en) * | 2023-12-12 | 2024-01-12 | 江苏中科云控智能工业装备有限公司 | Die casting burr detection system and method based on machine vision |
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