CN112903703A - Ceramic surface defect detection method and system based on image processing - Google Patents
Ceramic surface defect detection method and system based on image processing Download PDFInfo
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- CN112903703A CN112903703A CN202110109329.1A CN202110109329A CN112903703A CN 112903703 A CN112903703 A CN 112903703A CN 202110109329 A CN202110109329 A CN 202110109329A CN 112903703 A CN112903703 A CN 112903703A
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Abstract
The invention relates to the technical field of ceramic surface defect detection, in particular to a ceramic surface defect detection method and system based on image processing, wherein the method comprises the following steps: acquiring an RGB image and an NIR image of the ceramic, extracting a first image of a region where the ceramic is located from the RGB image, and extracting a second image of the region where the ceramic is located from the NIR image; converting the first image into a YUV format to obtain a YUV image, and obtaining a mixed image according to a Y component image and a second image in the YUV image; the mixed image is converted into a binary image, the defect area in the binary image is segmented, and the surface defect type of the ceramic is determined according to the defect area.
Description
Technical Field
The invention relates to the technical field of ceramic surface defect detection, in particular to a ceramic surface defect detection method and system based on image processing.
Background
In the process of ceramic production, glaze defects are a big trouble which causes unqualified ceramic quality, particularly ceramic surface defects such as concave glaze, glaze bubbles, brown holes, pinholes and the like are common quality problems, and the detection of the ceramic surface quality is an important way and measure for ensuring the appearance quality of the ceramic wall and floor tiles and the grade of the whole product.
In the prior art, only manual visual quality inspection is needed, so that error detection is easy to occur, time and labor are wasted, and the development requirements of current intelligent manufacturing cannot be met.
Disclosure of Invention
The invention provides a method and a system for detecting ceramic surface defects based on image processing, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for detecting ceramic surface defects based on image processing, the method comprising the steps of:
acquiring an RGB image and an NIR image of the ceramic, extracting a first image of a region where the ceramic is located from the RGB image, and extracting a second image of the region where the ceramic is located from the NIR image;
converting the first image into a YUV format to obtain a YUV image, and obtaining a mixed image according to a Y component image and a second image in the YUV image;
and converting the mixed image into a binary image, segmenting a defect area in the binary image, and determining the surface defect type of the ceramic according to the defect area.
Further, a conversion formula for converting the first image into YUV format is: Y-0.257R + 0.504G + 0.098B +16, U-0.148R-0.291G + 0.439B +128, V-0.439R-0.368G-0.071B + 128;
wherein: y, U, V for YUV images, R, G, B for RGB images.
Further, the calculation formula of the mixed image is as follows: the blended image is 255 × (Y component image in the second image/YUV image).
Further, the converting the mixed image into a binarized image, segmenting a defect region in the binarized image, and determining the surface defect type of the ceramic according to the defect region includes:
converting the mixed image into a binary image, and reducing the binary image according to a set proportional value in an equal proportion to obtain a reference model; the value range of the set proportion value is [0.001,0.01 ];
starting from one end of the binarized image, segmenting the binarized image by a reference module to obtain a reference image;
calculating the brightness average value of each reference image, and determining a brightness interval according to the brightness average value;
taking an image area deviating from the brightness section as a defect area in the reference image;
determining the size of the defect area, and if the defect area is smaller than a set threshold, determining the defect area as a point defect; and if the defect area is larger than a set threshold value, determining the surface defect type of the ceramic through a target detection model, wherein the defect type comprises concave glaze, glaze bubbles, brown holes and pinholes.
Further, the determining the surface defect type of the ceramic through the target detection model comprises:
building a neural network model, and training the network model by adopting a training image to obtain a target detection model, wherein the training image is calibrated with at least one defect type, and the defect type comprises concave glaze, glaze bubbles, brown holes and pinholes;
and identifying the input target detection model larger than the set threshold value to obtain the surface defect type of the ceramic.
Further, the neural network model is any one of a FasterR-CNN model, a YOLOv3 model or an SSD model.
An image processing based ceramic surface defect detection system, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement any of the image processing-based ceramic surface defect detection methods described above.
A computer readable storage medium having stored thereon an image processing-based ceramic surface defect detection program, which when executed by a processor implements the steps of any of the image processing-based ceramic surface defect detection methods described above.
The invention has the beneficial effects that: the invention discloses a ceramic surface defect detection method and system based on image processing, the invention can present clear images even under weak light by obtaining RGB images and NIR images of ceramic, can reduce the influence of illumination on image information, then convert the first image into YUV format to obtain YUV image, and obtain mixed image according to Y component image and second image in YUV image, the mixed image can accurately and comprehensively reflect the condition of ceramic surface, and is convenient for accurately detecting the surface defect of ceramic; the mixed image is converted into a binary image, a defect area in the binary image is segmented, the surface defect type of the ceramic is determined according to the defect area, and automatic detection can be carried out based on an image processing algorithm. The method is used for detecting the ceramic surface defects based on image processing, and has the advantages of high efficiency, reliability and low cost.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method for detecting defects on a ceramic surface based on image processing according to an embodiment of the present invention.
Detailed Description
The conception, specific structure and technical effects of the present application will be described clearly and completely with reference to the following embodiments and the accompanying drawings, so that the purpose, scheme and effects of the present application can be fully understood. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, fig. 1 illustrates a method for detecting defects on a ceramic surface based on image processing according to an embodiment of the present application, where the method includes the following steps:
s100, acquiring an RGB (red, green and blue) image and an NIR (near infrared) image of the ceramic, extracting a first image of a region where the ceramic is located from the RGB image, and extracting a second image of the region where the ceramic is located from the NIR image;
specifically, a first image of a region where the ceramics are located is extracted from the RGB image by adopting an image segmentation algorithm, and a second image of the region where the ceramics are located is extracted from the NIR image by adopting an image segmentation algorithm; the image segmentation algorithm comprises any one of a watershed algorithm and an edge detection algorithm.
Step S200, converting the first image into a YUV format to obtain a YUV image, and obtaining a mixed image according to a Y component image and a second image in the YUV image;
and step S300, converting the mixed image into a binary image, segmenting a defect region in the binary image, and determining the surface defect type of the ceramic according to the defect region.
In one embodiment, the conversion formula for converting the first image into YUV format is: Y-0.257R + 0.504G + 0.098B +16, U-0.148R-0.291G + 0.439B +128, V-0.439R-0.368G-0.071B + 128; wherein Y, U, V are three components of YUV image, and R, G, B are three components of RGB image.
In one embodiment, the calculation formula of the blended image is: the blended image is 255 × (Y component image in the second image/YUV image).
In one embodiment, the step S300 includes:
converting the mixed image into a binary image, and reducing the binary image according to a set proportional value in an equal proportion to obtain a reference model; the value range of the set proportion value is [0.001,0.01 ];
starting from one end of the binarized image, segmenting the binarized image by a reference module to obtain a reference image;
calculating the brightness average value of each reference image, and determining a brightness interval according to the brightness average value;
taking an image area deviating from the brightness section as a defect area in the reference image;
determining the size of the defect area, and if the defect area is smaller than a set threshold, determining the defect area as a point defect; and if the defect area is larger than a set threshold value, determining the surface defect type of the ceramic through a target detection model, wherein the defect type comprises concave glaze, glaze bubbles, brown holes and pinholes.
Determining a brightness interval according to the brightness average value specifically includes: the deviation range of ± 10% of the luminance average value is taken as the luminance section.
In one embodiment, the determining the surface defect type of the ceramic through the target detection model includes:
building a neural network model, and training the network model by adopting a training image to obtain a target detection model, wherein the training image is calibrated with at least one defect type, and the defect type comprises concave glaze, glaze bubbles, brown holes and pinholes;
and identifying the input target detection model larger than the set threshold value to obtain the surface defect type of the ceramic.
In one embodiment, the neural network model is any one of a FasterR-CNN model, a YOLOv3 model, or an SSD model.
Corresponding to the method of fig. 1, an embodiment of the present invention further provides an image processing-based ceramic surface defect detection system, which includes:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor may implement the method for detecting defects on a surface of a ceramic based on image processing according to any of the embodiments.
The contents in the above method embodiments are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above method embodiment, and the beneficial effects achieved by the present system embodiment are also the same as those achieved by the above method embodiment.
In correspondence with the method of fig. 1, an embodiment of the present invention further provides a computer-readable storage medium, on which an image processing-based ceramic surface defect detection program is stored, which, when executed by a processor, implements the steps of the image processing-based ceramic surface defect detection method according to any one of the above embodiments.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor is a control center of the image processing based ceramic surface defect detecting system, and various interfaces and lines are utilized to connect various parts of the whole image processing based ceramic surface defect detecting system operable device.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the image processing-based ceramic surface defect detection system by running or executing the computer programs and/or modules stored in the memory and calling up the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the description of the present application has been made in considerable detail and with particular reference to a few illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed that the present application effectively covers the intended scope of the application by reference to the appended claims, which are interpreted in view of the broad potential of the prior art. Further, the foregoing describes the present application in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial changes from the present application, not presently foreseen, may nonetheless represent equivalents thereto.
Claims (8)
1. An image processing-based ceramic surface defect detection method is characterized by comprising the following steps:
acquiring an RGB image and an NIR image of the ceramic, extracting a first image of a region where the ceramic is located from the RGB image, and extracting a second image of the region where the ceramic is located from the NIR image;
converting the first image into a YUV format to obtain a YUV image, and obtaining a mixed image according to a Y component image and a second image in the YUV image;
and converting the mixed image into a binary image, segmenting a defect area in the binary image, and determining the surface defect type of the ceramic according to the defect area.
2. The method of claim 1, wherein the conversion formula for converting the first image into YUV format is as follows: Y-0.257R + 0.504G + 0.098B +16, U-0.148R-0.291G + 0.439B +128, V-0.439R-0.368G-0.071B + 128;
wherein: y, U, V for YUV images, R, G, B for RGB images.
3. The method for detecting the ceramic surface defects based on the image processing as claimed in claim 2, wherein the calculation formula of the mixed image is as follows: the blended image is 255 × (Y component image in the second image/YUV image).
4. The method for detecting the surface defect of the ceramic based on the image processing as claimed in claim 3, wherein the converting the mixed image into the binary image, segmenting the defect area in the binary image, and determining the surface defect type of the ceramic according to the defect area comprises:
converting the mixed image into a binary image, and reducing the binary image according to a set proportional value in an equal proportion to obtain a reference model; the value range of the set proportion value is [0.001,0.01 ];
starting from one end of the binarized image, segmenting the binarized image by a reference module to obtain a reference image;
calculating the brightness average value of each reference image, and determining a brightness interval according to the brightness average value;
taking an image area deviating from the brightness section as a defect area in the reference image;
determining the size of the defect area, and if the defect area is smaller than a set threshold, determining the defect area as a point defect; and if the defect area is larger than a set threshold value, determining the surface defect type of the ceramic through a target detection model, wherein the defect type comprises concave glaze, glaze bubbles, brown holes and pinholes.
5. The method for detecting the surface defects of the ceramics based on the image processing as claimed in claim 4, wherein the determining the surface defect types of the ceramics through the target detection model comprises:
building a neural network model, and training the network model by adopting a training image to obtain a target detection model, wherein the training image is calibrated with at least one defect type, and the defect type comprises concave glaze, glaze bubbles, brown holes and pinholes;
and identifying the input target detection model larger than the set threshold value to obtain the surface defect type of the ceramic.
6. The method for detecting the ceramic surface defect based on the image processing as claimed in claim 5, wherein the neural network model is any one of a FasterR-CNN model, a YOLOv3 model or an SSD model.
7. An image processing based ceramic surface defect detection system, the system comprising:
at least one processor;
at least one memory for storing at least one program;
when executed by the at least one processor, cause the at least one processor to implement the method of any one of claims 1 to 6 for image processing-based detection of defects on a ceramic surface.
8. A computer-readable storage medium, wherein the computer-readable storage medium has stored thereon an image processing-based ceramic surface defect detection program, which when executed by a processor implements the steps of the image processing-based ceramic surface defect detection method of any one of claims 1 to 6.
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| CN114283324A (en) * | 2021-12-30 | 2022-04-05 | 杭州电子科技大学 | Ceramic teapot image segmentation and identification method and device based on N-cut algorithm |
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