CN117522809A - Method, device and equipment for detecting convex hull of carbon fiber cloth and storage medium - Google Patents
Method, device and equipment for detecting convex hull of carbon fiber cloth and storage medium Download PDFInfo
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- 229920000049 Carbon (fiber) Polymers 0.000 title claims abstract description 54
- 239000004917 carbon fiber Substances 0.000 title claims abstract description 54
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 title claims abstract description 53
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/30—Measuring arrangements characterised by the use of optical techniques for measuring roughness or irregularity of surfaces
- G01B11/303—Measuring arrangements characterised by the use of optical techniques for measuring roughness or irregularity of surfaces using photoelectric detection means
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T7/0004—Industrial image inspection
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- Physics & Mathematics (AREA)
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Abstract
The invention relates to the technical field of carbon fiber detection, in particular to a carbon fiber cloth convex hull detection method, a device, equipment and a storage medium, which comprise the following steps: setting a monitoring area, setting a gray threshold value, a pixel value and a convex hull area value, acquiring color image data, and converting the color image data into gray image data; extracting a region meeting a set gray threshold value from gray image data, performing a closing operation, and outputting the region as a region A; carrying out a region growing algorithm on the gray image data, merging regions larger than the set pixel value, outputting a result region, carrying out an open operation, carrying out a region classifying algorithm, and outputting as a region B; the intersection of the area A and the area B is marked as an area C, and the area C is subtracted by the area B to obtain an area D; and separating the non-communicated region of the region D, selecting the region with the largest area after separation, and alarming if the area is larger than the set convex hull area value. According to the invention, convex hull detection is carried out on the carbon fiber cloth in a visual mode, so that the quality of the carbon fiber cloth is improved.
Description
Technical Field
The invention relates to the technical field of carbon fiber detection, in particular to a carbon fiber cloth convex hull detection method, a device, equipment and a storage medium.
Background
The carbon fiber is a high molecular fibrous carbon material with carbon content over 90%, and is prepared with acrylic fiber and viscose fiber as material and through high temperature oxidation and carbonization.
In the production process of carbon fibers, yarns are woven through a warp knitting machine to form a whole carbon fiber cloth, however, carbon wires or some exogenous foreign matters possibly are woven into the carbon fiber cloth in the production process, so that convex hulls appear on the carbon fiber cloth, the quality of products is affected by the generation of the convex hulls, and the detection of the convex hulls becomes one of important detection indexes of the production qualification rate of the carbon fiber cloth. The traditional detection method needs to be observed manually for detection, however, the traditional manual detection consumes a large amount of human resources, is subject to the condition that the labor and the state of workers are easy to be mistakenly detected and missed to be detected, and is difficult to meet the detection requirement of a large amount of carbon fiber cloth on-line convex hulls on a high-speed production line.
Therefore, the convex hull detection of the carbon fiber cloth is carried out in a visual mode, and the technical problem to be solved is urgent.
Disclosure of Invention
In view of at least one of the above technical problems, the invention provides a method, a device, equipment and a storage medium for detecting convex hulls of carbon fiber cloth, which are used for detecting the convex hulls of the carbon fiber cloth in a visual mode.
According to a first aspect of the present invention, there is provided a carbon fiber cloth convex hull detection method, including the steps of:
s10: setting a monitoring area, setting a gray threshold value, setting a pixel value and setting a convex hull area value, and obtaining color image data of the carbon fiber cloth cover;
s20: converting the color image data into grayscale image data;
s30: extracting the region meeting the set gray threshold value from the gray image data, performing a closing operation, and outputting the region as a region A;
s40: carrying out a region growing algorithm on the gray image data, merging regions larger than the set pixel values, and outputting the merged regions as a result region;
s50: performing open operation on the result area, performing an area classification algorithm, and outputting the result as an area B;
s60: the intersection of the area A and the area B is marked as an area C, and the area C is subtracted by the area B to obtain an area D;
s70: and separating the non-communicated region of the region D, selecting the region with the largest area after separation, and alarming if the area is larger than the set convex hull area value.
In some embodiments of the present invention, in step S40, the following steps are further included:
s41: setting a convolution kernel with a size of 1×1 and a pixel gray difference value of 5;
s42: scanning on the gray image data by using the convolution kernel, and calculating the difference value between the gray of the central point pixel of the rectangular image in the convolution kernel and the gray of the central point pixel of the neighborhood rectangular image;
s43: merging the rectangular image and the area with the neighborhood rectangular image pixel gray level difference less than 5 into a preprocessing area;
s44: and respectively judging the pretreatment areas, and merging the areas with the pixel number larger than the set pixel value in the single pretreatment area into the result area.
In some embodiments of the present invention, in step S50, the region classification algorithm further includes the steps of:
s51: creating a 10 x 10 mask;
s52: scanning the gray image data by using the mask, and calculating the number of pixel points of the result area in the mask;
s53: and when the number of the pixel points is greater than or equal to 30, adding the center point of the mask into the region B.
In some embodiments of the present invention, the set gray threshold is 200-255, the set pixel value is 150, the set convex hull area value is 1000, and the kernel radii of the closed operation and the open operation are 3.5.
According to a second aspect of the present invention, there is also provided a carbon fiber cloth convex hull detection apparatus, including:
and the acquisition module is used for: the method comprises the steps of setting a monitoring area, setting a gray threshold value, setting a pixel value and setting a convex hull area value, and obtaining color image data of the carbon fiber cloth cover;
an image processing module: for converting the color image data into grayscale image data;
a first extraction module: the region which is used for extracting the gray image data and meets the set gray threshold value is subjected to closed operation, and is output as a region A;
and a merging module: the gray image data are used for carrying out a region growing algorithm, combining the regions larger than the set pixel values and outputting the regions as result regions;
and a second extraction module: the method comprises the steps of performing open operation on the result area, performing area classification algorithm, and outputting the result as an area B;
the algorithm module: the intersection of the area A and the area B is marked as an area C, and the area C is subtracted by the area B to obtain an area D;
and a judging module: and the method is used for separating the non-communicated region of the region D, selecting the region with the largest area after separation, and alarming if the area is larger than the set convex hull area value.
In some embodiments of the present invention, in the merging module, the following units are further included:
a setting unit: a convolution kernel for setting a size of 1×1, and a pixel gradation difference value 5;
a scanning unit: the method comprises the steps of scanning on gray image data by using the convolution kernel, and calculating a difference value between the gray of a central point pixel of a rectangular image in the convolution kernel and the gray of a central point pixel of a neighborhood rectangular image;
screening unit: the method comprises the steps of merging a region with the pixel gray difference value smaller than 5 between the rectangular image and the neighborhood rectangular image into a preprocessing region;
recombination unit: and the method is used for judging the pretreatment areas respectively, and combining the areas with the pixel numbers larger than the set pixel values in the single pretreatment area into the result area.
In some embodiments of the present invention, in the second extraction module, the region classification algorithm further includes the following units:
a preset unit: for creating a 10 x 10 mask;
a calculation unit: the method comprises the steps of scanning gray image data by using a mask, and calculating the number of pixel points of the result area in the mask;
an output unit: and adding the center point of the mask into the region B when the number of the pixel points is greater than or equal to 30.
In some embodiments of the present invention, the set gray threshold is 200-255, the set pixel value is 150, the set convex hull area value is 1000, and the kernel radii of the closed operation and the open operation are 3.5.
According to a third aspect of the present invention, there is also provided a carbon fiber cloth convex hull detection apparatus, comprising a computer apparatus including a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described above when executing the computer program.
According to a fourth aspect of the present invention, there is also provided a carbon fiber cloth convex hull detection storage medium comprising a storage medium having stored thereon a computer program which, when executed by a processor, implements the method described above.
The beneficial effects of the invention are as follows: according to the invention, the carbon fiber cloth is shot and converted into gray image data, the gray values of convex hulls and flat parts on the carbon fiber cloth are different due to light reflection, firstly, an area A in a gray threshold value is extracted, then a closed operation is carried out, the gray image data is subjected to an area growth algorithm, the areas with the pixel number larger than a set pixel value are combined into a result area, the result area is subjected to an open operation and area classification algorithm and output into an area B, the intersection of the area A and the area B is recorded as an area C, the area C is subtracted by the area B to obtain an area D which is an area containing the convex hulls, finally, the non-connected area of the area D is separated, the largest area is selected for judgment, and after the pixel number in the area is larger than the set convex hull area value, an alarm is carried out, so that the convex hull of the carbon fiber cloth is detected in a visual mode.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
FIG. 1 is a flow chart of steps of a method for detecting convex hulls of carbon fiber cloth according to an embodiment of the present invention;
FIG. 2 is a calculation flow chart of a carbon fiber cloth convex hull detection method in an embodiment of the invention;
FIG. 3 is a diagram showing the relationship between areas of a carbon fiber cloth convex hull detection method according to an embodiment of the present invention;
FIG. 4 is a gray image data diagram of a carbon fiber cloth convex hull detection method according to an embodiment of the present invention;
fig. 5 is a diagram of the area maximum in the area D of the carbon fiber cloth convex hull detection method according to the embodiment of the present invention;
fig. 6 is a schematic structural diagram of a carbon fiber cloth convex hull detection device in an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a carbon fiber cloth convex hull detection computer device in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
The method for detecting the convex hull of the carbon fiber cloth as shown in fig. 1 to 5 comprises the following steps:
s10: setting a monitoring area, setting a gray threshold value, setting a pixel value and setting a convex hull area value, and obtaining color image data of the carbon fiber cloth cover; when the convex hull detection is carried out on the carbon fiber cloth, the image collector is used for shooting the carbon fiber on the machine, and various data are set before the detection, wherein the values of the set gray threshold value, the set pixel value and the set convex hull area value can be adjusted according to the real-time background gray value.
S20: converting the color image data into gray scale image data; as shown in fig. 4, by converting color image data into gray image data, since the gray values formed by the light refraction of the impurities and the convex hulls on the carbon fiber cloth are different, the difference of the gray values between the areas is used for screening and judging.
S30: extracting a region meeting a set gray threshold value from gray image data, performing a closing operation, and outputting the region as a region A; and extracting a part of areas without convex hulls by setting a gray threshold value, performing a closing operation, and finally outputting the areas as an area A. It should be noted that the closed operation is one of the morphological processing operations, and is not described herein.
S40: carrying out a region growing algorithm on the gray image data, merging regions larger than the set pixel value, and outputting the merged regions as a result region; and calculating gray image data through a region growing algorithm, selecting a region with a pixel value larger than a set speed limit value, merging, and waiting for the next operation in the merged region. It is to be noted here that the resultant area includes a black area having a convex hull displayed on the gradation image data and a white area due to the lamp light.
S50: carrying out open operation on the result area, then carrying out an area classification algorithm, and outputting the result as an area B; and performing open operation on the result areas, and performing an area classification algorithm to connect the separated areas with relatively close distances in the result areas. It should also be noted that, the open operation is one of the morphological processing operations, and is not described herein.
S60: the intersection of the area A and the area B is marked as an area C, and the area C is subtracted by the area B to obtain an area D; as shown in fig. 3, the region D having the convex hull is obtained by calculation between the regions.
S70: and separating the non-communicated region of the region D, selecting the region with the largest separated area, and alarming if the area is larger than the set convex hull area value. In the area D, there may be multiple areas with convex hulls, separating the area D, separating the areas that are not connected in the area D, selecting the portion with the largest area for judgment, as shown in fig. 5, and alarming when the area is larger than the set convex hull area value.
As shown in fig. 1 to 5, the present invention photographs and converts the gray image data by using a carbon fiber cloth, the gray values of a convex hull and a flat portion on the carbon fiber cloth are different due to the reflection of light, firstly, an area a within a set gray threshold is extracted and closed operation is performed, then, an area growing algorithm is performed on the gray image data, the areas with the pixel number larger than the set pixel value are combined into a result area, and an opening operation and an area classification algorithm are performed on the result area, and the result area is output as an area B, then, the intersection of the area a and the area B is recorded as an area C, the area C is subtracted by using the area B to obtain an area D, finally, the non-connected area of the area D is separated, the largest area is selected and judged, and after the pixel number in the area is larger than the set convex hull area value, an alarm is performed, so that the convex hull is detected by the carbon fiber cloth in a visual manner. It should be noted that, the open operation and the close operation are both prior art, and are not described herein.
In S40, the gray image data is subjected to a region growing algorithm, and the regions larger than the set pixel value are combined and output as a result region, which comprises the following steps:
s41: setting a convolution kernel with a size of 1×1 and a pixel gray difference value of 5;
s42: scanning on the gray image data by using a convolution kernel, and calculating the difference value between the gray level of the central point pixel of the rectangular image in the convolution kernel and the gray level of the central point pixel of the neighborhood rectangular image;
s43: merging the areas with pixel gray differences smaller than 5 of the rectangular images and the neighborhood rectangular images into a preprocessing area;
s44: and respectively judging the plurality of pretreatment areas, and combining the areas with the pixel number larger than the set pixel value in the single pretreatment area into a result area.
As shown in fig. 2, all the scanning is performed by using 1×1 convolution kernel gray image data, when the difference between the gray level of the center point pixel of the rectangular image in the convolution kernel and the gray level of the center point pixel of the neighborhood rectangular image is smaller than the set pixel value 5, the rectangular image and the rectangular image of the neighborhood are combined to form a pre-processing area, then the pre-processing area is judged, when the pixel value in the pre-processing area is smaller than or equal to the set pixel value, the pre-processing area is abandoned, when the pixel value in the pre-processing area is larger than the set pixel value, the pre-processing area is reserved, and when all the pre-processing areas larger than the set pixel value are combined after all the pre-processing areas are judged, the result area is formed. The result area comprises a black area where the convex hull is located and a white area caused by lamplight.
S50, carrying out open operation on a result area, and then carrying out an area classification algorithm, and outputting the result as an area B, wherein the area classification algorithm comprises the following steps:
s51: creating a 10 x 10 mask;
s52: scanning gray image data by using a mask, and calculating the number of pixel points in the mask in a result area;
s53: when the number of pixel points is 30 or more, the center point of the mask is added to the region B.
In the present invention, as shown in fig. 5, the whole gray image data is scanned through a 10×10 mask, and the number of pixels in the resulting area in the mask is calculated, and when the number of pixels is 30 or more, the center point of the mask is added into the area B; and when the number of the pixel points is less than or equal to 30, skipping the partial region, and continuing to calculate the next partial region. The grayscale image data is scanned in a sequential loop to determine the extent of region B.
On the basis of the above embodiment, the gray threshold is set to 200-255, the pixel value is set to 150, the convex hull area value is set to 1000, and the inner core radius of both the closed operation and the open operation is 3.5. When the area A is marked, selecting a white area in gray image data, setting a set gray threshold value to be 200-255, and selecting all areas between white and a difference of 5 between the white gray value and the white gray value into the area A, wherein the area A is an area without convex hulls. The method comprises the steps of combining a rectangular image center point with a pixel gray difference value of 5 in a convolution kernel with a neighborhood rectangular image center point, reserving when the number of pixel points in a preprocessing area is greater than a set pixel value of 150 and discarding when the number of pixel points in the preprocessing area is less than or equal to 150, and combining to form a result area. After separating the non-connected areas of the area D, selecting the largest area for judgment, and ignoring when the area value of the convex hull set by the pixels of the area is less than or equal to 1000; and when the pixel setting convex hull area value of the area region is more than 1000, alarming. When the result area is subjected to the open operation and the close operation, the kernels of the open operation and the close operation are set to be 3.5 in radius.
It should be appreciated by those skilled in the art that the embodiments of the present application may be provided as a method, an apparatus, an electronic device, or a computer storage medium product, and thus, the embodiments of the present application may be implemented entirely in hardware embodiments, in embodiments combining hardware and software, or in pure software embodiments, and the following description of the test detection process data processing apparatus in the embodiments of the present application, where the following apparatus embodiments correspond to the foregoing method embodiments, and those skilled in the art may understand the following implementation based on the foregoing description and will not be described in detail herein.
The carbon fiber convex hull detection apparatus as shown in fig. 6 includes:
and the acquisition module is used for: the method comprises the steps of setting a monitoring area, setting a gray threshold value, setting a pixel value and setting a convex hull area value, and obtaining color image data of the carbon fiber cloth cover;
an image processing module: for converting color image data into grayscale image data;
a first extraction module: the method comprises the steps of extracting a region meeting a set gray threshold value from gray image data, performing a closing operation, and outputting the region as a region A;
and a merging module: the method comprises the steps of performing a region growing algorithm on gray image data, merging regions larger than a set pixel value, and outputting the merged regions as a result region;
and a second extraction module: the method comprises the steps of performing open operation on a result area, performing an area classification algorithm, and outputting a result as an area B;
the algorithm module: the intersection of the area A and the area B is marked as an area C, and the area C is subtracted by the area B to obtain an area D;
and a judging module: and the method is used for separating the non-communicated region of the region D, selecting the region with the largest area after separation, and alarming if the area is larger than the set convex hull area value.
In some embodiments of the present invention, in the merging module, the following units are further included:
a setting unit: a convolution kernel for setting a size of 1×1, and a pixel gradation difference value 5;
a scanning unit: the method comprises the steps of scanning gray image data by using a convolution kernel, and calculating a difference value between the gray level of a central point pixel of a rectangular image in the convolution kernel and the gray level of a central point pixel of a neighborhood rectangular image;
screening unit: the method comprises the steps of merging a region with a pixel gray difference value smaller than 5 between a rectangular image and a neighborhood rectangular image into a preprocessing region;
recombination unit: and the method is used for judging the plurality of pretreatment areas respectively, and combining the areas with the pixel number larger than the set pixel value in the single pretreatment area into a result area.
In some embodiments of the present invention, in the second extraction module, the region classification algorithm further includes the following units:
a preset unit: for creating a 10 x 10 mask;
a calculation unit: the method comprises the steps of scanning gray image data by using a mask, and calculating the number of pixel points of a result area in the mask;
an output unit: for adding the center point of the mask to the region B when the number of pixel points is 30 or more.
In some embodiments of the present invention, the gray threshold is set to be 200-255, the pixel value is set to be 150, the convex hull area value is set to be 1000, and the core radius of the closed operation and the open operation is 3.5.
In the following sections of the embodiments of the present invention, the embodiments of the electronic device and the computer storage medium in the embodiments of the present invention are described, and the embodiments of the electronic device and the computer storage medium in the following correspond to the embodiments of the method in the foregoing, and those skilled in the art may understand the following implementation process based on the foregoing description, which is not described in detail herein.
As shown in fig. 7, a schematic structural diagram of a computer device provided in an embodiment of the present application, a computer device 400 provided in an embodiment of the present application includes: the memory 420, the processor 410, and a computer program stored on the memory 420 and executable on the processor 410, the processor 410 implementing the method described above when executing the computer program.
The present embodiment also provides a storage medium 430 having stored thereon a computer program which, when executed by the processor 410, implements the method described above.
The storage medium 430 may be implemented by any type or combination of volatile or nonvolatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The meaning of "a plurality of" is two or more, unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present specification, a description referring to terms "one embodiment," "some 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 invention. In this specification, schematic representations of the above terms are not necessarily for the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. The carbon fiber cloth convex hull detection method is characterized by comprising the following steps of:
s10: setting a monitoring area, setting a gray threshold value, setting a pixel value and setting a convex hull area value, and obtaining color image data of the carbon fiber cloth cover;
s20: converting the color image data into grayscale image data;
s30: extracting the region meeting the set gray threshold value from the gray image data, performing a closing operation, and outputting the region as a region A;
s40: carrying out a region growing algorithm on the gray image data, merging regions larger than the set pixel values, and outputting the merged regions as a result region;
s50: performing open operation on the result area, performing an area classification algorithm, and outputting the result as an area B;
s60: the intersection of the area A and the area B is marked as an area C, and the area C is subtracted by the area B to obtain an area D;
s70: and separating the non-communicated region of the region D, selecting the region with the largest area after separation, and alarming if the area is larger than the set convex hull area value.
2. The method for detecting the convex hull of the carbon fiber cloth according to claim 1, further comprising the steps of:
s41: setting a convolution kernel with a size of 1×1 and a pixel gray difference value of 5;
s42: scanning on the gray image data by using the convolution kernel, and calculating the difference value between the gray of the central point pixel of the rectangular image in the convolution kernel and the gray of the central point pixel of the neighborhood rectangular image;
s43: merging the rectangular image and the area with the neighborhood rectangular image pixel gray level difference less than 5 into a preprocessing area;
s44: and respectively judging the pretreatment areas, and merging the areas with the pixel number larger than the set pixel value in the single pretreatment area into the result area.
3. The method for detecting a convex hull of carbon fiber cloth according to claim 1, wherein in step S50, the region classification algorithm further comprises the steps of:
s51: creating a 10 x 10 mask;
s52: scanning the gray image data by using the mask, and calculating the number of pixel points of the result area in the mask;
s53: and when the number of the pixel points is greater than or equal to 30, adding the center point of the mask into the region B.
4. The method for detecting the convex hull of the carbon fiber cloth according to claim 1, wherein the set gray threshold is 200-255, the set pixel value is 150, the set convex hull area value is 1000, and the inner core radiuses of the closed operation and the open operation are 3.5.
5. The utility model provides a carbon fiber cloth convex closure detection device which characterized in that includes:
and the acquisition module is used for: the method comprises the steps of setting a monitoring area, setting a gray threshold value, setting a pixel value and setting a convex hull area value, and obtaining color image data of the carbon fiber cloth cover;
an image processing module: for converting the color image data into grayscale image data;
a first extraction module: the region which is used for extracting the gray image data and meets the set gray threshold value is subjected to closed operation, and is output as a region A;
and a merging module: the gray image data are used for carrying out a region growing algorithm, combining the regions larger than the set pixel values and outputting the regions as result regions;
and a second extraction module: the method comprises the steps of performing open operation on the result area, performing area classification algorithm, and outputting the result as an area B;
the algorithm module: the intersection of the area A and the area B is marked as an area C, and the area C is subtracted by the area B to obtain an area D;
and a judging module: and the method is used for separating the non-communicated region of the region D, selecting the region with the largest area after separation, and alarming if the area is larger than the set convex hull area value.
6. The carbon fiber cloth convex hull detection apparatus according to claim 5, further comprising, in the merging module:
a setting unit: a convolution kernel for setting a size of 1×1, and a pixel gradation difference value 5;
a scanning unit: the method comprises the steps of scanning on gray image data by using the convolution kernel, and calculating a difference value between the gray of a central point pixel of a rectangular image in the convolution kernel and the gray of a central point pixel of a neighborhood rectangular image;
screening unit: the method comprises the steps of merging a region with the pixel gray difference value smaller than 5 between the rectangular image and the neighborhood rectangular image into a preprocessing region;
recombination unit: and the method is used for judging the pretreatment areas respectively, and combining the areas with the pixel numbers larger than the set pixel values in the single pretreatment area into the result area.
7. The carbon fiber cloth convex hull detection apparatus according to claim 5, wherein in the second extraction module, the region classification algorithm further includes:
a preset unit: for creating a 10 x 10 mask;
a calculation unit: the method comprises the steps of scanning gray image data by using a mask, and calculating the number of pixel points of the result area in the mask;
an output unit: and adding the center point of the mask into the region B when the number of the pixel points is greater than or equal to 30.
8. The carbon fiber cloth convex hull detection apparatus according to claim 5, wherein the set gray threshold is 200-255, the set pixel value is 150, the set convex hull area value is 1000, and the inner core radii of the closed operation and the open operation are 3.5.
9. A computer 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 any of claims 1-4 when executing the computer program.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-4.
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