CN115690011A - Synthetic adhesive non-woven fabric stain detection method based on optical information - Google Patents
Synthetic adhesive non-woven fabric stain detection method based on optical information Download PDFInfo
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Abstract
The invention relates to the technical field of material defect detection and analysis, in particular to a synthetic adhesive non-woven fabric stain detection method based on optical information. The method comprises the steps of obtaining a first image of the non-woven fabric adhesive tape under a white light source and a second image of the non-woven fabric adhesive tape under an alternative light source, and calculating the difference degree between the first image and the second image, wherein when the difference degree is larger than a preset threshold value, the non-woven fabric adhesive tape is a suspected defect adhesive tape; adjusting the color of the alternative light source to shoot a plurality of second images of the suspected-defect adhesive tape, calculating the difference degree between each second image and the first image, taking the second image with the difference degree larger than a preset threshold value as a contrast image, and subtracting the first image from the contrast image to obtain a difference image; dividing the difference image into a plurality of sub-areas, clustering all the sub-areas to obtain a plurality of cluster clusters, and obtaining a stain area of the suspected defect adhesive tape according to the optimal clustering effect; the accuracy of the analysis of the taint area is improved.
Description
Technical Field
The invention relates to the technical field of material defect detection and analysis, in particular to a synthetic adhesive non-woven fabric stain detection method based on optical information.
Background
The medical non-woven fabric adhesive tape is an important medical resource in the treatment and binding process. Due to the sticky nature of the nonwoven tape itself, there is a possibility that stains will contaminate the tape during its manufacture, and therefore surface stain detection has been an important part of quality detection during manufacture of nonwoven tapes.
The existing detection method for detecting whether stains exist on the surface of the non-woven fabric is usually manual detection, a large amount of human resources are consumed, the efficiency is low, and the stains are often detected inaccurately due to the textures on the surface of the non-woven fabric when the stains are detected visually.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides a method for detecting stains on a synthetic adhesive nonwoven fabric based on optical information, the method comprising the steps of:
acquiring a first image of a non-woven fabric adhesive tape under a white light source and a second image of the non-woven fabric adhesive tape under an alternative light source, and calculating the difference degree between the first image and the second image, wherein when the difference degree is greater than a preset threshold value, the non-woven fabric adhesive tape is a suspected defect adhesive tape;
adjusting the color of an alternative light source to shoot a plurality of second images of the suspected defect adhesive tape, calculating the difference degree between each second image and the first image, taking the second image with the difference degree larger than a preset threshold value as a contrast image, and subtracting the first image and the contrast image to obtain a difference image;
dividing the difference image into a plurality of sub-areas, clustering all the sub-areas to obtain a plurality of cluster clusters, and obtaining a stain area of the suspected defect adhesive tape according to the optimal clustering effect;
the evaluation method of the clustering effect comprises the following steps:
acquiring a first evaluation index based on the intra-class minimum principle of the cluster, and acquiring a second evaluation index based on the inter-class maximum principle of all clusters;
acquiring a cluster with the most elements in all cluster clusters as a reference cluster, and obtaining a third evaluation index based on the difference between the gray gradient direction and the expected gradient direction of each pixel point in the reference cluster;
dividing all the clustering clusters into a normal group and an abnormal group, and obtaining a fourth evaluation index based on the difference between the corresponding image characteristics of the normal group and the abnormal group;
and obtaining an evaluation index of the clustering effect according to the first evaluation index, the second evaluation index, the third evaluation index and the fourth evaluation index, wherein the best clustering effect is obtained when the evaluation index is the maximum.
Preferably, the step of calculating the degree of difference between the first image and the second image includes:
respectively obtaining the gray level intermediate value of a first image and the gray level intermediate value of a second image, and obtaining the difference degree between the first image and the second image based on the gray level intermediate values.
Preferably, the step of obtaining the difference degree between the first image and the second image based on the intermediate gray value includes:
acquiring an overlapping gray level range of the first image and the second image based on a gray level intermediate value of the first image and a gray level intermediate value of the second image, updating the gray level range of the first image and the gray level range of the second image based on the overlapping gray level range, and acquiring the probability of each gray level in the updated gray level range of the first image and the probability of each gray level in the updated gray level range of the second image; and obtaining the difference degree between the first image and the second image based on the difference of the probabilities between the gray levels of the corresponding positions.
Preferably, the difference between the first image and the comparison image is the difference between the gray value of each pixel in the first image and the gray value of the corresponding pixel in the comparison image.
Preferably, the method for dividing the difference image into a plurality of sub-regions is a region growing algorithm.
Preferably, the method for acquiring the first evaluation index includes:
acquiring an average gray value and a center coordinate corresponding to each sub-region, and calculating the first evaluation index based on the average gray value and the center coordinate as follows:
wherein, the first and the second end of the pipe are connected with each other,
represents a first evaluation index;
is shown as
Average gray values of all sub-areas in each cluster;
is shown as
The center coordinates of the individual cluster;
represents the first in the cluster
Average gray value of the sub-regions;
represents the first in the cluster
Center coordinates of the individual sub-regions;
indicating the number of all sub-regions in the cluster.
Preferably, the calculation formula of the second evaluation index is:
wherein, the first and the second end of the pipe are connected with each other,
represents a second evaluation index;
representing the average gray value of all sub-areas in the nth clustering cluster;
is shown as
The center coordinates of the individual cluster;
representing the average gray value of all the cluster clusters;
representing the center coordinates of all the cluster clusters;
indicating the number of all cluster clusters.
Preferably, the step of obtaining a third evaluation index based on a difference between the gradient direction of each pixel point in the reference cluster and an expected gradient direction includes:
and obtaining the average value of the difference between the gray gradient direction and the expected gradient direction of all the pixel points in the reference cluster as a third evaluation index.
Preferably, the step of obtaining a fourth evaluation index based on a difference between the corresponding image features of the normal group and the abnormal group includes:
acquiring a gray level co-occurrence matrix corresponding to the normal group and a gray level co-occurrence matrix corresponding to the abnormal group, and calculating a description operator corresponding to each gray level co-occurrence matrix, wherein the description operator is contrast and energy; and obtaining a fourth evaluation index based on the difference between the descriptor corresponding to the normal group and the descriptor corresponding to the abnormal group.
Preferably, the evaluation index is in a negative correlation with the first evaluation index, in a positive correlation with the second evaluation index, in a negative correlation with the third evaluation index, and in a positive correlation with the fourth evaluation index.
The invention has the following beneficial effects: whether a stain area exists or not is preliminarily judged based on a first image and a second image of the non-woven fabric double-faced adhesive tape under different point light sources, suspected defect adhesive tapes possibly having stains are further analyzed to obtain a difference image, the difference image is subjected to area division and clustering, a stain area and a normal area are obtained in all corresponding clustering clusters when the clustering effect is the best, the accuracy of clustering analysis is improved through evaluation of the clustering effect, and the stain area is detected based on a more accurate clustering result so that the detection result is more reliable and accurate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a method for detecting spots on a synthetic adhesive nonwoven fabric based on optical information according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined objects, the following detailed description of the method for detecting stains on synthetic adhesive non-woven fabric based on optical information, the detailed implementation, structure, features and effects thereof are provided in the accompanying drawings and preferred embodiments. In the following description, the different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The method is suitable for performing stain detection on the non-woven fabric double faced adhesive tape in the glue non-woven fabric, analyzing through the difference between images under different light sources, obtaining the final stain area based on the optimal clustering effect, and improving the accuracy of analysis and identification.
The following describes a specific scheme of the method for detecting stains on synthetic adhesive non-woven fabric based on optical information in detail with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of a method for detecting stains on a synthetic adhesive nonwoven fabric based on optical information according to an embodiment of the present invention is shown, the method including the following steps:
and S100, acquiring a first image of the non-woven fabric adhesive tape under a white light source and a second image of the non-woven fabric adhesive tape under an alternative light source, and calculating the difference degree between the first image and the second image, wherein when the difference degree is greater than a preset threshold value, the non-woven fabric adhesive tape is a suspected defect adhesive tape.
Specifically, because the non-woven fabric double-sided adhesive tape has the characteristic of light transmission, a point light source and a camera are arranged to acquire an image of the non-woven fabric double-sided adhesive tape, the point light source is arranged on one side of the non-woven fabric double-sided adhesive tape, the camera is arranged on the other side of the non-woven fabric double-sided adhesive tape, the point light source is a white light source, and the camera is used for acquiring the image of the non-woven fabric double-sided adhesive tape to obtain a first image; in order to increase the accuracy of analysis, the same point light source is arranged at the position of the camera, the color of the point light source is different from that of white, the two point light sources and the position of the camera are on the same connecting line, and at the moment, the camera is used for collecting a second image of the non-woven fabric double-sided adhesive tape under the point light source.
Furthermore, due to the fact that the non-woven double-sided adhesive tape has certain texture characteristics, stains on the surface of the non-woven fabric are difficult to detect; however, when the stain is stained on the double-sided adhesive of the non-woven fabric, permeation may occur, so that the light transmittance of the stain area on the double-sided adhesive of the non-woven fabric is changed, and therefore whether the stain area exists on the double-sided adhesive of the non-woven fabric can be preliminarily judged based on the color of the light source.
When no stain area exists on the double-sided adhesive of the non-woven fabric, the outlines of the gray level histograms corresponding to the first image and the second image which are acquired under different light sources are consistent; when a stain area exists, the surface of the non-woven double-sided adhesive tape changes, so that the sensitivity degrees of the non-woven double-sided adhesive tape to light are inconsistent, the shape profiles of the gray level histograms corresponding to the first image and the second image respectively under different light sources change, and therefore the difference degree between the first image and the second image is calculated. The specific method for acquiring the difference between the first image and the second image comprises the following steps:
firstly, the gray level intermediate value of the first image and the gray level intermediate value of the second image are respectively obtained, and the difference degree between the first image and the second image is obtained based on the gray level intermediate values.
Acquiring a gray level intermediate value corresponding to the first image; counting the occurrence frequency of each gray level in the first image to obtain the occurrence probability of each gray level, accumulating corresponding probabilities from the minimum value of the gray levels, and when the sum of the accumulated probabilities is equal to 0.5, recording the corresponding gray level as the gray level intermediate value of the first image, namely:
wherein, the first and the second end of the pipe are connected with each other,
representing a minimum value of gray levels in the first image;
representing the first in the first image
Probability corresponding to each gray level;
denotes the first
Gray level, i.e. the gray level median.
That is, the probabilities corresponding to the minimum gray levels in the first image are sequentially accumulated, wherein the accumulation order is from small gray level to large gray level until the first image is accumulated
At each gray level, the sum of the probabilities corresponding to all gray levels is 0.5, so the second one
The gray levels are intermediate gray values.
Based on the method for obtaining the same gray level intermediate value of the first image, the gray level intermediate value corresponding to the second image is obtained, and the gray level intermediate values corresponding to the first image and the second image are recorded as the gray level intermediate values respectively
，
。
Then, acquiring an overlapping gray level range of the first image and the second image based on the gray level intermediate value of the first image and the gray level intermediate value of the second image, updating the gray level range of the first image and the gray level range of the second image based on the overlapping gray level range, and acquiring the probability of each gray level in the updated gray level range of the first image and the probability of each gray level in the updated gray level range of the second image; and obtaining the difference degree between the first image and the second image based on the difference of the probabilities between the gray levels of the corresponding positions.
Specifically, the overlapping gray level range between the first image and the second image is obtained based on the gray level intermediate value corresponding to each image, and the original gray level range of the first image is assumed to be
The original gray scale range of the second image is
(ii) a Wherein, the first and the second end of the pipe are connected with each other,
representing a minimum gray level in the first image;
representing a maximum gray level in the first image;
representing a minimum gray level in the second image;
representing the maximum gray level in the second image.
The overlapping gray scale range of the first image and the second image is
(ii) a Wherein
。
Updating a gray level range of the first image based on the overlapping gray level range to
(ii) a Updating the gray scale range of the second image to
。
As an example, assume that the first image has a gray scale range of
The gray scale range of the second image is
(ii) a If the median gray value of the first image is 40 and the median gray value of the second image is 50, the overlapping gray level range is
(ii) a The overlapping gray scale ranges thus update the gray scale range of the first image to
The overlapping gray level range updates the gray level range of the second image to
。
And finally, acquiring the probability of each updated gray level in the first image and the probability of each updated gray level in the second image, and acquiring the difference degree between the first image and the second image based on the difference between the probabilities as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing the degree of difference;
indicating updated second image in first image
Probability corresponding to each gray level;
indicating updated second in second image
Probability corresponding to each gray level;
representing a gray level intermediate value corresponding to the first image;
representing the gray level intermediate value corresponding to the second image;
a minimum gray level representing a range of overlapping gray levels;
representing the maximum gray level of the overlapping gray level range.
In the first image, the first image is
A gray scale level associated with the second image
Each gray level is a gray level of a corresponding position in the gray level range; when the difference degree is larger than the preset threshold value, it is indicated that a large difference exists between the first image and the second image, so that stains may exist in the currently shot non-woven fabric double-sided adhesive tape, the non-woven fabric double-sided adhesive tape is marked as a suspected defect adhesive tape, and the current suspected defect adhesive tape needs to be further analyzed.
Preferably, the threshold value of the difference degree is set to be 0.05 in the embodiment of the invention, namely when the difference degree between the first image and the second image is greater than 0.05, the non-woven double-sided adhesive tape needs to be further analyzed.
Step S200, adjusting the color of the alternative light source to shoot a plurality of second images of the suspected defect adhesive tape, calculating the difference degree between each second image and the first image, taking the second image with the difference degree larger than a preset threshold value as a contrast image, and subtracting the first image and the contrast image to obtain a difference image.
Specifically, the double-sided adhesive tape of the non-woven fabric obtained in step S100 is a suspected defective adhesive tape with a stain, and the point light source is changed based on the characteristic that the stain is different from the material of the non-woven fabric, so as to further determine the stain area. In the embodiment of the invention, a new second image of the suspected defect adhesive tape is obtained by adjusting the color of the point light source and is marked as a contrast image, and the confirmation method of the contrast image comprises the following steps: acquiring a new second image of the suspected-defect adhesive tape under the point light sources with different colors, and calculating the difference between the second image and the first image in the step S100, wherein the method for calculating the difference is the method in the step S100; and the color of the point light source corresponding to the second image when the difference degree is greater than the preset threshold value is obtained as the determined color, and the second image at the moment is a contrast image.
Preferably, in the embodiment of the present invention, the preset threshold is set to 0.1, that is, when the difference between the second image and the first image is greater than 0.1, the color of the corresponding point light source is the final color, and the second image is the contrast image at this time.
It should be noted that, when the difference degrees between all the second images and the first image are less than 0.1, the second image corresponding to the largest difference degree among all the difference degrees is selected as the final contrast image.
Further, the first image and the contrast image are subtracted to obtain a difference image, the difference is the difference between the gray value of each pixel point in the first image and the gray value of the corresponding pixel point in the contrast image, that is:
wherein the content of the first and second substances,
representing coordinates in the first image as
The gray value of the pixel point;
representing coordinates in the contrast image as
The gray value of the pixel point;
representing the difference value of the pixel point;
and by analogy, obtaining the difference value of each pixel point of the first image and the second image, and taking an image formed by the corresponding difference values of all the pixel points as a difference image.
And step S300, dividing the difference image into a plurality of sub-areas, clustering all the sub-areas to obtain a plurality of cluster clusters, and obtaining a stain area of the suspected defect adhesive tape according to the optimal clustering effect.
Obtaining a difference image corresponding to the suspected defect tape in the step S200, and performing preliminary division on the difference image to obtain a plurality of sub-regions, wherein the division method in the embodiment of the invention adopts a region growing algorithm, any point in the difference image is a seed point, region growing is performed by taking the seed point as a center, and the growing condition is a point which is consistent with the gray value of the pixel point of the seed point; and so on, dividing the difference image into a plurality of sub-regions.
Further, acquiring the average gray value and the center coordinate of all pixel points in each sub-region, wherein the center coordinate is the coordinate of the center point in the sub-region; and clustering all the sub-areas according to the average gray value and the central coordinate of each sub-area, so that the areas with the stains are divided into the same category, and the stain areas are obtained. In the embodiment of the invention, a k-means clustering algorithm of a self-adaptive k value is adopted, and the optimal clustering effect is obtained by continuously updating the k value, so that a stain area in the suspected defect adhesive tape is obtained. When all the subregions are clustered, the distance between any two subregions is as follows:
wherein the content of the first and second substances,
representing the distance between two sub-regions;
denotes the first
Average gray value of the sub-regions;
denotes the first
Average gray value of the sub-regions;
denotes the first
The center coordinates of each sub-area;
is shown as
The center coordinates of each sub-region.
Preferably, in the embodiment of the invention, the k value range in the k-means clustering algorithm is set as [1,20], and different clustering clusters are obtained by continuously updating the value of k; and selecting the one with the best clustering effect in multiple clustering as a final clustering result.
The evaluation method of the clustering effect comprises the following steps: acquiring a first evaluation index based on an intra-class minimum principle of a cluster, and acquiring a second evaluation index based on an inter-class maximum principle of the cluster; acquiring a cluster with the most elements in all the cluster clusters as a reference cluster, and obtaining a third evaluation index based on the difference between the gradient direction of each pixel point in the reference cluster and the expected gradient direction; dividing all clustering clusters into normal groups and abnormal groups, and obtaining a fourth evaluation index based on the difference between the corresponding image characteristics of the normal groups and the abnormal groups; and obtaining an evaluation index of the clustering effect according to the first evaluation index, the second evaluation index, the third evaluation index and the fourth evaluation index, wherein the best clustering effect is obtained when the evaluation index is the maximum.
Firstly, the clustering effect should satisfy the condition that the difference between all sub-regions in each clustering cluster is minimum, namely the first evaluation index for obtaining the clustering effect based on the principle of minimum intra-class difference is as follows:
wherein, the first and the second end of the pipe are connected with each other,
represents a first evaluation index;
is shown as
Average gray values of all sub-regions in each cluster;
denotes the first
The center coordinates of the individual cluster;
represents the first in the cluster
Average gray value of the sub-regions;
represents the first in the cluster
Center coordinates of the individual sub-regions;
representing the number of all sub-regions in the cluster.
Then, the clustering effect should satisfy the condition that the difference between each clustering cluster is the largest, that is, the second evaluation index for obtaining the clustering effect based on the principle of the largest difference between the clusters is as follows:
wherein, the first and the second end of the pipe are connected with each other,
represents a second evaluation index;
representing the average gray value of all sub-areas in the nth clustering cluster;
is shown as
The center coordinates of the individual cluster;
representing the average gray value of all the cluster clusters;
representing the center coordinates of all cluster clusters;
indicating the number of all clusters.
Because the stain area on the double-sided adhesive tape of the non-woven fabric only occupies a small part, the cluster containing the most elements in the clusters is a normal part of the suspected defect adhesive tape, the expected gradient direction of each pixel point in the cluster is related to the position of the cluster, under the irradiation of a point light source, the expected gradient direction of each pixel point is a direction in which the pixel point at the position opposite to the point light source is taken as the center and has divergence, the actual gray gradient direction of each pixel point in the actual clustering result possibly has difference, and the average value of the difference between the gray gradient direction and the expected gradient direction of all the pixel points in the reference cluster is obtained as a third evaluation index.
Specifically, the cluster including the most pixel points in all the clusters is obtained as a reference cluster, and the gray gradient direction of each pixel point in the reference cluster is obtained, and the calculation method of the gradient direction is a known technology and is not described again. The expected gradient direction of the pixel point can be obtained according to the position of the point light source and the position of the pixel point as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing the expected gradient direction of the pixel point;
representing the coordinate position corresponding to the pixel point;
and the coordinate position of the pixel point at the position just opposite to the point light source is represented, namely the coordinate position of the central pixel point of the first image.
Obtaining the difference between the gray scale gradient direction and the expected gradient direction of the pixel point according to the obtained absolute value of the difference between the gray scale gradient direction and the expected gradient direction of the pixel point; by analogy, the differences corresponding to all the pixel points in the reference cluster are obtained, so that the third evaluation index for obtaining the clustering effect based on the differences corresponding to all the pixel points is as follows:
wherein the content of the first and second substances,
represents a third evaluation index;
indicates the first in the reference cluster
The difference between the gray scale gradient direction corresponding to each pixel point and the expected gradient direction;
and the number of all pixel points of the reference cluster is represented.
Further, considering that the difference of textures between a stain area and a normal area of the non-woven fabric synthetic adhesive is large, all clustered clusters obtained through clustering are preliminarily classified, one cluster with the most pixel points in the clustered clusters is recorded as a normal group, all other clustered clusters are recorded as abnormal groups, a gray level co-occurrence matrix corresponding to the normal group and a gray level co-occurrence matrix corresponding to the abnormal group are obtained, a description operator corresponding to each gray level co-occurrence matrix is calculated, and the description operator is contrast and energy; and obtaining a fourth evaluation index based on the difference between the descriptor corresponding to the normal group and the descriptor corresponding to the abnormal group.
Specifically, a gray level co-occurrence matrix of an image in a region corresponding to a normal group and a gray level co-occurrence matrix of an image in a region corresponding to an abnormal group are obtained, and a corresponding description operator is obtained based on the gray level co-occurrence matrix corresponding to each group, wherein the description operator in the embodiment of the invention comprises ASM (absolute second moment) energy and CON (contrast); the ASM energy is used for reflecting the image distribution uniformity and the texture thickness, and the CON contrast is used for reflecting the image definition and the texture depth; the gray level co-occurrence matrix and the calculation method of the description operator thereof are the prior known technologies and are not described in detail.
The fourth evaluation index for obtaining the clustering effect based on the description operators corresponding to the normal group and the abnormal group is as follows:
wherein the content of the first and second substances,
represents a fourth evaluation index;
representing the contrast of the gray level co-occurrence matrix corresponding to the normal group;
representing the contrast of the gray level co-occurrence matrix corresponding to the abnormal group;
representing the energy of the gray level co-occurrence matrix corresponding to the normal group;
representing the energy of the gray level co-occurrence matrix corresponding to the anomaly group.
And obtaining the evaluation index of the clustering effect according to the obtained first evaluation index, the second evaluation index, the third evaluation index and the fourth evaluation index of the clustering effect, wherein the evaluation index is in a negative correlation with the first evaluation index, in a positive correlation with the second evaluation index, in a negative correlation with the third evaluation index and in a positive correlation with the fourth evaluation index. The evaluation index is then:
wherein, the first and the second end of the pipe are connected with each other,
an evaluation index indicating a clustering effect;
represents a first evaluation index;
represents a second evaluation index;
represents a third evaluation index;
a fourth evaluation index;
representing the calculation of an exponential function.
In the embodiment of the present invention, the first evaluation index, the second evaluation index, the third evaluation index, and the fourth evaluation index after normalization are all used in the calculation of the evaluation index. By analogy, the evaluation indexes of the clustering effects corresponding to different k values in the k-means clustering algorithm are obtained, when the evaluation index is the maximum, the corresponding clustering effect is the best clustering effect, all the clustering clusters obtained by clustering at the moment comprise the clustering cluster with the most pixel points, and are normal areas of the suspected defective adhesive tape, and the rest areas are stain areas of the suspected defective adhesive tape.
In summary, the embodiments of the present invention provide a method for detecting stains on a non-woven fabric based on optical information, where a difference image is obtained by analyzing and subtracting double-sided tapes of the non-woven fabric under different light sources, the difference image is clustered for multiple times, and a clustering effect at each time is evaluated, where a cluster including the most pixel points in all clusters corresponding to the best clustering effect is a normal area of the double-sided tape of the non-woven fabric, and areas corresponding to other clusters are stain areas of the double-sided tape of the non-woven fabric. And a taint area is obtained based on a more accurate clustering effect, so that the analysis accuracy is improved.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit of the present invention.
Claims (10)
1. A synthetic adhesive non-woven fabric stain detection method based on optical information is characterized by comprising the following steps:
acquiring a first image of a non-woven fabric adhesive tape under a white light source and a second image of the non-woven fabric adhesive tape under an alternative light source, and calculating the difference degree between the first image and the second image, wherein when the difference degree is greater than a preset threshold value, the non-woven fabric adhesive tape is a suspected defect adhesive tape;
adjusting the color of an alternative light source to shoot a plurality of second images of the suspected defect adhesive tape, calculating the difference degree between each second image and the first image, taking the second image with the difference degree larger than a preset threshold value as a contrast image, and subtracting the first image and the contrast image to obtain a difference image;
dividing the difference image into a plurality of sub-areas, clustering all the sub-areas to obtain a plurality of cluster clusters, and obtaining a stain area of the suspected defect adhesive tape according to the optimal clustering effect;
the evaluation method of the clustering effect comprises the following steps:
acquiring a first evaluation index based on an intra-class minimum principle of the cluster, and acquiring a second evaluation index based on an inter-class maximum principle of all clusters;
acquiring a cluster with the most elements in all cluster clusters as a reference cluster, and acquiring a third evaluation index based on the difference between the gray gradient direction and the expected gradient direction of each pixel point in the reference cluster;
dividing all the clustering clusters into a normal group and an abnormal group, and obtaining a fourth evaluation index based on the difference between the corresponding image characteristics of the normal group and the abnormal group;
and obtaining an evaluation index of the clustering effect according to the first evaluation index, the second evaluation index, the third evaluation index and the fourth evaluation index, wherein the best clustering effect is obtained when the evaluation index is the maximum.
2. The method of claim 1, wherein the step of calculating the difference between the first image and the second image comprises:
respectively obtaining the gray level intermediate value of a first image and the gray level intermediate value of a second image, and obtaining the difference degree between the first image and the second image based on the gray level intermediate values.
3. The method as claimed in claim 2, wherein the step of obtaining the difference between the first image and the second image based on the gray level intermediate value comprises:
acquiring an overlapping gray level range of the first image and the second image based on a gray level intermediate value of the first image and a gray level intermediate value of the second image, updating the gray level range of the first image and the gray level range of the second image based on the overlapping gray level range, and acquiring the probability of each gray level in the updated gray level range of the first image and the probability of each gray level in the updated gray level range of the second image; and obtaining the difference degree between the first image and the second image based on the difference of the probabilities between the gray levels of the corresponding positions.
4. The method as claimed in claim 1, wherein the difference between the first image and the contrast image is the difference between the gray level of each pixel in the first image and the gray level of the corresponding pixel in the contrast image.
5. The method as claimed in claim 1, wherein the method of dividing the difference image into a plurality of sub-regions is a region growing algorithm.
6. The method for detecting stains on synthetic adhesive non-woven fabric based on optical information as claimed in claim 1, wherein the method for obtaining the first evaluation index comprises:
acquiring an average gray value and a center coordinate corresponding to each sub-region, and calculating the first evaluation index based on the average gray value and the center coordinate as follows:
wherein, the first and the second end of the pipe are connected with each other,
represents a first evaluation index;
is shown as
Average gray values of all sub-areas in each cluster;
denotes the first
Center coordinates of each cluster;
represents the first in the cluster
Average gray value of the sub-regions;
represents the first in the cluster
The center coordinates of each sub-area;
indicating the number of all sub-regions in the cluster.
7. The method of claim 6, wherein the second evaluation index is calculated by the following formula:
wherein, the first and the second end of the pipe are connected with each other,
represents a second evaluation index;
representing the average gray value of all sub-areas in the nth clustering cluster;
is shown as
The center coordinates of the individual cluster;
representing the average gray value of all the cluster clusters;
representing the center coordinates of all cluster clusters;
indicating the number of all cluster clusters.
8. The method as claimed in claim 1, wherein the step of obtaining a third evaluation index based on the difference between the gradient direction of each pixel point in the reference cluster and the expected gradient direction comprises:
and obtaining the average value of the difference between the gray gradient direction and the expected gradient direction of all the pixel points in the reference cluster as a third evaluation index.
9. The method as claimed in claim 1, wherein the step of obtaining a fourth evaluation index based on the difference between the corresponding image features of the normal group and the abnormal group comprises:
acquiring a gray level co-occurrence matrix corresponding to the normal group and a gray level co-occurrence matrix corresponding to the abnormal group, and calculating a description operator corresponding to each gray level co-occurrence matrix, wherein the description operator is contrast and energy; and obtaining a fourth evaluation index based on the difference between the descriptor corresponding to the normal group and the descriptor corresponding to the abnormal group.
10. The method as claimed in claim 1, wherein the evaluation index is negatively correlated to the first evaluation index, positively correlated to the second evaluation index, negatively correlated to the third evaluation index, and positively correlated to the fourth evaluation index.
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| CN116758071B (en) * | 2023-08-17 | 2023-11-03 | 青岛冠宝林活性炭有限公司 | Intelligent detection method for carbon electrode dirt under visual assistance |
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