CN115631173B - A Composite Thin Film Defect Identification Method - Google Patents

Abstract

The present invention relates to the technical field of defect identification, in particular to a defect identification method for a composite film. The method includes: obtaining a grayscale image of the surface of the composite film, determining the image of the composite film suspected of having a defect according to the gray value; segmenting the image to obtain a suspected Defect area; according to the difference between the gray value of the central pixel in the window of the set size and the gray value of other pixels in the window, the gray value deviation index of the pixel point is obtained; according to the gray value deviation index, the pixel point is divided into three Level, determine the possible defect pixels, use the possible defect pixels to perform region growth to obtain the possible defect area; obtain the end points of the line segment of the principal component direction of the possible defect area, and obtain the non-connectivity according to the distance between the end points corresponding to the possible defect area Indices, the connected area is determined according to the non-connected index and the index threshold, and the defect of the composite film is identified according to the connected area. The present invention can obtain accurate defect identification results.

Description

A Composite Thin Film Defect Identification Method

technical field

The invention relates to the technical field of defect identification, in particular to a defect identification method of a composite thin film.

Background technique

Composite film is a polymer material composed of two or more layers of films of different materials, and is mainly used for packaging. The surface wrinkle of the composite film is a common problem in the processing and application of composite flexible packaging materials. The so-called surface wrinkle problem is that the surface film is raised upwards. The surface wrinkle problem will affect the aesthetics and quality of the composite film packaging. Therefore, It is necessary to identify surface defects of the processed composite film at each production stage, and identify wrinkle defects.

The conventional identification method is to use the threshold segmentation method to process the surface image of the composite film, but because the wrinkle defects on the surface of the composite film are mainly in the shape of a tunnel, the difference in gray level is not obvious, and most of the composite films are white. Due to the presence of light, reflections will affect the selection of the threshold, making the result of image segmentation inaccurate, which in turn leads to inaccurate recognition of composite film defects.

Contents of the invention

In order to solve the above-mentioned technical problems, the object of the present invention is to provide a method for identifying defects in a composite film, and the technical solution adopted is as follows:

Obtaining a surface grayscale image of the composite film, determining a composite film image suspected of having a defect according to the gray value of a pixel in the surface grayscale image; performing threshold segmentation on the composite film image to obtain a suspected defective region;

Use the window of set size to perform sliding window processing on the pixels in the suspected defect area, and obtain the gray value deviation index of the pixel according to the difference between the gray value of the central pixel in the window and the gray value of other pixels in the window; The gray level deviation index divides the pixels into three grades, determines the possible defective pixels according to the number of different grades in the window corresponding to the pixels, and uses the possible defective pixels to perform region growth to obtain the possible defective regions;

Obtain the end points of the line segment of the principal component direction of the possible defect area, obtain the non-connectivity index according to the distance between the end points corresponding to the possible defect area, determine the connected area according to the non-connectivity index and the index threshold, and identify the composite film according to the connected area defect.

Preferably, the determination of the composite film image suspected of having a defect according to the gray value of the pixel in the surface gray image is specifically:

Calculate the color second-order moment of the image according to the grayscale value of the pixel in the surface grayscale image, obtain the color feature value corresponding to the surface grayscale image according to the color second-order moment, and calculate the surface gray with the color feature value greater than the color threshold The high-degree image is recorded as a composite film image suspected of having a defect.

Preferably, performing threshold segmentation on the composite film image to obtain the suspected defect area is specifically:

Construct a grayscale histogram according to the grayscale values of the pixels in the composite film image, obtain the grayscale values corresponding to the two troughs in the grayscale histogram, and record the smaller grayscale value corresponding to the two troughs as the first Segmentation threshold, record the larger gray value corresponding to the two troughs as the second segmentation threshold; the area formed by the pixels whose gray value is smaller than the first segmentation threshold and the pixel points whose gray value is greater than the second segmentation threshold The area is a suspected defect area.

Preferably, the method for obtaining the gray scale deviation index is specifically: ;

in, Indicates the grayscale deviation index corresponding to the zth pixel, Indicates the gray value of the zth pixel, Indicates the gray value of the other first pixel in the window where the zth pixel is located, Indicates the gray value of the other jth pixel in the window where the zth pixel is located, Indicates the total number of other pixels in the window except the center point, and exp() indicates an exponential function with the natural constant e as the base.

Preferably, dividing the pixels into three grades according to the grayscale deviation index is specifically: obtaining the difference between the grayscale deviation indexes of any two pixels, and dividing all the pixels into three grades based on all the differences. grade.

Preferably, the determining possible defective pixels according to the number of different levels in the window corresponding to the pixels is specifically:

For the window where any pixel is located, if there are three levels of pixels among all the pixels in the window, then the arbitrary pixel is a possible defective pixel; if there are no three levels of pixels, it will be The window is expanded according to the set step length. If there are three levels of pixels in all the pixels in the expanded window, then any one of the pixels is a possible defective pixel. If all pixels in the expanded window are If there are no pixels of the three levels among the pixels, any one of the pixels is a noise pixel.

Preferably, the determining the connected region according to the non-connectivity index and the index threshold is specifically:

When the non-connectivity indicators corresponding to the endpoints of the possible defect area are all less than the index threshold, connect the possible defect area with the possible defect area corresponding to the non-connectivity index less than the index threshold; when there is only one end point of the possible defect area corresponding to When the non-connectivity index is less than the index threshold, calculate the shortest distance from the other end point to the edge of the composite film image, if the shortest distance is less than the index threshold, then the possible defect area and the non-connectivity index are less than the index threshold corresponding to the possible defect If the shortest distance is greater than or equal to the index threshold, the possible defect area will be discarded and not connected; when the non-connectivity indicators corresponding to the endpoints of the possible defect area are not less than the index threshold, the possible defect area will be connected. Defect areas are discarded and not connected; all connected areas form a connected area.

Embodiments of the present invention have at least the following beneficial effects:

The present invention firstly determines the composite film image suspected of having defects through the gray value of the pixels in the image, analyzes the image roughly in combination with the color distribution characteristics in the image, and screens out the images with defective parts for subsequent further judgment; and then uses The window processes the composite film image, calculates the gray scale deviation index of the pixel point, and then obtains the possible defective pixel point according to the gray scale deviation index point, and analyzes the gray value difference of the pixel point within the window range, considering the pixel point in the neighborhood The gray level difference of the composite film makes it possible to obtain more accurate defect information; finally, according to the connectivity between the defect regions, the defect regions are connected, and then the defect identification result is obtained, considering the shape characteristics of the defects in the composite film, that is, A certain degree of connectivity further increases the accuracy of the obtained defect area, making the result of defect identification more accurate.

Description of drawings

In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or in the prior art, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments or the prior art. Apparently, the appended The drawings are only some embodiments of the present invention, and those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is a method flow chart of a composite film defect identification method of the present invention.

Detailed ways

In order to further explain the technical means and effects of the present invention to achieve the intended purpose of the invention, the specific implementation, structure, Features and their effects are detailed below. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field of the invention.

The specific scheme of a composite thin film defect identification method provided by the present invention will be described in detail below with reference to the accompanying drawings.

Example

The main purpose of the present invention is to detect the surface defect of the composite film after the composite film is off the machine at each production stage through the image processing technology, and the main detection defect is the wrinkle defect.

The specific scenarios targeted by the present invention are: after the composite film is off the machine, after aging treatment, when the bag-making process is completed and placed for a period of time, when the boiling treatment is completed and placed for a period of time, when the cooking treatment is completed and placed for a period of time, Wrinkle defect detection is carried out at all times.

Please refer to Fig. 1, which shows a method flow chart of a composite film defect identification method provided by an embodiment of the present invention, the method includes the following steps:

Step 1: Acquiring a surface grayscale image of the composite film, determining a composite film image suspected of having a defect according to the gray value of a pixel in the surface grayscale image; performing threshold segmentation on the composite film image to obtain a suspected defect area.

First of all, after the composite film is off the machine, after the aging treatment, when the bag making process is completed and after a period of storage, when the boiling treatment is completed and after a period of storage, when the cooking treatment is completed and after a period of storage, all stages are carried out. Image acquisition, to obtain the surface image of the composite film. Due to the influence of mechanical noise during the acquisition process, the acquired surface image of the composite film was subjected to noise reduction processing. In this embodiment, the Gaussian filtering method is used to denoise the surface image, and then the semantic segmentation method is used to remove the interference of the background, and finally the image obtained after the semantic segmentation process is grayscaled to obtain the surface gray of the composite film. degree image.

It should be noted that, in order to reduce the amount of calculation, it is first necessary to perform image analysis on the images collected at each stage, that is, to pre-judge whether there is a wrinkle defect in the surface grayscale image of the composite film corresponding to the current stage according to the grayscale features in the image , so that subsequent analysis is only performed on the surface grayscale image with wrinkle defects.

When there is a wrinkle defect in the composite film, there will be raised tunnel-shaped wrinkles on the surface of the composite film. The area where the wrinkles exist is shown in the image. The color of the raised part of the folded part is brighter and whiter, but the raised part The depressions on both sides are darker to grayish in color. It can be seen that if there are wrinkle defects on the surface of the composite film, bright white and dark gray will appear in the surface grayscale image of the composite film due to the wrinkle defects.

Based on this, according to the analysis of the grayscale characteristics of the pixels in the surface grayscale image of the composite film, the color distribution range in the surface grayscale image of the composite film is judged. When there is no wrinkle defect in the composite film, the surface grayscale of the composite film The gray values in the images are relatively close, which is represented as a relatively close color value, and when there is a wrinkle defect in the composite film, two colors with different gray values will be added to the surface gray image of the composite film, and the image Expressed as three color values.

Then, since the color moment is a simple and effective color feature marking method, and the second-order moment of color can characterize the distribution range of the color in the image, in this embodiment, the grayscale of the pixel in the surface grayscale image based on the composite film Value computes the color second moment of the image , expressed as: ;

in, Indicates the gray value of the i-th pixel in the surface gray image of the composite film, and N indicates the total number of pixels in the surface gray image. In the formula, the difference between the gray value of each pixel and the average gray value of the image is calculated, which reflects the degree of deviation of each pixel relative to the average gray value of the image, and the deviation degree of all pixels is summed to obtain the average value. Indicates the size of the color distribution range of the surface grayscale image. when The larger the value, the wider the color distribution range of the image, indicating that there may be more color values in the surface grayscale image, and vice versa. The smaller the value, the narrower the color distribution range of the image, indicating that there may be fewer color values in the surface grayscale image, that is, there may be one color value.

In this embodiment, the color second-order moment obtained based on the gray value of the pixel in the surface gray image of the composite film Perform normalization processing, that is, obtain the color feature value of the surface grayscale image according to the second-order moment of the color, expressed as ,in, is the color eigenvalue obtained after normalizing the second moment of the color, is the second-order moment of the color of the image, and e is a natural constant.

when When the value of is larger, The larger the value of , the wider the color distribution range of the image, indicating that there may be more color values in the surface grayscale image, and there may be wrinkle defects on the surface of the composite film corresponding to the image, otherwise when the younger The smaller the value of , that is, the narrower the color distribution range of the image, it means that there may be fewer color values in the surface grayscale image, that is, there may be one color value, and there may be no wrinkles on the surface of the composite film corresponding to the image defect.

Set the color threshold, the value in this embodiment is 0.4, when the value of the color feature value corresponding to the surface grayscale image is greater than the color threshold, that is , it indicates that the color distribution range in the surface grayscale image is large, and there may be wrinkle defects, so the surface grayscale image is recorded as a composite film image suspected of having defects. When the value of the color feature value corresponding to the surface grayscale image is less than or equal to the color threshold, that is When , it means that the color distribution range in the surface grayscale image is narrow, and there may be no wrinkle defects. Therefore, this embodiment does not perform subsequent analysis on the surface grayscale image of the composite film whose color characteristic value is less than or equal to the color threshold.

Finally, in the area where wrinkles exist on the composite film image, the raised parts on the folds appear bright white due to reflection, and the bottom of the mountains on both sides of the raised parts on the folds appear bright white due to the occlusion of the raised parts. A dark gray shade. All the wrinkled areas involved in this embodiment include the raised bright white part and the dark gray shaded part at the bottom of the mountain roots on both sides of the raised part.

Based on this, a grayscale histogram is constructed based on the grayscale values of the pixels in the composite film image suspected to have defects, and the grayscale value corresponding to the two valleys in the grayscale histogram is used as the segmentation threshold to segment the composite film image Get the suspected defect area.

Specifically, the gray value corresponding to the two valleys in the gray histogram is used as the segmentation threshold, the gray value corresponding to the two valleys is smaller as the first segmentation threshold, and the gray value corresponding to the two valleys is compared The larger one is recorded as the second segmentation threshold, and the image is divided into three regions by using the first segmentation threshold and the second segmentation threshold, and the first segmentation threshold is used as the segmentation point between the dark gray shadow part and the main color part of the composite film, so The area composed of pixels whose gray value is less than the first segmentation threshold is recorded as the wrinkled convex area, and the second segmentation threshold is used as the segmentation point between the main color part of the composite film and the bright white convex part, so the gray value is greater than The area formed by the pixels of the second segmentation threshold is recorded as the wrinkled shadow area. Then, the area formed by pixels whose gray value is greater than or equal to the first segmentation threshold and less than or equal to the second segmentation threshold is a normal area, and there is no wrinkle defect in the normal area. Among them, the wrinkled raised area and the wrinkled shadow area constitute the suspected defect area.

Step 2: Use the window of the set size to perform sliding window processing on the pixels in the suspected defect area, and obtain the gray value deviation of the pixel according to the difference between the gray value of the central pixel in the window and the gray value of other pixels in the window Index: Divide the pixels into three grades according to the gray level deviation index, determine the possible defect pixels according to the number of different grades in the window corresponding to the pixels, and use the possible defect pixels to perform region growth to obtain the possible defect area.

First of all, it needs to be explained that the area where the folds exist includes the raised parts on the folds and the shadow parts at the bottom of the mountain root on both sides of the raised folds. The composite film image is processed using a window of a set size. From the composite film image The upper left corner of the window starts to slide from left to right and from top to bottom. The sliding step is 1. When sliding to a position, it is judged which area the pixel belongs to according to the gray value of the pixel at the center of the window. When the center When the position pixel belongs to the possible defect area, the pixel distribution characteristics in the window are analyzed.

When the pixel point at the center belongs to the possible defect area, that is, the pixel point belongs to the wrinkle raised area or the wrinkle shadow area, it means that there is a wrinkle defect in the window, and the raised part and the shadow part of the wrinkle exist simultaneously and Concentrated distribution, and in a window of a certain size, there will be pixels belonging to the wrinkled raised area and the wrinkled shadow area at the same time, which is reflected in the color distribution. The gray value of the pixel in the window will exist in two intervals, That is, if it is less than the first segmentation threshold and greater than the second segmentation threshold, there are bright white pixels and dark gray pixels. After the size of the window is enlarged, the window includes the normal area of the composite film in addition to the defective part, then there must be three color distributions in the window, that is, the gray value of the pixel point in the window will exist in three intervals. At the same time, the pixels of the three colors distributed in the window are not distributed randomly, but are divided into three areas and distributed intensively.

Wherein, in this embodiment, the value of the set size is 3*3, and the implementer can set it according to the actual situation.

Then, use the 3*3 window to process the possible defect area in the composite film image. For any pixel, if there is a wrinkle area in the window with the pixel as the center, then respectively The difference between the gray value of the pixel at the center position and other pixels in the window is calculated, and the pixel deviation of the pixel at the center position can be obtained according to the difference between each difference.

According to the difference between the gray value of the central pixel in the window and the gray value of other pixels in the window, the gray value deviation index of the pixel is obtained, expressed as: ;

in, Indicates the grayscale deviation index corresponding to the zth pixel, Indicates the gray value of the zth pixel, Indicates the gray value of the other first pixel in the window where the zth pixel is located, Indicates the gray value of the other jth pixel in the window where the zth pixel is located, and exp() represents an exponential function based on the natural constant e, Indicates the total number of other pixels in the window except the center point, which is 8 in this embodiment.

Indicates that the difference between the gray value of the zth pixel and the gray value of the first other pixel in the window is used as the reference value to calculate the gray value of the zth pixel and the other gray values in the window The deviation of the difference between the gray values of the jth pixel, and then sequentially calculate the deviation between the reference value and other differences. The difference between the grayscale values of the pixels in the window can reflect the grayscale difference of the pixels, and by selecting any grayscale difference as the reference value, the degree of deviation between the grayscale differences is calculated.

The smaller the gray value difference between pixels, the smaller the corresponding gray difference, indicating that there may be no wrinkle defects in the window where the pixel is located, and the smaller the deviation between the gray differences, the pixel The smaller the value of the gray scale deviation index of the point, the more likely the pixel point belongs to the normal pixel point on the composite film image. The larger the gray value difference between pixels, the larger the corresponding gray difference, indicating that there may be wrinkle defects in the window where the pixel is located, and the greater the deviation between the gray differences, the pixel The larger the value of the gray scale deviation index of the point, the more likely the pixel point belongs to the pixel point in the possible defect area on the composite film image.

Finally, calculate the gray scale deviation indexes of all pixels in the possible defect area on the composite film image according to the above method, obtain the difference between the gray scale deviation indexes of any two pixels, and classify all pixels based on all differences. for three levels.

Specifically, any two pixels are selected as the first pixel and the second pixel, and the difference value of the gray scale deviation index corresponding to the two pixels is calculated. If the value of the difference is greater than the threshold, it is considered that this Two pixels are less likely to belong to pixels in the same area, so the gray scale deviation indicators corresponding to these two pixels are divided into two different levels; if the value of the difference is less than or equal to the threshold, then It is considered that these two pixels may belong to pixels in the same area, so the gray scale deviation indicators corresponding to these two pixels are classified into the same level.

Then, arbitrarily select another pixel to be recorded as the third pixel, calculate the difference between the gray scale deviation index corresponding to the third pixel point and the gray scale deviation index corresponding to the first pixel point, and calculate the gray scale deviation index corresponding to the third pixel point The difference between the gray scale deviation index and the gray scale deviation index corresponding to the second pixel point. If there is a difference that is less than or equal to the threshold value, that is, the difference between the gray scale deviation index corresponding to the third pixel point and the first pixel point is less than or equal to the threshold value, then the gray scale deviation index corresponding to the third pixel point is divided into the gray scale deviation index corresponding to the first pixel point. If the level of the gray scale deviation index corresponding to the pixel is located, or the difference between the gray scale deviation index corresponding to the third pixel point and the second pixel point is less than or equal to the threshold value, then the gray scale deviation index corresponding to the third pixel point is divided into The level of the gray scale deviation index corresponding to the second pixel. If there is no difference less than or equal to the threshold value, that is, the difference between the gray scale deviation indicators of the third pixel point and the other two pixel points is greater than the threshold value, then the gray scale deviation index corresponding to the third pixel point is divided into the first three grades. Wherein, in this embodiment, the value of the threshold is 0.2, and the implementer can set it according to the actual situation.

Based on this, and so on, all pixels are divided into three grades based on all differences. The classification standard is to divide the level with the smallest gray scale deviation index into the first level, and divide the level with the larger gray scale deviation into the first level. In the second level, the level with the largest gray scale deviation is divided into the third level. Because if there is a wrinkle defect in the composite film image, the color distribution of the pixels in the image must be three colors, and the gray scale deviation index corresponding to the pixels in the image must be divided into three levels. At the same time, each pixel corresponds to a grayscale deviation index, each grayscale deviation index corresponds to a level, and each pixel corresponds to a level, and the three levels can be approximately regarded as three different areas, that is, normal areas, Wrinkled raised areas as well as wrinkled shaded areas.

It should be noted that, in this embodiment, by considering the deviation of the gray level difference between the pixel point and the pixel points in its neighborhood, the change of the gray level difference in the image can be analyzed in more detail, and the pixel point is calculated based on the change state of the difference. By making a judgment, more accurate defect information can be obtained.

Further, judge the pixels in the window where the pixels are located. In a window of a certain size, there will be pixels belonging to the wrinkled raised area and the wrinkled shadow area at the same time, which is reflected in the deviation of the gray level difference. The pixel in the window The level corresponding to the point may have two different levels. After the size of the window is enlarged, the window includes the normal area of the composite film in addition to the defect part, and there must be three color distributions in the window, which are manifested in the difference in grayscale. In terms of deviation, there are three different levels of levels corresponding to pixels in the enlarged window.

Therefore, in the window where any pixel is located, if there are three levels of pixels among all the pixels in the window, it means that there may be pixels in three different regions in the window where the pixel is located, then the Any pixel is a possible defective pixel; if there are no three-level pixels, expand the window according to the set step size, and if there are three-level pixels among all the pixels in the expanded window, Then any one of the pixel points is a possible defective pixel point. If there are no three-level pixel points in all the pixel points in the enlarged window, it means that the currently judged pixel point does not belong to the defective part and may only be a noise point. , then any pixel is a noise pixel. Wherein, in this embodiment, the value of the setting step is 2, that is, the size of the enlarged window is 5*5, and the implementer can set the value of the setting step according to the actual situation.

After obtaining possible defective pixels according to the above method, each possible defective pixel is used as an initial seed point for region growth, and the region obtained by region growth is recorded as a possible defect region, and the implementer of the rule for region growth can carry out according to the actual situation Settings, such as growing when the difference between the gray values of pixels is less than the set threshold, the value of the set threshold needs to be set by the implementer according to the actual situation.

Step 3: Obtain the end points of the line segment of the principal component direction of the possible defect area, obtain the non-connectivity index according to the distance between the end points corresponding to the possible defect area, determine the connected area according to the non-connectivity index and the index threshold, and identify the connected area according to Composite Film Defects.

It should be noted that the wrinkle defects existing on the surface of the composite film can also be called tunnel-type defects, because the raised parts on the folds extend in all directions, constantly converging or bifurcating, and then forming a complete wrinkle similar to the tunnel shape, that is, Wrinkles are connected to each other on the composite film, and there is no discrete distribution of wrinkled parts. Therefore, for any possible defect region, the connectivity between the possible defect region and other possible defect regions is analyzed respectively, and the degree of confidence that the possible defect region is a real defect region is judged according to the connectivity.

Specifically, PCA principal component analysis is performed on each possible defect region to obtain the principal component direction of each possible defect region, and the two endpoints of the line segment where the principal component direction of the possible defect region is obtained, then these two endpoints can be regarded as possible The two endpoints of the wrinkle line segment in the defect area.

Since the wrinkle defect presents a tunnel shape in the image, the areas with wrinkle defects are connected to each other. Based on this, by judging the distance between the two end points corresponding to the possible defect area and the end points corresponding to other possible defect areas Distance, and then judge whether the possible defect area is connected with other possible defect areas.

Based on this, the non-connectivity index is obtained according to the distance between the endpoints corresponding to the possible defect area, and the non-connectivity index between the two endpoints corresponding to the possible defect area and the endpoints of other possible defect areas is respectively recorded as the first non-connectivity index. The connectivity index and the second non-connectivity index, that is, the two endpoints corresponding to the possible defect area correspond to a non-connectivity index, expressed as:

;

in, Indicates the first non-connectivity index corresponding to the first endpoint in the possible defect area c, Indicates the second non-connectivity index corresponding to the second end point in the possible defect area c, and min() indicates a function for finding the minimum value.

A Cartesian coordinate system with pixels as the unit is established with the upper left corner of the image as the origin, and the abscissa and ordinate of the pixels are the number of columns and rows in the image respectively. and respectively denote the abscissa and ordinate of the first endpoint in the possible defect region c on the composite film image, and represent the abscissa and ordinate of the first endpoint of the possible defect region c on the image, respectively, and respectively represent the abscissa and ordinate of the kth end point of other possible defect regions on the image, and Respectively represent the abscissa and ordinate of the o-th endpoint of other possible defect regions on the image.

and Both represent the minimum value of the distance between the endpoints in the current possible defect region and the endpoints in other possible defect regions. When the minimum distance between the endpoints corresponding to different possible defect regions is less than the index threshold, it indicates that there is a certain connectivity between different possible defect regions, and they need to be connected. That is, the smaller the minimum value of the distance between the endpoints, the greater the connectivity between the possible defect regions, and the smaller the non-connectivity index corresponding to the possible defect regions.

At the same time, considering that there may be folds on the edge, it is necessary to calculate the shortest distance from the two endpoints corresponding to each possible defect area to the edge of the composite film image, which are respectively recorded as the first non-edge index and the second non-edge index The smaller the shortest distance, the closer the endpoint is to the edge of the image, and the smaller the value of the corresponding non-edge index.

Further, the connected area is determined according to the non-connectivity index and the index threshold. When the non-connectivity index corresponding to the endpoint of the possible defect area is less than the index threshold, that is, the first non-connectivity index and the second non-connectivity index corresponding to the two endpoints of the area The two non-connectivity indexes are both less than the index threshold, and the possible defect area is connected with the possible defect area corresponding to the non-connectivity index less than the index threshold, that is, the area is connected with the possible defect area where the endpoint corresponding to the first non-connectivity index is located, This area is connected with the possible defect area where the endpoint corresponding to the second non-connectivity index is located, and the connected area is a connected area.

When there is only the non-connectivity index corresponding to one endpoint of the possible defect region less than the threshold value, calculate the shortest distance from the other endpoint to the edge of the composite film image, the shortest distance is the non-edge index corresponding to the endpoint, if the non- If the marginality index is less than the index threshold, connect the possible defect area with the possible defect area corresponding to the non-connectivity index less than the threshold, and the connected area is a connected area. If the shortest distance is greater than or equal to the index threshold, then the Possibly defective regions are discarded and not connected.

For example, among the two endpoints of the possible defect area, if the first non-connectivity index corresponding to the first endpoint is less than the index threshold, and the second non-connectivity index corresponding to the second endpoint is greater than the index threshold, then compare the corresponding The size of the second non-marginal index and the index threshold, if greater than or equal to, indicates that the possible defect area may be a discrete area, so it needs to be discarded and not connected, if it is less than, it indicates the possible defect area One side is connected to other possible defect areas, and the other side is close to the edge of the image, so the possible defect area needs to be connected to the possible defect area where the endpoint corresponding to the first non-connectivity index is located.

When the disconnection index corresponding to the endpoints of no possible defect area is less than the index threshold, that is, the first disconnection index and the second disconnection index corresponding to the two endpoints of the area are not less than the index threshold, then it means There is no connectivity between the possible defect region and other possible defect regions, and the possible defect region is a discrete region, so the possible defect region is discarded and not connected.

Wherein, in this embodiment, the value of the index threshold is 4, and the implementer can set it according to the actual situation.

All possible defect areas are judged according to the above method, and the possible defect areas that need to be connected are connected, and all connected areas form a connected area, and the composite film image is segmented according to the connected area, and then the defects of the composite film are identified. In this embodiment, the pixel values of the pixels in the connected area are set to 255, and the pixel values of other pixels are set to 0, and then the composite film image is segmented, and the segmented image is used for defect recognition to obtain the recognition result. Among them, there are various methods for segmenting images and identifying defects, and implementers can choose according to actual conditions.

It should be noted that the present invention first analyzes the possibility that the pixel points are pixels in the wrinkle area, and then grows the possible wrinkle connected domain, and analyzes the possibility that the possible wrinkle connected domain forms a tunnel, thereby identifying the complete wrinkle region, which overcomes the difficulty that threshold segmentation cannot identify wrinkles with subtle grayscale differences.

The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still implement the foregoing embodiments Modifications to the technical solutions recorded in the examples, or equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of each embodiment of the application, and should be included in the scope of the technical solutions of the embodiments of the application. within the scope of protection.

Claims (6)

1. A composite film defect identification method is characterized by comprising the following steps:

acquiring a surface gray image of the composite film, and determining the composite film image suspected of having defects according to gray values of pixel points in the surface gray image; performing threshold segmentation on the composite film image to obtain a suspected defect area;

performing sliding window processing on the pixels in the suspected defect area by using a window with a set size, and obtaining a gray level deviation index of the pixels according to the gray level value of the central pixel in the window and the gray level value difference of other pixels in the window; dividing the pixel points into three grades according to the gray level deviation index, determining possible defect pixel points according to the number of different grades in a window corresponding to the pixel points, and performing region growth by using the possible defect pixel points to obtain a possible defect region;

acquiring end points of a line segment where the principal component direction of the possible defect area is located, obtaining a non-connectivity index according to the distance between the end points corresponding to the possible defect area, determining a connected area according to the non-connectivity index and an index threshold, and identifying the defect of the composite film according to the connected area;

the specific steps of determining the possible defect pixel points according to the number of different levels in the window corresponding to the pixel point are as follows:

for a window where any one pixel point is located, if pixel points of three grades exist in all the pixel points in the window, the any one pixel point is a possible defect pixel point; if the pixel points of the three grades do not exist, the window is enlarged according to the set step length, if the pixel points of the three grades exist in all the pixel points in the enlarged window, any one pixel point is a possible defect pixel point, and if the pixel points of the three grades do not exist in all the pixel points in the enlarged window, any one pixel point is a noise pixel point.

2. The method according to claim 1, wherein the determining the composite film image suspected of having the defect according to the gray-scale values of the pixels in the surface gray-scale image specifically comprises:

and calculating a color second moment of the image according to the gray value of the pixel point in the surface gray image, obtaining a color characteristic value corresponding to the surface gray image according to the color second moment, and marking the surface gray image with the color characteristic value larger than a color threshold value as a composite film image suspected of having defects.

3. The method according to claim 1, wherein the step of performing threshold segmentation on the composite film image to obtain the suspected defect area specifically comprises:

constructing a gray histogram according to gray values of pixel points in the composite film image, acquiring gray values corresponding to two wave troughs in the gray histogram, recording a smaller gray value corresponding to the two wave troughs as a first segmentation threshold, and recording a larger gray value corresponding to the two wave troughs as a second segmentation threshold; and the area formed by the pixel points with the gray value smaller than the first segmentation threshold and the area formed by the pixel points with the gray value larger than the second segmentation threshold are suspected defect areas.

4. The method for identifying the defects of the composite film according to claim 1, wherein the method for obtaining the gray scale deviation index specifically comprises the following steps:

；

wherein,

represents a gray scale deviation indicator corresponding to the z-th pixel point, based on the pixel value in the pixel value bin>

The gray value of the z-th pixel point is represented,

represents the gray value of the other first pixel point in the window where the z-th pixel point is located, and/or the gray value of the other first pixel point is greater than or equal to>

Represents the gray value of the jth pixel point in the window where the jth pixel point is located, and/or the gray value of the other jth pixel point is greater than or equal to>

Representing the total number of other pixels except the center point within the window, exp () represents an exponential function with a natural constant e as the base.

5. The method for identifying defects in a composite film according to claim 1, wherein the dividing of the pixels into three levels according to the gray scale deviation index specifically comprises: and acquiring a difference value between the gray level deviation indexes of any two pixel points, and dividing all the pixel points into three levels based on all the difference values.

6. The method for identifying the defects of the composite film as claimed in claim 1, wherein the determining the connected region according to the non-connectivity index and the index threshold specifically comprises:

when the non-connectivity indexes corresponding to the end points of the possible defect area are all smaller than the index threshold value, connecting the possible defect area with the possible defect area corresponding to the non-connectivity index smaller than the index threshold value; when the non-connectivity index corresponding to only one end point of the possible defect area is smaller than the index threshold, calculating the shortest distance from the other end point to the edge of the composite film image, if the shortest distance is smaller than the index threshold, connecting the possible defect area with the possible defect area corresponding to the non-connectivity index smaller than the index threshold, and if the shortest distance is greater than or equal to the index threshold, removing the possible defect area without connection; when the non-connectivity indexes corresponding to the end points of the possible defect areas are not less than the index threshold, the possible defect areas are discarded and are not connected; all connected regions constitute connected regions.

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