Image quality monitoring method in thermal insulation aluminum veneer wood grain transfer printing process

Abstract

The invention relates to the technical field of image data processing, in particular to an image quality monitoring method for a thermal insulation aluminum veneer wood grain transfer printing process, which comprises the following steps: acquiring a first image and a second image of a thermal insulation aluminum veneer; acquiring a connected domain of the first image, and determining the thermal deformation degree of the core material of the first image by using the form coefficient and the area of the connected domain; acquiring a detection necessary value based on the thermal deformation degree of the core material corresponding to the continuous frames of first images; under the condition that the detection necessary value is in a preset detection necessary value threshold range, obtaining a texture color difference fuzzy value of the second image; acquiring a wood grain transfer printing quality detection value according to the detection necessary value and the texture color difference fuzzy value of the second image; and adjusting the wood grain transfer printing process of the thermal insulation aluminum veneer by using the wood grain transfer printing quality detection value. The accuracy and efficiency of wood grain transfer quality monitoring of the thermal insulation aluminum veneer can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of image data processing, in particular to an image quality monitoring method for wood grain transfer process of thermal insulation aluminum veneer. BACKGROUND

[0002] Thermal insulation aluminum veneer is a kind of building thermal insulation material, which is often used for building exterior wall. In order to improve the aesthetic appearance of building exterior wall, people often transfer wood grain on thermal insulation aluminum veneer to obtain thermal insulation aluminum veneer with both beauty and practicality.

[0003] The existing problem: at present, the wood grain transfer of thermal insulation aluminum veneer is mainly carried out by heat transfer method. However, due to the existence of thermal insulation core material in thermal insulation aluminum veneer, the core material will be deformed, foamed and shrunk, and gas will be released in the process of heat transfer of thermal insulation aluminum veneer, which will cause the defects of bulging, deformation or bubbles in the thermal insulation aluminum veneer after wood grain transfer, thereby affecting the service life of thermal insulation aluminum veneer. Therefore, it is particularly important to monitor the transfer quality of thermal insulation aluminum veneer during wood grain transfer. At present, the transfer quality of thermal insulation aluminum veneer is mainly monitored by manual method. However, this method depends on the experience of monitoring personnel, and the accuracy and efficiency of monitoring cannot be guaranteed. SUMMARY

[0004] The present application provides an image quality monitoring method for wood grain transfer process of thermal insulation aluminum veneer to solve the existing problem.

[0005] The image quality monitoring method for wood grain transfer process of thermal insulation aluminum veneer provided by the present application adopts the following technical scheme: One embodiment of the present application provides an image quality monitoring method for wood grain transfer process of thermal insulation aluminum veneer, which comprises the following steps: obtaining a first image and a second image of the thermal insulation aluminum veneer; wherein the first image is an image in the wood grain transfer process of the thermal insulation aluminum veneer, and the second image is an image after the wood grain transfer of the thermal insulation aluminum veneer is completed; performing edge detection on the first image to obtain the connected domain of the first image, and determining the core material thermal deformation degree of the first image by using the morphological coefficient of each connected domain and the area of each connected domain; based on the core material thermal deformation degree corresponding to the continuous frame first image, obtaining the detection necessary value corresponding to the continuous frame first image; when the detection necessary value corresponding to the continuous frame first image is within the preset detection necessary value threshold range, obtaining the texture color difference blur value of the second image corresponding to the continuous frame first image; obtaining the wood grain transfer quality detection value of the thermal insulation aluminum veneer according to the detection necessary value corresponding to the continuous frame first image and the texture color difference blur value of the second image corresponding to the continuous frame first image; The wood grain transfer quality detection value of the heat preservation aluminum single board is used to adjust the wood grain transfer process of the heat preservation aluminum single board.

[0006] Further, the first image and the second image of the heat preservation aluminum single board are obtained, and the specific steps include the following: An original image set is obtained, wherein the original image set includes original images in a wood grain transfer process of the heat preservation aluminum single board and original images after the wood grain transfer of the heat preservation aluminum single board is completed; The brightness of each original image is balanced and color correction is performed to obtain a corresponding gray image of each original image; Each gray image is denoised to obtain a denoised image corresponding to each gray image; Each denoised image is edge enhanced to obtain an edge enhanced image corresponding to each denoised image; Surface distortion of each edge enhanced image is extracted to obtain a pre-processed image of each original image, the pre-processed image of the original image in the wood grain transfer process of the heat preservation aluminum single board is taken as the first image of the heat preservation aluminum single board, and the pre-processed image of the original image after the wood grain transfer of the heat preservation aluminum single board is completed is taken as the second image of the heat preservation aluminum single board.

[0007] Further, the first image is edge detected to obtain a connected domain of the first image, and the core material thermal deformation degree of the first image is determined by using a morphological coefficient of each connected domain and an area of each connected domain, and the specific steps include the following: The first image is edge detected to obtain all connected domains of the first image; Based on a gray value of a pixel point in each connected domain, a gray mean value of each connected domain is obtained; By using the area of each connected domain, an edge texture distortion degree of each connected domain is obtained; According to the gray mean value of each connected domain and the edge texture distortion degree of each connected domain, a morphological coefficient of each connected domain is obtained; Based on the morphological coefficient of each connected domain, the area of each connected domain and the number of connected domains, the core material thermal deformation degree of the first image is determined.

[0008] Further, the area of each connected domain is obtained to obtain the edge texture distortion degree of each connected domain, and the specific steps include the following: A minimum circumscribed rectangle of each connected domain is obtained, and a ratio of an area of the minimum circumscribed rectangle to an area of the connected domain is determined as the edge texture distortion degree of each connected domain.

[0009] Further, the gray mean value of each connected domain and the edge texture distortion degree of each connected domain are used to obtain the morphological coefficient of each connected domain, and the specific steps include the following: The ratio of the average gray value of each connected domain to the edge texture distortion degree of each connected domain is determined as the morphological coefficient of each connected domain.

[0010] Further, the specific steps of obtaining the detection necessary value corresponding to the continuous frame first image based on the core material thermal deformation degree corresponding to the continuous frame first image are as follows: The thermal deformation degree sequence is constituted by the core material thermal deformation degree corresponding to the continuous frame first image. The mean value of all elements, the number of elements and the difference value of adjacent elements in the thermal deformation degree sequence are obtained. The detection necessary value corresponding to the continuous frame first image is determined based on the mean value of all elements, the number of elements and the difference value of adjacent elements in the thermal deformation degree sequence.

[0011] Further, the specific steps of obtaining the texture color difference blur value of the second image corresponding to the continuous frame first image in the case that the detection necessary value corresponding to the continuous frame first image is in the preset detection necessary value threshold range are as follows: The detection necessary value corresponding to the continuous frame first image is judged: in the case that the detection necessary value corresponding to the continuous frame first image is in the preset first detection necessary value threshold range, the wood grain transfer process of the thermal insulation aluminum veneer is prewarned. In the case that the detection necessary value corresponding to the continuous frame first image is in the preset second detection necessary value threshold range, the second image corresponding to the continuous frame first image is obtained. The second image is segmented and clustered to obtain all cluster clusters in the second image. The gray entropy of each cluster cluster is obtained based on the gray value of the pixel points in each cluster cluster. The contrast of the second image is obtained, and the texture color difference blur value of the second image corresponding to the continuous frame first image is obtained by using the contrast of the second image, the gray entropy of each cluster cluster and the number of cluster clusters.

[0012] Further, the specific steps of prewarning the wood grain transfer process of the thermal insulation aluminum veneer in the case that the detection necessary value corresponding to the continuous frame first image is in the preset first detection necessary value threshold range are as follows: In the case that the detection necessary value corresponding to the continuous frame first image is greater than or equal to the preset first detection necessary value threshold and less than or equal to the preset second detection necessary value threshold, the wood grain transfer process of the thermal insulation aluminum veneer is prewarned.

[0013] Further, the specific steps of obtaining the second image corresponding to the continuous frame first image in the case that the detection necessary value corresponding to the continuous frame first image is in the preset second detection necessary value threshold range are as follows: In a case that the detection necessary value corresponding to the first image of the continuous frame is greater than a preset second detection necessary value threshold and less than or equal to a preset third detection necessary value threshold, a second image corresponding to the first image of the continuous frame is acquired.

[0014] Further, the wood grain transfer quality detection value of the heat preservation aluminum single plate is used to adjust the wood grain transfer process of the heat preservation aluminum single plate, and the specific steps include the following: In a case that the wood grain transfer quality detection value of the heat preservation aluminum single plate is greater than or equal to a preset quality detection value threshold, the wood grain transfer of the heat preservation aluminum single plate is stopped. In a case that the wood grain transfer quality detection value of the heat preservation aluminum single plate is less than a preset quality detection value threshold, the wood grain transfer process parameters of the heat preservation aluminum single plate are adjusted.

[0015] The beneficial effects of the technical scheme of the present application are that: the image quality monitoring method of the wood grain transfer process of the heat preservation aluminum single plate is proposed in the embodiment of the present application, the image in the wood grain transfer process of the heat preservation aluminum single plate and the image after the wood grain transfer is completed are analyzed, the quality monitoring result of the wood grain transfer of the heat preservation aluminum single plate is obtained, and the wood grain transfer process is adjusted according to the quality monitoring result, compared with the manual quality monitoring mode, the accuracy and efficiency of the wood grain transfer quality monitoring of the heat preservation aluminum single plate can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical scheme in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiment or the prior art description will be briefly introduced below, and obviously, the drawings in the following description can obtain other drawings according to the drawings without creative labor for those skilled in the art.

[0017] Figure 1 The step flow chart of the image quality monitoring method of the wood grain transfer process of the heat preservation aluminum single plate. DETAILED DESCRIPTION

[0018] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the image quality monitoring method of the wood grain transfer process of the heat preservation aluminum single plate according to the present application, its specific implementation, structure, features and effects are described in detail as follows by combining with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, 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 application belongs.

[0020] The application provides a kind of wood grain transfer process image quality monitoring method of heat preservation aluminum veneer.

[0021] Please refer to Figure 1 It shows the step flow chart of a kind of wood grain transfer process image quality monitoring method of heat preservation aluminum veneer provided by the embodiment of the application, and the method comprises the following steps: Step S001: obtaining the first image and the second image of the heat preservation aluminum veneer;Wherein, the first image is the image in the wood grain transfer process of the heat preservation aluminum veneer, and the second image is the image after the wood grain transfer of the heat preservation aluminum veneer is completed.

[0022] The embodiment of the application provides a kind of wood grain transfer process image quality monitoring method of heat preservation aluminum veneer, which is analyzed by the image in the wood grain transfer process of the heat preservation aluminum veneer and the image after the wood grain transfer is completed, obtains the quality monitoring result of the wood grain transfer of the heat preservation aluminum veneer, and adjusts the wood grain transfer process according to the quality monitoring result, compared with artificial quality monitoring mode, the accuracy and efficiency of the wood grain transfer quality monitoring of the heat preservation aluminum veneer can be improved in the embodiment of the application.

[0023] It should be noted that: in this embodiment, images are collected first, and then preprocessed, for subsequent image analysis.

[0024] In the embodiment, an original image set is obtained;The original image set includes the original image in the wood grain transfer process of the heat preservation aluminum veneer and the original image after the wood grain transfer of the heat preservation aluminum veneer is completed. The brightness of each original image is balanced and color corrected to obtain a corresponding gray image. Each gray image is denoised to obtain a corresponding denoised image. Each denoised image is edge enhanced to obtain a corresponding edge enhanced image. The surface distortion of each edge enhanced image is extracted to obtain a preprocessed image of each original image. The preprocessed image of the original image in the wood grain transfer process of the heat preservation aluminum veneer is taken as the first image of the heat preservation aluminum veneer, and the preprocessed image of the original image after the wood grain transfer of the heat preservation aluminum veneer is completed is taken as the second image of the heat preservation aluminum veneer.

[0025] Specifically, an industrial optical camera is installed on the production line of the wood grain transfer of the heat preservation aluminum veneer. For example, the camera is installed above the transfer channel. Then adjust the angle and distance of the camera, so that the camera and the workpiece surface maintain a suitable distance. When collecting images, high-resolution and high-frame-rate cameras are preferred. The original images in the wood grain transfer process and after the wood grain transfer are collected by the set camera.

[0026] The original image is subjected to brightness equalization and color correction to eliminate the influence of light source variation.

[0027] Median filtering or Gaussian filtering is used to remove sensor noise and random interference.

[0028] By The operator highlights the wood grain boundary for subsequent detection. The operator is a known technology and will not be described in detail here.

[0029] It should be noted that under the condition of side light / oblique light, the bulge area will appear abnormal reflection or shadow, and the bulge detection preprocessing is realized through texture distortion analysis. When monitoring the camera, a light source with adjustable angle is provided to form a 15°-45° oblique incidence between the light and the surface of the aluminum single board. The bulge or small surface fluctuation will cause local reflection enhancement or shadow. This difference is manifested as high-contrast light and dark changes in the collected image, thereby strengthening the defect features and facilitating distortion and texture anomaly recognition in the preprocessing link.

[0030] At this point, the preprocessing of the original image during and after the wood grain transfer process is completed.

[0031] Step S002: edge detection is performed on the first image to obtain the connected domain of the first image, and the core material thermal deformation degree of the first image is determined by using the morphological coefficient of each connected domain and the area of each connected domain.

[0032] It should be noted that the wood grain transfer process of the aluminum single board and its results have several quality indicators to be monitored, including the quality of the aluminum single board during the transfer process and the visual quality after the wood grain transfer is completed. In order to ensure the quality of wood grain transfer, heat transfer technology is usually used, however, when dealing with aluminum single board materials, the heat transfer technology will cause the phenomenon of thermal deformation, foaming shrinkage or gas release of polyurethane and polystyrene due to the effect of heat accumulation inside the aluminum single board, which presents a bulge effect visually. When the aluminum single board appears bulge, due to the uneven heating of the bulge area and other areas, the aluminum single board after wood grain transfer has color difference and blurred defects. Therefore, the present embodiment analyzes the multi-light view angle of multiple images to obtain the possible bulge effect and provide early warning information; further, in combination with the actual transfer image content, the specific transfer quality result is obtained, which plays the purpose of quality monitoring.

[0033] By analyzing multiple images, the thermal deformation degree of the corresponding internal material of the aluminum single board is obtained, and then the necessity of subsequent texture quality detection is determined. First, the bulge rate corresponding to the multiple images is analyzed, and then the core material thermal deformation degree is determined according to the bulge rate. The more obvious the bulge is, the higher the degree of core material expansion is, and the greater the influence on the subsequent wood grain transfer texture quality is.

[0034] In this embodiment, edge detection is performed on the first image to obtain all connected domains in the first image. Based on the grayscale values ​​of the pixels within each connected domain, the grayscale mean of each connected domain is obtained. The edge texture distortion of each connected domain is obtained using the area of ​​each connected domain. Based on the grayscale mean and edge texture distortion of each connected domain, the morphology coefficient of each connected domain is obtained. Based on the morphology coefficient of each connected domain, the area of ​​each connected domain, and the number of connected domains, the degree of thermal deformation of the core material in the first image is determined.

[0035] In this embodiment, the minimum bounding rectangle of each connected domain is obtained, and the ratio of the area of ​​the minimum bounding rectangle to the area of ​​the connected domain is determined as the edge texture distortion of each connected domain. The ratio of the grayscale mean of each connected domain to the edge texture distortion of each connected domain is determined as the morphological coefficient of each connected domain.

[0036] Specifically, multiple frames of first images are acquired and processed. The edge detection of the operator is used to obtain the connected domain formed by several edges and the size of the connected domain.

[0037] It should be noted that the morphological characteristics of the bulge are convex or corrugated distortion, which will produce local bright spots under illumination, and appear as texture distortion in the deformed area during image acquisition.

[0038] First, calculate the edge texture distortion of each connected domain , Indicates in Frame image next The edge texture distortion of a connected domain represents the ratio between the minimum circumscribed rectangle area of ​​each connected domain and the area of ​​the connected domain. The higher the ratio, the more regular the shape of the connected domain and the lower the edge texture distortion.

[0039] Then calculate the grayscale mean of each connected region , Indicates in Frame image next The grayscale mean of the connected domains, The higher it is, the more obvious the corresponding bulge shape is, that is, the higher the degree of thermal deformation of the core material is.

[0040] Finally, calculate the morphological coefficient of each connected domain:

[0041] in, Indicates the Frame image next The morphological coefficient of the connected domain is used to analyze the degree of thermal deformation of the core material. In this morphological coefficient, the higher the grayscale mean value, the higher the degree of bright spots of the bulge, and the higher the degree of thermal deformation of the core material caused by heat accumulation inside the thermal insulation aluminum veneer.

[0042] At the same time, the smaller the ratio of the minimum circumscribed rectangle of each connected domain to the connected domain itself, the more irregular the edge distribution of the connected domain is and the higher the possibility of bulging is.

[0043] Determine the degree of thermal deformation of the core material:

[0044] in, Indicates the The degree of thermal deformation of the core material corresponding to the frame image; Indicates the The total number of connected domains in the frame image; as summation coefficient; Indicates the Frame image next The area of ​​a connected domain.

[0045] By analyzing each connected domain in each frame of the image, the corresponding comprehensive evaluation is obtained. Specifically, when the sum of the areas of the connected domains in the frame is The higher it is, the more obvious the number and size of bulges caused by thermal effects in the frame.

[0046] At the same time, the normalized connected domain morphological coefficient As a summation coefficient, it can be seen from the above description that this coefficient also reflects the real-time morphological characteristics of the connected domain, and furthermore, the corresponding degree of thermal deformation of the core material can be determined.

[0047] The more obvious the bulging effect reflected by the summation coefficient is, the higher the coefficient value in the summation is, and the closer it is to 1, the higher the summation result is, and the higher the final degree of thermal deformation of the core material is.

[0048] Similarly, for the number of connected domains , which serves as the coefficient of the summation result. Here, the purpose of determining the total amount of bulges in the image is to analyze the total number of connected domains. The more connected domains there are, the more corresponding bulges there are, and the higher the degree of deformation of the core material caused by heat.

[0049] Step S003: Based on the degree of thermal deformation of the core material corresponding to the first images of the continuous frames, the necessary detection values ​​corresponding to the first images of the continuous frames are obtained.

[0050] In this embodiment, the core material thermal deformation degree corresponding to the first image of the continuous frame is used to form a thermal deformation degree sequence; the mean value of all elements, the number of elements, and the difference value of adjacent elements in the thermal deformation degree sequence are obtained; based on the mean value of all elements, the number of elements, and the difference value of adjacent elements in the thermal deformation degree sequence, the detection necessity value corresponding to the first image of the continuous frame is determined.

[0051] It should be noted that: further, after determining the core material thermal deformation degree of a single frame image, in order to analyze whether the deformation degree is necessary for early warning or the necessity of subsequent wood grain transfer quality detection, it is necessary to continuously monitor the thermal accumulation growth reflected by the determined deformation degree, and the higher the thermal accumulation growth, the higher the degree of heat impact on the aluminum single board at this time.

[0052] Specifically, a plurality of continuous frame side / skew images captured by an industrial camera are obtained, and the corresponding core material thermal deformation degree is obtained to form a thermal deformation degree sequence:

[0053] The physical meaning of this sequence is that by showing the core material thermal deformation degree at different times, the change rule is obtained, and then the heat persistence and the expansion persistence of the internal material of the aluminum single board can be analyzed.

[0054] If the sequence shows a trend of only increasing but not decreasing within a period of time, it means that the heat accumulation effect in the aluminum single board continues to increase, and the necessity of early warning and subsequent transfer texture quality detection is also higher.

[0055] Analysis of detection necessity value:

[0056] Among them, represents the analysis detection necessity value corresponding to the time [0, ] interval after analysis during the wood grain transfer process of the aluminum single board. represents the mean value of the core material thermal deformation degree in the time interval; represents the total amount of time participating in the calculation.

[0057] It should be noted that: each time corresponds to a first image, so according to the core material thermal deformation degree of the continuous frame first image, the detection necessity value corresponding to the continuous frame first image can be obtained.

[0058] represents that the difference value calculation is performed on the core material thermal deformation degree corresponding to the front and rear time frames, and then the sum is obtained to obtain the thermal deformation degree growth. If it continues to grow, the difference value result is always greater than 0, and the sum result is also larger; on the contrary, the difference value result is always less than 0, and the sum result is also smaller.

[0059] The former calculates the average of the core material thermal deformation degree in the interval, which aims to quantify the specific size of the deformation degree. The higher the average is, the higher the core material thermal deformation degree is, and the higher the necessity of pre-warning and subsequent quality detection is.

[0060] Step S004: In the case that the detection necessity value corresponding to the continuous frame first image is in the preset detection necessity value threshold range, the texture color difference blur value of the second image corresponding to the continuous frame first image is obtained.

[0061] In the embodiment, the detection necessity value corresponding to the continuous frame first image is judged: in the case that the detection necessity value corresponding to the continuous frame first image is in the preset first detection necessity value threshold range, the wood grain transfer process of the thermal insulation aluminum veneer is pre-warned; in the case that the detection necessity value corresponding to the continuous frame first image is in the preset second detection necessity value threshold range, the second image corresponding to the continuous frame first image is obtained; the second image is segmented and clustered to obtain all the clustering clusters in the second image; based on the gray values of the pixel points in each clustering cluster, the gray entropy of each clustering cluster is obtained; the contrast of the second image is obtained, and the texture color difference blur value of the second image corresponding to the continuous frame first image is obtained by using the contrast of the second image, the gray entropy of each clustering cluster, and the number of clustering clusters.

[0062] It should be noted that: the preset first detection necessity value threshold range and the preset second detection necessity value threshold range are set according to specific conditions, which are not limited here.

[0063] In the embodiment, in the case that the detection necessity value corresponding to the continuous frame first image is greater than or equal to the preset first detection necessity value threshold and less than or equal to the preset second detection necessity value threshold, the wood grain transfer process of the thermal insulation aluminum veneer is pre-warned. In the case that the detection necessity value corresponding to the continuous frame first image is greater than the preset second detection necessity value threshold and less than or equal to the preset third detection necessity value threshold, the second image corresponding to the continuous frame first image is obtained.

[0064] It should be noted that: the preset first detection necessity value threshold, the preset second detection necessity value threshold and the preset third detection necessity value threshold are set according to specific conditions, which are not limited here.

[0065] Specifically, when the detection necessity value is in the range [0.4, 0.6], it is considered that there may be a certain degree of slight bulging, but it does not affect the wood grain transfer quality, and pre-warning processing is performed at this time; further, when the detection necessity value is in the range (0.6, 1], it is considered that the bulging effect caused by the internal heat accumulation effect of the thermal insulation aluminum veneer will affect the transfer texture quality.

[0066] Furthermore, obtaining the thermal insulation aluminum veneer and its transfer image that need to be subjected to transfer texture detection is to analyze the second image.

[0067] It's important to note that uneven heating of the bulged area can lead to either insufficient or excessive transfer temperatures. This visually manifests as an abnormal distribution of wood grain color relative to the surrounding area, appearing darker (due to high temperature) or lighter (due to low temperature). This characteristic manifests as color difference in the transfer result. Furthermore, texture boundaries become blurred, creating a "fuzzy" pattern. Therefore, if the visual characteristic of color fuzziness is present in a wood grain transfer image requiring inspection, the image quality monitoring results indicate low wood grain transfer quality.

[0068] After the wood grain transfer is completed The texture color difference fuzziness corresponding to the frame image. The color difference of wood grain transfer is visually manifested as the grayscale difference of clusters in a certain area of ​​the image. Therefore, the image is first segmented and clustered using the superpixel segmentation algorithm to obtain several clusters and obtain the grayscale entropy within the clusters. .

[0069] It should be noted that the superpixel segmentation algorithm and the calculation of grayscale entropy within clusters are well-known technologies and will not be described in detail here. Grayscale entropy within clusters indicates the degree of grayscale dispersion within a cluster. The more dispersed, the more jumpy the color distribution, and the more likely it is to cause transfer color aberration and blurring.

[0070] Then get the The contrast within the second image of the frame. The larger the value, the more obvious the color difference after the wood grain transfer.

[0071] Then, we can get:

[0072] in, Indicates the Texture color difference blur value of the second image of the frame; Indicates the the contrast of the second image of the frame; Indicates the The total number of clusters in the second image of the frame; Indicates the Frame 2 image next The grayscale entropy of each cluster. Within a frame, higher contrast and larger color span indicate a more monotonous transfer result when transferring wood grain onto thermal insulation aluminum veneer. Conversely, a larger color span indicates color aberration caused by uneven transfer temperature. Furthermore, the greater the grayscale entropy within each cluster, the greater the corresponding blurriness and color aberration.

[0073] It should be noted that when it is determined that the detection necessity value corresponding to the first image of the continuous frame is large, the second image corresponding to the first image of the continuous frame at this time is obtained, and the second image at this time and the first image of the continuous frame have a corresponding relationship. The first image is an image in the wood grain transfer process, and the second image is the corresponding image after the image completes the transfer.

[0074] Step S005: According to the detection necessity value corresponding to the first image of the continuous frame and the texture color difference blur value of the second image corresponding to the first image of the continuous frame, the wood grain transfer quality detection value of the thermal insulation aluminum veneer is obtained.

[0075] The wood grain transfer quality detection value is obtained in combination with the detection necessity value:

[0076] The wood grain transfer quality detection value is represented, and the higher the value is, the worse the corresponding wood grain transfer effect is, and the more the core material abnormality needs to be alarmed and reminded.

[0077] Step S006: The wood grain transfer process of the thermal insulation aluminum veneer is adjusted by using the wood grain transfer quality detection value of the thermal insulation aluminum veneer.

[0078] In the embodiment, in the case that the wood grain transfer quality detection value of the thermal insulation aluminum veneer is greater than or equal to a preset quality detection value threshold, the wood grain transfer of the thermal insulation aluminum veneer is stopped; in the case that the wood grain transfer quality detection value of the thermal insulation aluminum veneer is less than the preset quality detection value threshold, the wood grain transfer process parameters of the thermal insulation aluminum veneer are adjusted.

[0079] It should be noted that the preset quality detection value threshold is set according to specific conditions, which is not limited here.

[0080] Specifically, when the quality detection value is greater than or equal to 60%, the wood grain texture color difference blur caused by the bulging effect is high, the alarm level is high, and the wood grain transfer is stopped; when the quality detection value is less than 60%, the alarm level is low, the wood grain transfer is temporarily suspended, and the process parameters are adjusted to reduce the wood grain transfer blur defects caused by the heat accumulation effect.

[0081] Thus far, the present application is completed.

[0082] In summary, in the embodiment of the present application, an image quality monitoring method for the wood grain transfer process of the thermal insulation aluminum veneer is proposed, which analyzes the image in the wood grain transfer process of the thermal insulation aluminum veneer and the image after the wood grain transfer is completed, obtains the quality monitoring result of the wood grain transfer of the thermal insulation aluminum veneer, and adjusts the wood grain transfer process according to the quality monitoring result. Compared with the manual quality monitoring method, the embodiment of the present application can improve the accuracy and efficiency of the wood grain transfer quality monitoring of the thermal insulation aluminum veneer.

[0083] ​The above merely provides the preferred embodiment of the present application, and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for monitoring image quality during wood grain transfer of thermal insulation aluminum veneer, characterized in that: The method comprises the following steps: Acquire a first image and a second image of the thermal insulation aluminum veneer; wherein the first image is an image of the thermal insulation aluminum veneer during the wood grain transfer process, and the second image is an image of the thermal insulation aluminum veneer after the wood grain transfer is completed; Performing edge detection on the first image to obtain connected domains of the first image, and determining the degree of thermal deformation of the core material in the first image using a morphological coefficient and an area of ​​each connected domain; Based on the thermal deformation degree of the core material corresponding to the first image of the continuous frame, obtaining the detection necessary value corresponding to the first image of the continuous frame; When the detection necessary value corresponding to the first image of the continuous frame is within a preset detection necessary value threshold range, obtaining a texture color difference blur value of the second image corresponding to the first image of the continuous frame; Obtaining a wood grain transfer quality detection value of the thermal insulation aluminum veneer according to a detection necessary value corresponding to the first image of the continuous frame and a texture color difference fuzzy value of the second image corresponding to the first image of the continuous frame; The wood grain transfer process of the thermal insulation aluminum veneer is adjusted using the wood grain transfer quality test value of the thermal insulation aluminum veneer.

2. The image quality monitoring method for the wood grain transfer process of the thermal insulation aluminum veneer according to claim 1 is characterized in that: The specific steps of obtaining the first image and the second image of the thermal insulation aluminum veneer are as follows: Acquire an original image set; wherein the original image set includes original images during the wood grain transfer process of the thermal insulation aluminum veneer and original images after the wood grain transfer of the thermal insulation aluminum veneer is completed; Perform brightness equalization and color correction on each original image to obtain a grayscale image corresponding to each original image; Perform denoising on each grayscale image to obtain a denoised image corresponding to each grayscale image; Perform edge enhancement on each denoised image to obtain an edge enhanced image corresponding to each denoised image; Surface distortion extraction is performed on each edge-enhanced image to obtain a pre-processed image of each original image. The pre-processed image of the original image during the wood grain transfer process of the thermal insulation aluminum veneer is used as the first image of the thermal insulation aluminum veneer, and the pre-processed image of the original image after the wood grain transfer of the thermal insulation aluminum veneer is completed is used as the second image of the thermal insulation aluminum veneer.

3. The image quality monitoring method for the wood grain transfer process of the thermal insulation aluminum veneer according to claim 2 is characterized in that: The method of performing edge detection on the first image, obtaining connected domains of the first image, and determining the degree of thermal deformation of the core material of the first image by using the morphological coefficient and the area of ​​each connected domain includes the following specific steps: Performing edge detection on the first image to obtain all connected regions of the first image; Based on the grayscale values ​​of the pixels in each connected domain, the grayscale mean of each connected domain is obtained; Using the area of ​​each connected domain, the edge texture distortion of each connected domain is obtained; According to the grayscale mean value of each connected domain and the edge texture distortion of each connected domain, the morphological coefficient of each connected domain is obtained; The degree of thermal deformation of the core material of the first image is determined based on the shape coefficient of each connected domain, the area of ​​each connected domain, and the number of connected domains.

4. The image quality monitoring method for the wood grain transfer process of the thermal insulation aluminum veneer according to claim 3 is characterized in that: The method of obtaining the edge texture distortion of each connected domain by using the area of ​​each connected domain includes the following specific steps: The minimum bounding rectangle of each connected domain is obtained, and the ratio of the area of ​​the minimum bounding rectangle to the area of ​​the connected domain is determined as the edge texture distortion of each connected domain.

5. The image quality monitoring method for the wood grain transfer process of the thermal insulation aluminum veneer according to claim 3 is characterized in that: The specific steps of obtaining the morphological coefficient of each connected domain according to the grayscale mean value of each connected domain and the edge texture distortion of each connected domain are as follows: The ratio of the grayscale mean of each connected domain to the edge texture distortion of each connected domain is determined as the morphological coefficient of each connected domain.

6. The method for monitoring image quality during wood grain transfer of thermal insulation aluminum veneer according to claim 5, characterized in that: The method of obtaining the necessary detection value corresponding to the first image of the continuous frames based on the thermal deformation degree of the core material corresponding to the first image of the continuous frames includes the following specific steps: The thermal deformation degree sequence of the core material corresponding to the first image of the continuous frames is formed; Obtain the mean value of all elements, the number of elements, and the difference between adjacent elements in the thermal deformation degree sequence; Based on the mean value of all elements, the number of elements, and the difference between adjacent elements in the thermal deformation degree sequence, the necessary detection value corresponding to the first image of the continuous frame is determined.

7. The image quality monitoring method for the wood grain transfer process of the thermal insulation aluminum veneer according to claim 6, characterized in that: The step of obtaining the texture color difference blur value of the second image corresponding to the first image of the continuous frame when the detection necessary value corresponding to the first image of the continuous frame is within a preset detection necessary value threshold range includes the following specific steps: Determining the necessary detection values ​​corresponding to the first images of the continuous frames: when the necessary detection values ​​corresponding to the first images of the continuous frames are within a preset first necessary detection value threshold range, issuing an early warning for the wood grain transfer process of the thermal insulation aluminum veneer; When the detection necessary value corresponding to the first image of the continuous frame is within a preset second detection necessary value threshold range, acquiring a second image corresponding to the first image of the continuous frame; Performing segmentation and clustering processing on the second image to obtain all clusters in the second image; Based on the grayscale values ​​of the pixels in each cluster, the grayscale entropy of each cluster is obtained; The contrast of the second image is obtained, and the texture color difference fuzzy value of the second image corresponding to the continuous frame first image is obtained using the contrast of the second image, the grayscale entropy of each cluster, and the number of clusters.

8. The method for monitoring image quality during wood grain transfer of thermal insulation aluminum veneer according to claim 7, characterized in that: When the necessary detection value corresponding to the first image of the continuous frames is within the preset first necessary detection value threshold range, the specific steps of issuing an early warning for the wood grain transfer process of the thermal insulation aluminum veneer are as follows: When the detection necessary value corresponding to the first image of the continuous frame is greater than or equal to the preset first detection necessary value threshold and less than or equal to the preset second detection necessary value threshold, an early warning is issued for the wood grain transfer process of the thermal insulation aluminum veneer.

9. The method for monitoring image quality during wood grain transfer of thermal insulation aluminum veneer according to claim 7, characterized in that: The method of acquiring the second image corresponding to the first image of the continuous frames when the detection necessary value corresponding to the first image of the continuous frames is within a preset second detection necessary value threshold range includes the following specific steps: When the detection necessary value corresponding to the first image of the continuous frame is greater than the preset second detection necessary value threshold and less than or equal to the preset third detection necessary value threshold, the second image corresponding to the first image of the continuous frame is acquired.

10. The method for monitoring image quality during wood grain transfer of thermal insulation aluminum veneer according to claim 1, characterized in that: The wood grain transfer quality test value of the thermal insulation aluminum veneer is used to adjust the wood grain transfer process of the thermal insulation aluminum veneer, and the specific steps include the following: When the wood grain transfer quality detection value of the thermal insulation aluminum veneer is greater than or equal to the preset quality detection value threshold, stopping the wood grain transfer of the thermal insulation aluminum veneer; When the wood grain transfer quality detection value of the thermal insulation aluminum veneer is less than a preset quality detection value threshold, the wood grain transfer process parameters of the thermal insulation aluminum veneer are adjusted.

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