Printed matter printing quality detection and analysis system based on machine vision

Abstract

The invention belongs to the technical field of presswork quality detection, and particularly relates to a presswork printing quality detection and analysis system based on machine vision, which performs sample selection on batch cigarette case presswork, performs imaging test based on the sample, and determines proper imaging parameters according to test results. According to the parameters, apparent image acquisition is carried out on batch cigarette case printed matters, so that the acquisition quality of the apparent images can be improved to the greatest extent in the process, and reliable data support is provided for apparent defect identification; meanwhile, the proper imaging parameters are used for carrying out apparent image collection on batch cigarette case presswork, infrared image collection is synchronously carried out at the same time, by combining apparent defects of apparent image recognition and internal structure defects of infrared image recognition, the system can carry out comprehensive printing quality evaluation, comprehensive defect recognition is achieved, and the printing quality of the cigarette case presswork is improved. According to the method, the accuracy and integrity of defect detection are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of printed matter quality detection, and in particular relates to a printed matter printing quality detection and analysis system based on machine vision. Background Art

[0002] Although the rise of digital media has changed the way information is disseminated, printed materials still occupy an irreplaceable position in modern society, especially in the field of tobacco packaging, where there is a large demand for printed cigarette boxes. In order to ensure the quality and compliance of printed cigarette boxes, strict quality inspection must be carried out after production is completed. Usually, this process relies on visual inspection equipment to perform high-precision image acquisition and analysis to ensure that the product meets strict industry standards and quality requirements.

[0003] In the prior art, a Chinese invention patent with publication number CN101738397A discloses a printed product quality inspection process, which uses a visual inspection device to collect images of printed products, records the location information of the defects when defects are detected, and sends the printed products to a rewinding device for rewinding. The rewinding device compares the stored defect location information with the location information during rewinding, and stops rewinding when the printed product runs to the defect location. The invention realizes the separation of detection and processing, reduces the burden of the visual inspection device, and thus improves the efficiency of printed product quality inspection. However, the scheme ignores the adaptive adjustment of imaging parameters during image acquisition. Since the printed products are affected by factors such as raw materials and inks during the production process, their surface states may vary. If the imaging parameters are not adaptively adjusted, the collected images may be overexposed, underexposed, blurred, reflective, dark spots, etc., resulting in poor image quality, thereby affecting the accurate identification of defects. For mass-produced printed products, such inaccurate image acquisition may lead to a large number of defects being missed or misdetected, seriously affecting the overall quality control of the printed products. In addition, low-quality images may increase the difficulty of subsequent data analysis and reduce the reliability and accuracy of the detection system.

[0004] In addition, the above solution is limited to the detection of surface defects when using visual inspection equipment to collect the surface image of printed products for defect identification. It cannot identify internal structural defects. For cigarette box printed products, the paper is usually thicker, and internal structural defects such as defect thickness and defect depth cannot be identified through surface images. Therefore, relying solely on the collection of printed product surface images cannot achieve comprehensive defect identification, which will have a significant impact on the integrity and accuracy of quality inspection results. Summary of the invention

[0005] The present invention aims to solve the deficiencies in the prior art and provides a printed matter printing quality inspection and analysis system based on machine vision. By collecting visible light and infrared images of mass-produced cigarette boxes and selecting samples for imaging tests for each batch of cigarette box prints during the visible light image collection process, the optimal imaging parameters can be determined, thereby achieving high-quality image collection and comprehensive defect recognition of batch cigarette box prints, effectively making up for the problems existing in the prior art.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A printed product printing quality detection and analysis system based on machine vision, comprising the following modules: a printed product sample selection module, used to select samples of batch printed products produced in the same batch using the same raw materials.

[0007] The imaging test module is used to perform imaging tests on selected printed product samples, thereby determining suitable imaging parameters for batch printed products.

[0008] The defect background feature extraction module is used to collect the apparent image of the printed sample after adjusting the imaging device based on appropriate imaging parameters, and extract the defect background features from the apparent image.

[0009] The image preprocessing test module is used to perform image preprocessing tests based on defect background features in the apparent image of the printed product sample, thereby determining the appropriate image preprocessing sequence after imaging of batch printed products.

[0010] The multi-source image acquisition module is used to acquire the apparent image of the batch of printed products by using the appropriate imaging parameters of the batch of printed products, and to simultaneously acquire the infrared image to obtain the apparent image and infrared image of each printed product.

[0011] The image preprocessing module is used to perform image preprocessing on the apparent images of batch printed products according to a suitable image preprocessing sequence.

[0012] The printing quality inspection module is used to perform surface defect inspection on the surface image of each printed product in the batch after image preprocessing, and to perform internal structural defect inspection on the infrared image of each printed product.

[0013] The printing quality assessment module is used to assess the printing quality based on the detection results of the surface defects and internal structural defects of each printed product in the batch of printed products.

[0014] Combining all the above technical solutions, the present invention has the following positive effects: 1. The present invention selects samples from batches of cigarette box prints, performs imaging tests based on the samples, determines appropriate imaging parameters according to the test results, and then collects surface images of batches of cigarette box prints according to these parameters. This process can maximize the collection quality of surface images, provide reliable data support for surface defect identification, and thus significantly improve the reliability and accuracy of the detection system.

[0015] 2. The present invention adds defect background feature extraction of sample imaging results after performing imaging tests on selected cigarette box print samples, thereby performing image preprocessing tests to determine the optimal image preprocessing sequence. This process can provide an efficient processing solution for apparent image preprocessing of batch cigarette box prints, ensuring that the defect features in the image are more prominent, thereby enhancing the efficiency and accuracy of apparent defect recognition of batch cigarette box prints.

[0016] 3. The present invention uses appropriate imaging parameters to collect surface images of batch cigarette box prints while also collecting infrared images. By combining the surface defects identified by the surface images and the internal structural defects identified by the infrared images, the system can conduct a comprehensive printing quality assessment and achieve comprehensive defect identification. This method not only improves the accuracy and completeness of defect detection, greatly reduces the risk of missed detection and false detection, but also significantly improves the efficiency and reliability of printed product quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0018] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0019] Figure 2 This is a diagram of the operation of the imaging test in the present invention.

[0020] Figure 3 This is a diagram of the batch print quality inspection and evaluation operation in the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 As shown, the present invention provides a printed product printing quality detection and analysis system based on machine vision, including a printed product sample selection module, an imaging test module, a defect background feature extraction module, an image preprocessing test module, a multi-source image acquisition module, an image preprocessing module, a printing quality detection module and a printing quality evaluation module, wherein the printed product sample selection module is connected to the imaging test module, the imaging test module is connected to the defect background feature extraction module, the defect background feature extraction module is connected to the image preprocessing test module, the imaging test module is connected to the multi-source image acquisition module, the multi-source image acquisition module and the image preprocessing test module are both connected to the image preprocessing module, the image preprocessing module is connected to the printing quality detection module, and the printing quality detection module is connected to the printing quality evaluation module.

[0023] The printed product sample selection mold is used for selecting samples of batch printed products produced in the same batch using the same raw materials.

[0024] In the implementation of the above scheme, the specific operation of sample selection is as follows: the total number of batch prints is counted, and each print is numbered, wherein the numbering can be performed according to the production order of the prints.

[0025] The number of samples is determined from the total number according to the predetermined sample selection ratio.

[0026] By way of example, the sample selection ratio may be 10%, and when the number of printed products in a batch is 500, the number of samples determined is 50.

[0027] The numbering intervals between the batch prints are calculated based on the determined number of samples, and the samples are selected according to the intervals.

[0028] In a further example, when the number of samples is 50, the numbering interval is 500 divided by 50, which equals 10. That is, starting from the print numbered 1, one print is selected as a sample every 10 numbers, and the final selected sample numbers are 1, 11, 21, 31...491, totaling 50 prints.

[0029] It should be understood that by selecting samples at numbered intervals from batch prints, the representativeness of the samples can be ensured, deviations can be avoided, and the accuracy and reliability of the test results can be improved. In addition, this method can ensure that the samples come from different locations of the entire production batch, avoiding local deviations caused by selecting samples from only a certain part, which is conducive to ensuring the uniform distribution of samples and covering the entire production batch. It can more comprehensively reflect the overall quality status of batch prints, provide representative imaging samples for subsequent imaging tests, and avoid the problem of repeated tests or over-sampling. This can ensure the authenticity of the imaging test results and reduce test time and resource consumption while ensuring the accuracy of the test results, thereby improving test efficiency.

[0030] See also Figure 2 As shown, the imaging test module is used to perform an imaging test on the selected printed product samples, thereby determining suitable imaging parameters for batch printed products.

[0031] It is important to know that when conducting quality inspection of printed products, it is necessary to use imaging equipment to capture images of the printed product surface, where the imaging equipment is a visible light imaging device, specifically a high-definition camera, and identify defects (such as scratches, stains, etc.) from the captured apparent image. However, since the raw materials used to produce printed products have different properties such as smoothness and color, these properties will have a significant impact on the image acquisition effect. For example, a rougher paper surface may produce more scattered light, resulting in shadows, reflections, or blurring during image acquisition; different colors of paper have different absorption and reflection characteristics for light, light-colored paper reflects more light, which can easily lead to overexposure; while dark-colored paper absorbs more light, which can easily lead to underexposure. Therefore, when capturing the apparent image of a printed product, it is necessary to determine the appropriate imaging parameters for the printed product in the corresponding production batch through imaging tests to ensure the best imaging effect.

[0032] Applied to the above operation, the imaging test is specifically implemented as follows: determining the imaging parameter variables to be tested, thereby forming an imaging test matrix with imaging parameter combinations, and counting the number of imaging test groups.

[0033] In the above operation example, the imaging parameter variables include exposure time, polarization angle, gain, etc. The control range of each imaging parameter variable can be obtained from the user manual according to the specifications of the imaging device, and these control ranges are divided into equal intervals to obtain several test values. For example, the control range of the exposure time is [X1, X2], which is divided into multiple test values ​​at equal intervals; the control range of the polarization angle is [Y1, Y2], which is divided into multiple test values; the control range of the gain is [Z1, Z2], which is divided into multiple test values.

[0034] It is important to understand that exposure time, polarization angle, and gain are selected as imaging parameter variables because these parameters have a significant impact on image quality during image acquisition. Each parameter directly affects the image brightness, contrast, detail clarity, and noise level, which in turn affects the accuracy and reliability of defect detection.

[0035] Specifically, exposure time refers to the length of time the sensor is exposed to light when capturing an image. The longer the exposure time, the more light the sensor receives, and the brighter the image will be. Conversely, the shorter the exposure time, the darker the image will be. Appropriate exposure time can ensure that the image brightness is moderate and avoid overexposure or underexposure.

[0036] Polarization angle refers to the adjustment of the vibration direction of light through a polarizing filter or light source so that it propagates only in a certain direction. Reflections and glare may occur on the surface of printed materials, especially smooth or glossy materials, which interfere with image quality. Using polarized light can effectively reduce these reflections and make the details in the image clearer.

[0037] Gain refers to the sensor's amplification of the signal, which determines the brightness and noise level of the image. The higher the gain, the brighter the image; the lower the gain, the darker the image. Appropriate gain setting can ensure that the image brightness is moderate and avoid being too bright or too dark.

[0038] One test value is selected from several test values ​​of each imaging parameter variable in turn to form an imaging test matrix. For example, assuming that there are 3 test values ​​for exposure time, polarization light, and gain, the imaging test matrix formed is 3×3×3, that is, 27 imaging test groups.

[0039] The selected printed samples are evenly divided into corresponding groups according to the number of imaging test groups. This ensures that there are enough printed samples to present the imaging test results after the tests of each imaging test group, avoiding insufficient representation of the test effect and result deviation caused by a single printed sample.

[0040] When performing the imaging test of each imaging test group, the imaging environment state is controlled to be consistent, and the apparent image of the corresponding printed product sample of each imaging test group is obtained after the test.

[0041] It should be pointed out that when conducting imaging tests, the imaging environment should be kept consistent, including stable control of conditions such as light intensity and ambient temperature. This can avoid interference from external factors and prevent fluctuations in test results and data deviations caused by environmental changes.

[0042] Further applied to the above operation, the appropriate imaging parameters for batch prints are determined as follows: visual effect indicators are extracted from the apparent images of the print samples corresponding to each imaging test group. Exemplarily, the visual effect indicators may include edge intensity, contrast and color depth, thereby performing image visual effect scoring for each print sample.

[0043] It should be pointed out that edge strength indicates the clarity of the boundaries and details in the image. High edge strength means that the outlines of the objects in the image are clear and the details are clear, thereby improving the readability and visual clarity of the image. Contrast refers to the degree of difference between the brightest and darkest parts of the image; a high-contrast image means that the difference between the bright and dark parts of the image is very obvious. Color depth refers to the number of colors that can be displayed in an image, usually expressed in bits (such as 24-bit color). The higher the color depth, the richer the color levels of the image. By using edge strength, contrast and color depth as visual effect indicators, the visual effect of the image can be comprehensively evaluated.

[0044] The visual effect scoring formula of the above image is Wherein p represents the image visual effect score, ES, CR, and CD represent edge strength, contrast, and color depth respectively, ES0, CR0, and CD0 represent the ideal edge strength, ideal contrast, and ideal color depth of the imaging device respectively, and R represents the rating system, which can be one point, ten points, or one hundred points. When R is one point, R=1, and when R is ten points, R=10.

[0045] It should be added that the ideal edge intensity, ideal contrast, and ideal color depth of the imaging device mentioned above can be extracted from the operating instructions of the imaging device and the maximum values ​​that the imaging device can achieve are used as ideal values. From the above-mentioned image visual effect scoring formula, it can be seen that the closer the visual effect indicators of the apparent image of the printed sample are to the ideal values, the greater the image visual effect scoring value.

[0046] The image visual effect score of each printed sample in each imaging test group is compared with the full score to calculate the imaging effect coefficient corresponding to each imaging test group. The specific calculation formula is: Where Q represents the imaging effect coefficient, p i represents the image visual effect score of the i-th printed sample in the imaging test group, i represents the number of the printed sample in the imaging test group, i=1,2,......,n, n represents the number of printed samples in the imaging test group, p0 represents the full score, e represents the natural constant, p i max、p i min represents the maximum and minimum values ​​of the visual effect scores of the printed samples in the imaging test group, respectively, and η allow represents the preset allowed fluctuation of the image visual effect score, for example, η allow=0.3, where p′ represents the visual effect score of the set target image.

[0047] It should be pointed out that when the scoring system is one point, the full score is 1 and the standard score is 0.7; when the scoring system is ten points, the full score is 10 and the standard score is 7; when the scoring system is one hundred points, the full score is 100 and the standard score is 70.

[0048] When evaluating the imaging effect coefficient, it is considered that the image visual effect scores of the printed samples in each imaging test group may fluctuate. When the fluctuation is small, it means that the image visual effects of each printed sample are relatively consistent, and the imaging system performs stably on different samples. At this time, the image visual effect score of each printed sample is compared with the full score, which can effectively evaluate the overall performance of the imaging system. If the scores of all samples are close to the full score, the imaging effect is better. When the fluctuation is large, it means that there are significant differences in the image visual effects of each printed sample, which may be caused by experimental errors. At this time, the image visual effect score of each printed sample is compared with the set qualified score, which can more reasonably evaluate the minimum qualified standard of the imaging system. If the scores of most printed samples can reach the qualified score, it means that the imaging system can meet the basic quality requirements in most cases.

[0049] The imaging effect coefficient corresponding to each imaging test group is compared with the set standard threshold. For example, the standard threshold is 0.8. If the imaging effect coefficient corresponding to a certain imaging test group reaches the standard threshold, the imaging test group is regarded as a valid imaging test group.

[0050] The number of effective imaging test groups is counted. If there is only one effective imaging test group, the imaging parameter combination corresponding to the imaging test group is used as the appropriate imaging parameter for batch printing. If there is more than one effective imaging test group, the imaging parameter combination corresponding to the effective imaging test group to which the maximum imaging effect coefficient belongs is used as the appropriate imaging parameter for batch printing.

[0051] If no imaging test group that meets the threshold value can be screened out, the imaging test groups corresponding to the maximum imaging effect coefficient and the second largest imaging effect coefficient are extracted from the imaging effect coefficients corresponding to each imaging test group and recorded as the first imaging test group and the second imaging test group respectively.

[0052] Imaging parameter refinement is performed between imaging parameter combinations corresponding to the first imaging test group and the second imaging test group to form a new imaging test group.

[0053] It should be added that parameter refinement is to make more detailed adjustments between two imaging test groups with better performance to find the optimal imaging parameter combination. This method is based on the idea of ​​gradient optimization and gradually approaches the optimal parameters, which has a scientific basis. By refining between the imaging parameters corresponding to the maximum and second largest imaging effect coefficients, blind search from scratch can be avoided, which improves the efficiency and accuracy of optimization.

[0054] Imaging tests are continued under the new imaging test groups to obtain the apparent images of the printed samples corresponding to each imaging test group. Similarly, imaging effect coefficient analysis is performed and compared with the set standard threshold, so as to screen out the imaging test groups that meet the standard threshold, so that the imaging parameter combination corresponding to the imaging test group is used as the appropriate imaging parameter for batch prints.

[0055] The present invention determines appropriate imaging parameters based on the imaging test results by first performing an initial screening and then performing parameter refinement. In the initial screening stage, an imaging test matrix is ​​designed to perform a comprehensive parameter test to ensure that all possible imaging parameter combinations are covered. This provides a solid foundation for subsequent optimization. In the subsequent optimization stage, through parameter refinement, attention is focused on parameter combinations with better performance, avoiding excessive exploration of invalid parameters and saving time and resources.

[0056] The defect background feature extraction module is used to collect the apparent image of the printed product sample after adjusting the imaging device based on appropriate imaging parameters, and extract the defect background feature from the apparent image.

[0057] It should be added that after the apparent image of the printed sample is acquired, the image needs to be preprocessed before defect recognition. This is because in order to achieve accurate defect segmentation during defect recognition, the defect area must be effectively separated from the background. To this end, it is necessary to extract the defect background features in the apparent image. These features include noise features, color features, geometric shape features, etc., which significantly affect the extraction and segmentation of defects. Since the transformations and adjustments introduced into the image by each preprocessing step are interdependent, these steps may enhance or weaken certain features and affect the effects of subsequent steps. This makes different image preprocessing step sequences have different effects on defect extraction and segmentation results. Therefore, it is necessary to determine the optimal preprocessing sequence through image preprocessing experiments to ensure the accuracy and reliability of defect recognition.

[0058] The image preprocessing test module is used to perform an image preprocessing test based on defect background features in the apparent image of the printed product sample, thereby determining a suitable image preprocessing sequence after imaging of batch printed products.

[0059] Preferably, the image preprocessing test refers to the following process: determine the required image preprocessing method based on the defect background features extracted from the apparent image of the printed sample. Specifically, when the noise features extracted from the defect background features are noisy, the required image preprocessing method is denoising. When the color features extracted from the defect background features have color differences, the required image preprocessing method is color segmentation, which can help separate the defect area. When the geometric shape features extracted from the defect background features are more complex, the required image preprocessing method is morphological operation, which can help simplify the image structure and highlight the defect features. Common morphological operations include: dilation and erosion, opening and closing operations, skeleton extraction, etc.

[0060] The required image preprocessing methods are sequentially combined to form a number of image preprocessing sequence groups.

[0061] In the example of the above operation, when the required image preprocessing method is denoising, color segmentation and morphological operation, the image preprocessing order groups formed at this time are the first group: first perform denoising, then color segmentation and finally perform morphological operation, the second group: first perform color segmentation, then perform denoising and finally perform morphological operation, the third group: first perform color segmentation, then perform morphological operation and finally perform denoising, the fourth group: first perform color segmentation, then perform morphological operation and finally perform denoising, the fifth group: first perform color segmentation, then perform denoising and finally perform morphological operation.

[0062] The printed sample surface image was preprocessed in each image preprocessing sequence group, and the preprocessing time and defect detection related parameters in each sequence group were recorded.

[0063] The preprocessing time mentioned above refers to the time required to complete a series of preprocessing steps, which directly affects the overall defect detection efficiency. Shorter preprocessing time means that image processing can be completed in a shorter time, improving the efficiency of image quality analysis, especially in the quality inspection of batch prints, time cost is an important consideration.

[0064] The defect detection associated parameters mentioned above may be defect detection rate and false detection rate. The defect detection rate refers to the proportion of successfully detected defects among actually existing defects, and the false detection rate refers to the proportion of all non-defective areas that are mistakenly identified as defective areas.

[0065] Further preferably, the appropriate image preprocessing sequence after batch printing is imaged is determined as follows: defect detection effect scoring is performed based on defect detection associated parameters of the printed sample surface image under each image preprocessing sequence group. Specifically, the defect detection effect scoring formula is: Where λ and δ represent the defect detection rate and false detection rate respectively.

[0066] The preprocessing time and defect detection effect score of the printed sample appearance image in each image preprocessing sequence group are statistically analyzed. The presentation value of the printed sample appearance image in each image preprocessing sequence group is calculated. The specific statistical formula is: Where PV represents the presentation value, t represents the preprocessing time, s represents the defect detection effect score, s0 represents the full score of the defect detection effect, t 总 It represents the cumulative preprocessing time of the apparent image of the printed sample under each image preprocessing sequence group.

[0067] The presentation values ​​of the printed sample's apparent image in each image preprocessing sequence group are compared, and the sequence group with the greatest presentation value is selected as the preferred image preprocessing sequence group for the printed sample's apparent image.

[0068] The present invention can find the best balance between accuracy and efficiency by conducting image preprocessing tests and recording the preprocessing time, defect detection rate and false detection rate under each image preprocessing sequence group. Some preprocessing sequences may increase the defect detection rate, but also increase the false detection rate or preprocessing time; while other sequences may shorten the preprocessing time but reduce the defect detection rate. Therefore, a comprehensive evaluation of these parameters can help determine the optimal preprocessing sequence to ensure that efficiency is maximized while meeting accuracy requirements.

[0069] The preferred image preprocessing sequence groups of the apparent image of each printed sample are compared, and the preferred image preprocessing sequence group with the highest occurrence frequency is selected, and the image preprocessing sequence in this group is used as the appropriate image preprocessing sequence after imaging of batch printed products.

[0070] It should be pointed out that since batch prints are produced using the same raw materials, the defect background features in the apparent images obtained under appropriate imaging parameters are largely similar. Therefore, the preferred image preprocessing sequence group for the apparent image of each print sample should have a certain degree of universality. By comparing the preferred image preprocessing sequence groups of each print sample, the sequence group with the highest frequency of occurrence is selected and used as the standard image preprocessing sequence after batch print imaging, ensuring the consistency and stability of batch prints during imaging and defect detection.

[0071] The multi-source image acquisition module is used to acquire the apparent image of the batch of printed products by using the appropriate imaging parameters of the batch of printed products, and to simultaneously acquire the infrared image to obtain the apparent image and infrared image of each printed product.

[0072] The image preprocessing module is used to perform image preprocessing on the apparent images of batch printed products according to a suitable image preprocessing sequence.

[0073] It should be noted that since the apparent image is collected by visible light imaging equipment, the raw material of the printed matter will significantly affect the visual effect of the image. Therefore, in order to ensure the efficiency and high quality of the apparent image, imaging tests and image preprocessing tests must be carried out to optimize the imaging parameters and preprocessing steps, and improve the accuracy and reliability of defect detection. In contrast, infrared images are collected by infrared imaging equipment, using infrared radiation to penetrate the surface of the printed matter to form an image. Since infrared imaging mainly relies on the thermal radiation characteristics of the object rather than the reflective characteristics of visible light, the raw material of the printed matter has little effect on the visual effect of the infrared image. Therefore, infrared images usually do not require complex imaging tests, but imaging tests can also be selected.

[0074] See also Figure 3 As shown, the printing quality detection module is used to perform surface defect detection on the surface image of each printed product in the batch of printed products after image preprocessing, and to perform internal structural defect detection on the infrared image of each printed product.

[0075] It should be added that before using surface images and infrared images for defect detection, the two types of images need to be registered. This is because surface images and infrared images usually come from different imaging modalities and have different physical properties and imaging mechanisms. These differences may lead to inconsistent spatial positions between the two images, thus affecting subsequent fusion, comparison and analysis.

[0076] The above-mentioned surface inspection is as follows: apparent defects are captured from the apparent image of the printed product, and the location and characteristic information of each apparent defect are marked. The characteristic information includes but is not limited to defect shape, defect color, defect texture, defect size, etc., among which defect shape, defect color, and defect texture can be used to distinguish defect types, such as scratches, stains, and ink diffusion.

[0077] Internal structural defect detection is as follows: structural defects are captured from the infrared image of the printed product, and the location and characteristic information of each structural defect are marked. The characteristic information includes defect thickness, defect depth, etc., where defect thickness refers to the maximum or average thickness of the defect in a certain direction. The greater the thickness, the more serious the defect. The defect depth refers to the depth of the defect inside the material.

[0078] The printing quality assessment module is used to perform printing quality assessment based on the apparent defect and internal structural defect detection results of each printed product in a batch of printed products. The specific printing quality assessment refers to the following process: the positions of the apparent defects and the positions of the structural defects of the printed products are compared to determine whether they are the same defect area, thereby merging the defect areas existing in the printed products.

[0079] In the manner in which the above operation can be implemented, the following process is used to determine whether they are the same defect area: the boundary contours of the apparent defects and structural defects at various locations on the printed product are extracted respectively to calculate their overlap. Specifically, the overlap can be obtained by dividing the area of ​​the overlapped area by the area of ​​the boundary contour of the apparent defect, and then comparing it with a preset overlap threshold. For example, the overlap threshold is 90%. If the overlap between the boundary contour of an apparent defect at a certain location and the boundary contour of a structural defect at a certain location reaches or exceeds the overlap threshold, it is determined that the two defects belong to the same defect area.

[0080] The apparent defect characteristics and structural defect characteristics of each defect area after merging are integrated to evaluate the quality coefficient of each printed product.

[0081] Specifically, when only superficial defects exist in a defective area, the defect type (such as scratches, stains, etc.) and defect size (such as length, area, etc.) in the superficial defect information are used to quantify the defect degree to obtain the defect degree value of the defective area.

[0082] Exemplarily, the quantification of the degree of defects can be achieved by introducing influencing factors of different defect types. The specific steps are as follows: First, an influencing factor is set for each defect type, which reflects the degree of influence of this type of defect on product quality and performance. Then, the size of each defect is measured, its area is calculated, and it is divided by the total surface area of ​​the printed product to obtain the defect ratio. Finally, the influencing factor corresponding to the defect type is multiplied by the defect ratio to obtain the final defect degree value.

[0083] When only internal structural defects exist in a defective area, the defect degree is quantified using the internal structural defect information (such as defect thickness and defect depth) to obtain the defect degree value of the defective area.

[0084] When a defective area has both surface defects and internal structural defects, the two defects are quantified separately, and the defect degree values ​​of the two are added together to obtain the total defect degree value of the defective area.

[0085] Finally, the quality coefficient of each printed product is calculated by accumulating the defect degree values ​​of all defective areas in each printed product.

[0086] The quality coefficient of each printed product is compared with the qualified quality coefficient of the corresponding batch of printed products, wherein the qualified quality coefficient can be extracted from the production documents of the batch of printed products, thereby calculating the quality compliance rate of the batch of printed products. Specifically, the number of printed products that reach the qualified quality coefficient can be calculated divided by the total number of batch printed products.

[0087] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A printed matter printing quality detection and analysis system based on machine vision, characterized in that: Includes the following modules: A printed product sample selection module is used to select samples from batches of printed products produced from the same raw materials and the same batch; An imaging test module is used to perform an imaging test on a selected printed product sample, thereby determining suitable imaging parameters for a batch of printed products; A defect background feature extraction module, used to collect an apparent image of a printed product sample after adjusting an imaging device based on appropriate imaging parameters, and extract defect background features from the apparent image; An image preprocessing test module is used to perform an image preprocessing test based on defect background characteristics in the apparent image of a printed product sample, thereby determining an appropriate image preprocessing sequence after imaging of a batch of printed products; A multi-source image acquisition module is used to acquire the apparent image of the batch of printed products using the appropriate imaging parameters of the batch of printed products, and to simultaneously acquire the infrared image to obtain the apparent image and infrared image of each printed product; An image preprocessing module, used for performing image preprocessing on the apparent images of batch printed products according to an appropriate image preprocessing sequence; The printing quality inspection module is used to inspect the surface defects of each printed product in the batch after image preprocessing, and to inspect the internal structural defects of the infrared image of each printed product; The printing quality assessment module is used to assess the printing quality based on the detection results of the surface defects and internal structural defects of each printed product in the batch of printed products.

2. The printed matter printing quality detection and analysis system based on machine vision as claimed in claim 1, characterized in that: The sample selection process is as follows: Count the total number of printed materials in a batch and number each printed material; Determine the number of samples from the total number according to the predetermined sample selection ratio; The numbering intervals between the batch prints are calculated based on the determined number of samples, and the samples are selected according to the intervals.

3. The printed matter printing quality detection and analysis system based on machine vision as claimed in claim 1, characterized in that: The imaging test on the selected printed product sample refers to the following process: Determine the imaging parameter variables that need to be tested, thereby forming an imaging test matrix with imaging parameter combinations, and count the number of imaging test groups; According to the number of imaging test groups, the selected printed samples were evenly divided into corresponding groups; When performing the imaging test of each imaging test group, the imaging environment state is controlled to be consistent, and the apparent image of the corresponding printed product sample of each imaging test group is obtained after the test.

4. The printed matter printing quality detection and analysis system based on machine vision as claimed in claim 3, characterized in that: Determining suitable imaging parameters for batch prints is implemented as follows: Extracting visual effect indicators from the apparent images of the corresponding printed product samples of each imaging test group, thereby scoring the image visual effect of each printed product sample; The image visual effect score of each printed sample in each imaging test group is compared with the full score to calculate the imaging effect coefficient corresponding to each imaging test group. The specific calculation formula is: Where Q represents the imaging effect coefficient, p i represents the image visual effect score of the i-th printed sample in the imaging test group, i represents the number of the printed sample in the imaging test group, i=1,2,......,n, n represents the number of printed samples in the imaging test group, p0 represents the full score, e represents the natural constant, p i max、p i min represents the maximum and minimum values ​​of the visual effect scores of the printed samples in the imaging test group, respectively, and η allow Indicates the allowed fluctuation of the preset image visual effect score, where p′ represents the visual effect score of the set target image; Compare the imaging effect coefficient corresponding to each imaging test group with the set standard threshold, and if the imaging effect coefficient corresponding to a certain imaging test group reaches the standard threshold, then the imaging test group is regarded as a valid imaging test group; The number of effective imaging test groups is counted. If there is only one effective imaging test group, the imaging parameter combination corresponding to the imaging test group is used as the appropriate imaging parameter for batch printing. If there is more than one effective imaging test group, the imaging parameter combination corresponding to the effective imaging test group to which the maximum imaging effect coefficient belongs is used as the appropriate imaging parameter for batch printing.

5. The printed matter printing quality detection and analysis system based on machine vision as claimed in claim 4, characterized in that: The method of determining suitable imaging parameters for batch prints also includes the following process: If no imaging test group that meets the threshold value can be screened out, the imaging test groups corresponding to the maximum imaging effect coefficient and the second largest imaging effect coefficient are extracted from the imaging effect coefficients corresponding to each imaging test group and recorded as the first imaging test group and the second imaging test group respectively; Refining imaging parameters between imaging parameter combinations corresponding to the first imaging test group and the second imaging test group to form a new imaging test group; Imaging tests are continued under the new imaging test groups to obtain the apparent images of the printed samples corresponding to each imaging test group. Similarly, imaging effect coefficient analysis is performed and compared with the set standard threshold, and then the imaging test group that meets the standard threshold is screened out, so that the imaging parameter combination corresponding to the imaging test group is used as the appropriate imaging parameter for batch prints.

6. The printed matter printing quality detection and analysis system based on machine vision as claimed in claim 1, characterized in that: The image preprocessing test refers to the following process: Determine the required image preprocessing method based on the defect background features extracted from the surface image of the printed sample; Sequentially combining the required image preprocessing methods to form a number of image preprocessing sequence groups; The printed sample surface image was preprocessed in each image preprocessing sequence group, and the preprocessing time and defect detection related parameters in each sequence group were recorded.

7. The system for detecting and analyzing printed matter quality based on machine vision as claimed in claim 6, characterized in that: The process of determining the appropriate image preprocessing sequence after batch printing is imaged is as follows: The defect detection effect is scored based on the defect detection correlation parameters of the printed sample surface image under each image preprocessing sequence group; The preprocessing time and defect detection effect score of the printed sample appearance image in each image preprocessing sequence group are statistically analyzed. The presentation value of the printed sample appearance image in each image preprocessing sequence group is calculated. The specific statistical formula is: Where PV represents the presentation value, t represents the preprocessing time, s represents the defect detection effect score, s0 represents the full score of the defect detection effect, t 总 It represents the cumulative preprocessing time of the apparent image of the printed sample under each image preprocessing sequence group; Compare the presentation values ​​of the printed matter sample appearance image in each image preprocessing sequence group, and select the sequence group with the largest presentation value as the preferred image preprocessing sequence group for the printed matter sample appearance image; The preferred image preprocessing sequence groups of the apparent image of each printed sample are compared, and the preferred image preprocessing sequence group with the highest occurrence frequency is selected, and the image preprocessing sequence in this group is used as the appropriate image preprocessing sequence after imaging of batch printed products.

8. The printed matter printing quality detection and analysis system based on machine vision as claimed in claim 1, characterized in that: The surface defect detection refers to the following process: Capture surface defects from the surface image of the printed product and mark the location and characteristic information of each surface defect; The internal structural defect detection is as follows: Capture structural defects from infrared images of printed products, and mark the location and feature information of each structural defect.

9. The printed matter printing quality detection and analysis system based on machine vision as claimed in claim 8, characterized in that: The printing quality evaluation is as follows: Compare the locations of the apparent defects of the printed product with the locations of the structural defects to determine whether they are the same defect area, thereby merging the defect areas existing in the printed product; The apparent defect characteristics and structural defect characteristics of each defect area after merging are integrated to evaluate the quality coefficient of each printed product; The quality coefficient of each printed product is compared with the qualified quality coefficient of the corresponding batch of printed products, thereby calculating the quality compliance rate of the batch of printed products.

10. The system for detecting and analyzing printed matter quality based on machine vision according to claim 9, characterized in that: The process of determining whether the defective area is the same is as follows: The boundary contours of the apparent defects and structural defects at various locations on the printed product are extracted respectively, and their overlap is calculated and compared with the preset overlap threshold. If the overlap between the boundary contour of an apparent defect and the boundary contour of a structural defect reaches or exceeds the overlap threshold, it is determined that the two defects belong to the same defect area.

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