An online detection system and method with the function of the first proof of printed matter

Abstract

The present application relates to an online detection system and method with the function of the first proof of printed matter, aiming to improve the first inspection efficiency and accuracy of printing enterprises. The method includes: collecting the image of the first printed matter, and simultaneously performing online modeling and online proofing according to the image. The image of the first product is captured in real time by a high-resolution image acquisition device, and image processing technology is used to accurately calibrate and register the plate electronic file and the image of the first printed matter to ensure the geometric consistency between the two. Subsequently, the system compares the structural elements one by one, finds out the difference points and conducts screening and evaluation to generate a detailed online proofing report. If the result is qualified, continuous printing production is carried out based on the created online model; otherwise, the problems are checked and the calibration, registration and comparison operations are repeated until qualified. The present application significantly shortens the first inspection time, reduces manual intervention, improves the detection accuracy and reliability, reduces the scrap rate, and enhances customer satisfaction and market competitiveness.

Description

Technical Field

[0001] This application relates to the technical field of printing quality inspection, and particularly to an online detection system and method with the function of proofreading the first printed sheet of printed matter. Background Art

[0002] Before mass printing, printing enterprises usually need to proofread and inspect the first printed product (i.e., first inspection) to ensure the correctness of the content of the printed layout file and avoid batch scrapping caused by layout errors or information loss. In the prior art, the first inspection is mainly carried out by using offline detection equipment or manual proofreading methods. These methods have the following problems: The traditional manual first inspection method requires multiple people to cooperate, the process is complex, time-consuming, and is easily affected by subjective judgment, and there is a risk of missed inspection or misjudgment. The offline detection method requires sending the first printed product to the detection equipment to complete image acquisition and detection. The process involves multiple positions and operation steps, which not only increases the time cost, but also may increase the error probability due to multiple handling and operations. In addition, the waiting time for the offline detection results often makes the printing press idle, reducing production efficiency. Summary of the Invention

[0003] In view of one or more of the problems existing in the prior art, the first aspect of this application provides an online detection method with the function of proofreading the first printed sheet of printed matter, including:

[0004] Collecting an image of the first printed product while printing the first printed product;

[0005] Performing online modeling based on the image of the first printed product;

[0006] Performing online proofreading based on the image of the first printed product;

[0007] When the online proofreading result is qualified, taking the online modeling result as the detection standard to carry out continuous printing production;

[0008] Among them, online modeling based on the image of the first printed product and online proofreading based on the image of the first printed product are carried out simultaneously;

[0009] Performing online proofreading based on the image of the first printed product includes:

[0010] Preprocessing the electronic file of the printing plate to obtain an electronic file comparison image with a set resolution;

[0011] Calibrating the image of the first printed product according to the electronic file comparison image;

[0012] Registering the electronic file comparison image and the calibrated image of the first printed product;

[0013] Comparing and evaluating the structure of the registered image of the first printed product with the electronic file comparison image, and outputting the online proofreading result.

[0014] Preferably, preprocessing the plate electronic file to obtain an electronic file reference image with a set resolution includes:

[0015] Converting the plate electronic file into a first image with a set resolution;

[0016] Segmenting the pattern and background of the first image and performing blurring processing;

[0017] Extracting and merging the pattern texture of the first image according to the three color channels of RGB, and generating a texture mask image through a threshold to obtain an electronic file reference image with a set resolution.

[0018] Preferably, calibrating the first printed matter image according to the electronic file reference image includes:

[0019] Adjusting the resolution of the first printed matter image to be consistent with the resolution of the electronic file reference image with a set resolution;

[0020] Performing geometric correction on the first printed matter image to restore the local deformation of the first printed matter image;

[0021] Performing color correction on the first printed matter image to further reduce the difference between the first printed matter image and the electronic file reference image;

[0022] Performing filtering processing on the first printed matter image to make the blurring degree of the first printed matter image close to that of the electronic file reference image.

[0023] Preferably, the geometric correction of the first printed matter image specifically includes:

[0024] Obtaining multiple rough corner points on the first printed matter image through a corner point detection algorithm; screening out fine corner points from the rough corner points by using a clustering algorithm; taking the fine corner points as a reference, comparing the similarity in the corresponding area with the electronic file reference image; adjusting the position, rotation and scaling of the first printed matter image according to the comparison result to correct the local deformation.

[0025] Preferably, registering the electronic file reference image and the calibrated first printed matter image includes:

[0026] Detecting and extracting significant feature points in the electronic file reference image;

[0027] Selecting positioning kernels from the feature points;

[0028] Determining the best matching points corresponding to each positioning kernel in the first printed matter image to establish a position correspondence relationship;

[0029] Based on the matching of a large number of positioning kernels, map multiple regions between the electronic file of the printing plate and the image of the first printed product;

[0030] Perform weighted processing on the local mapping relationship to obtain the accurate mapping relationship at each position between the images.

[0031] Preferably, compare and evaluate the structure of the registered image of the first printed product with the control image of the electronic file, and the output online proofing result includes:

[0032] Based on the registered position correspondence, compare the structure elements of the image of the first printed product with the control image of the electronic file one by one to find the difference points;

[0033] According to the preset evaluation method, screen the difference points to generate the online proofing result.

[0034] Preferably, after comparing and evaluating the structure of the registered image of the first printed product with the control image of the electronic file and outputting the online proofing result, it further includes:

[0035] The online proofing result is shown through the human-computer interaction module, and the operator reviews the online proofing result. If the online proofing result is unqualified, corresponding measures need to be taken for correction and then repeat the calibration, registration, and comparison operations until it is qualified.

[0036] The second aspect of the present application provides an online detection system with the function of proofing the first printed product of a printed product, which is used for online detection of the printing production controlled by a printing control module, including:

[0037] An image acquisition module, which is used to acquire the image of the first printed product;

[0038] An online modeling module, connected to the image acquisition module, which is used to perform online modeling according to the image of the first printed product;

[0039] The first proofing module, connected to the image acquisition module and the printing control module, which is used to perform online proofing according to the image of the first printed product;

[0040] A human-computer interaction module, connected to the first proofing module and the printing control module, which is used to display the online proofing result and send a control instruction to the printing device according to the online proofing result;

[0041] An online detection module, connected to the online modeling module, which is used to detect the printed products of continuous printing production with the online modeling result as the detection standard.

[0042] Preferably, the first proofing module includes:

[0043] A calibration unit, connected to the image acquisition module and the printing control module, is configured to receive the first printed product image and the plate electronic file, preprocess the plate electronic file to obtain an electronic file comparison image with a set resolution, and calibrate the first printed product image according to the electronic file comparison image;

[0044] A registration unit, connected to the calibration unit, is configured to combine the calibrated first printed product image, and register the electronic file comparison image and the calibrated first printed product image;

[0045] An evaluation unit, respectively connected to the registration unit and the human-computer interaction module, is configured to compare and evaluate the structure of the registered first printed product image with the electronic file comparison image, and output the online proofing result to the human-computer interaction module.

[0046] One or more of the above embodiments have at least the following beneficial effects:

[0047] First, the first inspection time is significantly shortened. The traditional manual first inspection or off-line detection method usually takes more than half an hour to complete the proofing inspection of the first product. However, through parallel processing of the modeling and proofing tasks, combined with high-resolution image acquisition and real-time data analysis, the present invention can complete the entire process within 5 minutes. This fast response ability not only improves work efficiency but also reduces production stagnation caused by waiting for the first inspection result.

[0048] Second, the detection accuracy and reliability are improved. The online detection system uses image processing algorithms (such as SIFT, SURF, etc.) for feature point extraction, registration, and evaluation, ensuring the consistency and accuracy between the first printed product image and the plate electronic file. Through an accurate registration and differential point screening mechanism, the system can effectively identify subtle defects, avoiding omissions and misjudgments that may occur in manual detection, thus ensuring the quality consistency of each printed product.

[0049] Furthermore, the labor cost and operation complexity are reduced. The system realizes the full process automation from image acquisition to the output of the final proofing result, reducing the need for multi-position and multi-person collaboration. The operator only needs to perform simple instruction input and review confirmation on the human-computer interaction module, greatly simplifying the work process, reducing the uncertainty brought by human factors, and at the same time improving the safety and convenience of operation. Description of the Drawings

[0050] The drawings are used to provide a further understanding of the present application, and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application, and do not constitute a limitation to the present application. In the drawings:

[0051] Figure 1It is a flowchart of an online detection method with the function of the first proof of printed matter provided by an exemplary embodiment of the present application;

[0052] Figure 2 It is a schematic diagram of the fine corner point clustering result during geometric correction in calibration in the online detection method with the function of the first proof of printed matter provided by an exemplary embodiment of the present application;

[0053] Figure 3 It is a schematic structural diagram of an online detection system with the function of the first proof of printed matter provided by an exemplary embodiment of the present application;

[0054] Figure 4 It is a schematic structural diagram of the first proof module of the online detection system with the function of the first proof of printed matter provided by an exemplary embodiment of the present application.

[0055] Reference numerals:

[0056] 100, printing control module; 200, image acquisition module; 300, online modeling module; 400, first proof module; 410, calibration unit; 420, registration unit; 430, evaluation unit; 500, human-computer interaction module; 600, online monitoring module. Detailed implementation manners

[0057] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application claimed, but merely represents the selected embodiments of the present application.

[0058] Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0059] In the description of the present application, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation of the present application. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0060] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", and "coupling" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0061] Next, the technical solutions of the present application will be described clearly and completely in conjunction with Figures 1 to 4 Obviously, the described embodiments are some embodiments of the present application, rather than all embodiments.

[0062] Figure 1 It is a flowchart of an online detection method with the function of the first proof of a printed matter provided by an exemplary embodiment of the present application.

[0063] Referring to Figure 1 a first aspect of the present application provides an online detection method with the function of the first proof of a printed matter, including the following steps:

[0064] S100. While printing the first printed matter, collect an image of the first printed matter.

[0065] While the printing device prints the first product according to the plate electronic file (such as PDF file, electronic design drawing, etc.), the image acquisition module immediately captures the image of the sample, ensuring that the quality of the image is sufficient for subsequent processing and analysis. This step is the basis for realizing the online first proof and provides the original data for subsequent modeling and proofing.

[0066] The image acquisition module should have high resolution and fast response capabilities, and be able to obtain clear image data without affecting the printing speed.

[0067] In some specific examples, the plate electronic file can be transmitted to the printing control module of the printing device through the enterprise internal network (LAN) or dedicated file transfer protocols (such as FTP, HTTP, SMB) for printing operations.

[0068] In some specific examples, the image acquisition module can adopt high-performance cameras, such as industrial high-speed imaging devices, high-definition cameras with higher resolution, etc., which support multiple image formats (such as JPEG, TIFF), ensuring that a high-quality image of the first product is captured. The high-resolution image provides a reliable basis for subsequent comparison and reduces the possibility of misjudgment caused by poor image quality.

[0069] In some specific examples, there is a synchronous trigger mechanism between the image acquisition module and the printing device, that is, through the linkage control of the image acquisition module and the printing device, the image acquisition module is automatically triggered to shoot the moment the first printed product leaves the printing device, without the need for human intervention, and a seamless connection from printing to image acquisition is achieved, shortening the time of the entire first inspection process.

[0070] S200, online modeling based on the first print image.

[0071] After acquiring the first printed image, the image acquisition module transmits it to the online modeling module, which can analyze the key feature points in the image and record this information to create an accurate inspection template for subsequent print quality inspection.

[0072] The inspection template contains the structural features of the first printed product (such as text, graphics, color, etc.) and serves as a reference standard for subsequent production.

[0073] In some specific examples, the online modeling module can use image processing techniques, such as edge detection and corner point recognition, to accurately locate text, graphics and other important elements in the image; then, using computer vision and machine learning algorithms, these features are digitally represented to build a high-precision detection template. For example, if a printed product contains a piece of text and a pattern, the system will extract the font, size, spacing of the text, and the edge information of the pattern to create the corresponding modeling data.

[0074] Online modeling can create an accurate digital model based on the actual printed image as a standard template for subsequent product testing. In this way, automated and standardized production can be achieved, manual operations and human errors can be reduced, and the controllability and stability of printing quality can be improved.

[0075] S200', online proofing based on first print image.

[0076] After acquiring the first print image, the image acquisition module transmits it to the first proofing module, which compares the acquired first print image with the electronic file of the printing plate and automatically performs online proofing. The first proofing module uses algorithms to detect various indicators in the first print image, including ink spots, text errors, color deviations, missing patterns, etc., to ensure that the print meets the predetermined standards. If the difference between the image and the electronic sample exceeds the allowable error range, the first proofing module will mark it as unqualified and provide specific defect information.

[0077] In some specific examples, the printing control module controls the transfer of the plate electronic file to the first proofing module, or transfers the plate electronic file from the file storage server to the first proofing module through a network protocol (such as FTP, HTTP, SMB, or a dedicated enterprise file transfer protocol).

[0078] In some specific examples, assume that the printed matter contains a piece of text and a graphic. The first proofing module will compare the collected printed image with the corresponding part in the standard electronic sample. If differences are found in the color of the graphic or the size of the font, the system will detect these differences through image processing algorithms and automatically mark them as "color deviation" or "font mismatch".

[0079] In some specific examples, step S200, online modeling based on the first printed matter image and step S200', online proofing based on the first printed matter image are carried out simultaneously. By parallel processing the modeling and proofing steps, the overall time of the first proofing process can be significantly reduced. Traditionally, modeling and proofing are usually executed linearly, with proofing inspection completed first and then modeling. Executing these two steps in parallel can immediately start proofing detection and online modeling synchronously after the image is collected, significantly improving the processing speed of the first proofing, avoiding waiting time in the production process, and enhancing production efficiency.

[0080] In some embodiments, step S200', online proofing based on the first printed matter image specifically includes the following steps:

[0081] S210', preprocess the plate electronic file to obtain an electronic file comparison image with a set resolution.

[0082] The purpose of preprocessing the plate electronic file is to ensure that the electronic file image for comparison has the same resolution as the collected first printed matter image, so as to eliminate content misalignment and comparison errors caused by resolution differences.

[0083] In some embodiments, preprocessing the plate electronic file to obtain an electronic file comparison image with a set resolution includes:

[0084] Convert the plate electronic file into a first image with a set resolution:

[0085] Segment the pattern and background of the first image and perform blurring processing;

[0086] Extract and merge the pattern texture of the first image according to the RGB three color channels, and generate a texture mask image through a threshold to obtain an electronic file comparison image with a set resolution.

[0087] In some specific examples, the first proofing module can determine a suitable output resolution (such as 300 dpi) as the set resolution according to the specific requirements of the printing device and the resolution of the first printed matter image; then, the first proofing module converts the original plate electronic file (usually in PDF or EPS format) into a first image with the set resolution.

[0088] In some specific examples, professional graphic processing software (such as Adobe Illustrator or CorelDRAW) can be used to convert the original plate electronic file (usually in PDF or EPS format) into a high-resolution raster image (the first image). This process preserves the vector information in the original file to ensure that the quality of the converted image is not damaged.

[0089] In some specific examples, the purpose of segmenting the pattern and the background of the first image and performing blurring is to reduce local deformation and distortion through segmentation and blurring, and improve the accuracy of subsequent feature extraction. The first proofing module can apply image segmentation algorithms (such as GrabCut or deep learning-based semantic segmentation models) to accurately separate the pattern (foreground) and the background in the first image. This segmentation can effectively remove background interference and make subsequent processing more focused on key areas. Then, Gaussian blurring with different parameters is applied to the segmented pattern part. By adjusting the blur radius, the internal details can be smoothed while maintaining the clarity of the pattern edges, reducing small deformations that may affect the comparison.

[0090] In some specific examples, the first proofing module uses different texture analysis algorithms (such as gray-level co-occurrence matrix GLCM, local binary pattern LBP, etc.) according to the three color channels of red (R), green (G), and blue (B) to extract texture features from each color channel. These algorithms can capture the local structure and repeating patterns in the image, providing rich information for subsequent comparison. Then, the first proofing module combines the extracted texture features according to the RGB three channels to generate a complete texture image. Then, by setting an appropriate threshold, a texture mask image is generated. This method can highlight the important texture areas in the image while suppressing noise and other irrelevant information. If necessary, the first proofing module can further optimize the generated texture mask image, such as applying morphological operations (dilation, erosion) to clean up noise points to ensure the integrity and accuracy of the mask. Finally, an electronic file comparison image with the set resolution is obtained.

[0091] Through the above detailed steps, the present application converts the plate electronic file into an electronic file comparison image, and maintains the resolution and color consistency of the electronic file comparison image and the first printed product, eliminating the comparison error caused by resolution differences and color deviations, and significantly improving the accuracy of detection. In addition, through the pattern and background segmentation, blurring processing, and multi-channel texture extraction of the electronic file comparison image, the key features in the image are enhanced, making the subsequent feature matching more reliable.

[0092] S220’: Calibrate the first printed product image according to the electronic file comparison image.

[0093] The online detection system with the function of the first proof of the printed product provided by the present application is installed on the production equipment. The jitter of the product paper and the slight deformation during the imaging process of the image acquisition module to collect images will affect the quality of the first printed product image collected online, and the first inspection proof has relatively high requirements for the image quality. Therefore, calibrating the first printed product image collected online to ensure that it can meet the first inspection requirements for comparison with the electronic file comparison image is a necessary prerequisite for the function.

[0094] In some embodiments, calibrating the first printed product image according to the electronic file comparison image includes:

[0095] Adjust the resolution of the first printed product image to be the same as the resolution of the electronic file comparison image with the set resolution, ensuring that the first printed product image and the electronic file comparison image have the same resolution, so as to eliminate the content misalignment and comparison error caused by the resolution difference;

[0096] Perform geometric correction on the first printed product image. Select rough corner points on the first printed product image through an algorithm, and further screen out fine corner points as calibration base nodes; perform similarity comparison and correction restoration with the electronic file comparison image within the range of action of each calibration base node to restore the local deformation of the first printed product image to the greatest extent; correct the geometric deformation of the first printed product image caused by scanning, shooting or other reasons to ensure its shape consistency with the electronic file comparison image.

[0097] Perform color correction on the first printed product image to further reduce the difference between the first printed product image and the electronic file comparison image;

[0098] Perform filtering processing on the first printed product image to make the blur degree of the first printed product image close to the same as that of the electronic file comparison image, reducing the comparison error caused by different blur degrees.

[0099] In some specific examples, the first proofing module compares the resolution of the first printed image with a known electronic file and uses an interpolation algorithm in image processing software (such as Adobe Photoshop or GIMP) to adjust the resolution of the first printed image to the same resolution. Common interpolation methods include nearest neighbor, bilinear, bicubic, etc., and the method that can best maintain the quality of the original image is selected. When adjusting the resolution, special attention is paid to maintaining the clarity of small text and patterns in the image to avoid blurring or distortion caused by magnification or reduction.

[0100] Figure 2 It is a schematic diagram of the fine corner point clustering result during geometric correction in the calibration of an online detection method with the first proofing function of printed matter provided by an exemplary embodiment of the present application.

[0101] In some specific examples, the first proofing module can use computer vision techniques (such as Harris corner detection or FAST feature detection) to automatically identify multiple possible corner points on the first printed image as preliminary positioning points (coarse corner points) to calibrate the key positions of image geometric correction.

[0102] Refer to Figure 2 , 50 coarse corner points are obtained by searching the entire image through the FAST function, and each corner point meets the relevant score requirements and the filtering requirements for the difference in mutual distance. Further applying the K-means algorithm, the coarse corner points are reduced to the most representative fine corner points through unsupervised learning clustering division. Figure 2 The large square box in it represents the image area, and there are 20 selected fine corner points inside, that is, each hollow in the figure. The square box around it represents the calibration range of this point. These selected fine corner points will be used as basic nodes in the subsequent correction process. Finally, based on the selected fine corner points, the similarity of local areas is compared with the electronic file reference image within the action range of each calibration basic node. Metrics such as normalized cross-correlation (NCC) and structural similarity (SSIM) are used to measure the similarity, and accordingly, the position, rotation, and scaling of the first printed image are adjusted to restore local deformation to the greatest extent and reduce the image misdetection difference during subsequent first inspection comparison and evaluation.

[0103] Among them, the FAST (Features from Accelerated Segment Test) function is an algorithm for corner detection. It identifies corner points by quickly detecting changes in pixel values in the image. Specifically, the FAST algorithm checks a continuous pixel segment around a pixel. If the brightness of these pixel segments is significantly different from the central pixel (i.e., meets a certain threshold condition), then this pixel is determined to be a corner point.

[0104] The K-means algorithm is an unsupervised learning clustering algorithm used to divide data points in a dataset into K clusters. The algorithm continuously updates the centroids of the clusters through iteration and assigns the data points to the clusters to which the nearest centroids belong.

[0105] In some specific examples, a suitable lookup table (such as an sRGB lookup table) is used to implement color correction. The sRGB lookup table can standardize the color space to ensure that the color performance of the first printed image is consistent with that of the electronic file reference image.

[0106] In some specific examples, the method of guided image filtering is selected for filtering. The electronic sample file reference image is used as the guided reference image, and the first printed image after geometric correction and color correction is used as the object to be guided. This method can smooth the noise while retaining the edge details, ensuring that the final output image has a generally consistent degree of blurriness and meeting the requirements of the next registration.

[0107] After the above detailed steps, this application eliminates the errors caused by differences in imaging conditions by strictly controlling the resolution, geometric correction, color correction, and filtering, greatly improving the accuracy and reliability of the comparison between the first printed image and the electronic file reference image.

[0108] S230’: Register the electronic file reference image and the calibrated first printed image.

[0109] After calibration, the first proofing module registers the plate electronic file with the calibrated first printed image. Registration means precisely aligning the two to facilitate subsequent difference comparison. The purpose of registration is to ensure that the key elements in the printed image can precisely correspond to the corresponding parts in the plate electronic file, providing an accurate basis for subsequent comparison and evaluation.

[0110] In some embodiments, the process of the first proofing module performing registration includes: automatically detecting and extracting significant feature points in the plate electronic file; from the large number of extracted feature points, through a series of screening rules (such as stability, repeatability, etc.), selecting the most representative and reliable points as "registration nuclei", and these registration nuclei will become the key reference points for establishing subsequent mapping relationships; for each registration nucleus, searching for its corresponding best matching point in the first printed image, thereby establishing multiple position correspondence relationships, and this step can be achieved through methods such as nearest neighbor search, template matching or other similarity measurement methods; through the matching of a large number of registration nuclei, mapping of multiple regions between the plate electronic file and the first printed image can be realized, and this mapping is not limited to single feature points, but also includes the overall structure and shape of the regions where these points are located; in order to improve the mapping accuracy, the first proofing module will perform weighted processing on the local mapping relationships: specifically, according to the importance of each registration nucleus and the influence of its surrounding environment, different weight values are assigned, and finally an accurate mapping relationship for each position between the images is obtained. This method can effectively reduce the overall deviation caused by the accumulation of local errors.

[0111] In some specific examples, these feature points can be the corners of patterns, the edges of texts, the intersections of graphics, etc. They are positions with uniqueness and stability in the image and are suitable as reference points for comparison.

[0112] In some specific examples, image processing algorithms (such as SIFT, SURF or ORB) are used to identify and locate these feature points. These algorithms can effectively capture the local invariant features in the image and maintain good recognition effects even in the presence of slight deformations or lighting changes.

[0113] By precisely registering the plate electronic file and the calibrated first printed image, the present invention ensures the geometric consistency between the printed product and the design file, providing a reliable basis for subsequent image comparison and evaluation. The registration process involves multiple steps such as detection and extraction of feature points, selection and matching of registration nuclei, multi-region mapping, and weighted processing, aiming to maximize the accuracy and reliability of detection. This method not only optimizes work efficiency but also brings significant quality improvement and technical advantages to printing enterprises.

[0114] S240’: Compare and evaluate the structure of the registered first printed image with the electronic file control image, and output the online proofing result.

[0115] After registration is completed, the first proofing module compares and evaluates the registered first printed image with the plate electronic file. The purpose of this step is to detect the differences between the two and screen and evaluate these differences according to the set criteria, and output the online proofing result.

[0116] In some embodiments, the process of the first proofing module for comparison and evaluation includes: based on the registered position correspondence, the first proofing module will compare the image structure of the first printed matter with the structural elements of the plate electronic file one by one, identify and mark any difference points between the two images; according to the preset evaluation criteria (such as tolerance range, threshold, etc.), the first proofing module screens the found difference points, and the screening aims to distinguish real problems from small errors that can be ignored; for the screened difference points, the first proofing module will prioritize them according to their severity to ensure that the most important problems are noticed and processed first. Finally, an online proofing result is generated and output.

[0117] In some specific examples, the structural elements include, but are not limited to, key visual features such as text, graphics, color distribution, etc.

[0118] In some specific examples, the difference points include, but are not limited to, stains or blurred areas caused by excessive or uneven ink, differences in text content or font styles, some parts not being printed correctly or completely missing, color performance not meeting the expected standards, etc.

[0119] In some specific examples, the online proofing result can be an online proofing report, listing all the found problems and their locations, and providing corresponding suggestions. The report content can include: problem description: elaborating on the specific situation of each difference point; location information: indicating the specific position coordinates in the image where the problem appears; correction suggestions: proposing possible solutions for each problem, such as adjusting the plate, resetting parameters, etc.

[0120] In some specific examples, the generated online proofing result can be transmitted to the human - machine interaction module in real - time through an internal network or a dedicated communication interface. The human - machine interaction module enables the operator to intuitively view and manage the running state of the system. In some specific examples, the online proofing result can be displayed and processed through a touch - screen monitor, which can display the online proofing result in a graphical interface, including visual marking of difference points, location information, and problem description; the operator can directly click or slide on the screen to zoom in on the image, select a specific area for detailed inspection, and even mark or annotate some problems. The touch - screen monitor can provide a series of shortcut buttons for quick access to common operations, such as re - detection, adjustment of settings, etc.

[0121] S300. When the online proofing result is qualified, continuous printing production is carried out with the online modeling result as the detection standard.

[0122] When the operator determines that the online proofing result is qualified, a continue printing instruction is issued to the printing control module through the human-machine interaction module. The printing control module will use the previously established online modeling result as the quality standard for subsequent printing production and conduct continuous batch production. All subsequent printed products will be compared with the standard template obtained from online modeling to ensure the quality consistency of subsequent products.

[0123] In some embodiments, the operator reviews the online proofing result. If the online proofing result is unqualified, corresponding measures need to be taken for correction and then the above process is repeated until it is qualified.

[0124] The corresponding measures can usually be to make the operator recheck whether there are errors in the electronic file and the printing plate, check whether there are errors in the process of image acquisition of the printed product, check whether there are errors in the preprocessing of the electronic file, etc. After troubleshooting and correcting the errors, the above steps are carried out again.

[0125] Repeating the above process includes acquiring the image of the first printed product again, performing calibration, registration, comparison, and evaluation until a satisfactory result is obtained.

[0126] An online detection method with the function of the first proofing of printed products provided by one or more of the above embodiments of the present application can achieve instant detection and feedback by executing the online modeling and the online proofing steps of the first printed product in parallel. The time on the production line is fully utilized, the production stagnation caused by waiting for the proofing result is reduced, and the working efficiency of the first proofing is greatly improved. And through operations such as calibrating, registering, comparing, and evaluating the image of the first printed product, the image deviation caused by paper jitter and minute deformation can be automatically identified and corrected, ensuring the precise matching between the image and the electronic file. In addition, the automation degree of the online detection system is significantly improved, and the need for manual intervention is reduced. In the traditional method, manual proofing is prone to omissions and errors, while the method provided by the present application greatly reduces the possibility of human errors through automated proofing and modeling processing, ensuring that each operation can be strictly executed according to the standard.

[0127] Figure 3 is a schematic structural diagram of an online detection system with the function of the first proofing of printed products provided by an exemplary embodiment of the present application; Figure 4 is a schematic structural diagram of the first proofing module of an online detection system with the function of the first proofing of printed products provided by an exemplary embodiment of the present application.

[0128] Refer to Figure 3 and Figure 4, the second aspect of this application provides an online detection system with the function of the first proof of printed matter, which is used to online detect the printing production controlled by the printing control module 100, including an image acquisition module 200, an online modeling module 300, a first proof module 400, a human-computer interaction module 500 and an online monitoring module 600.

[0129] The image acquisition module 200 is the first link of this online detection system, and its main function is to collect the image of the first printed matter produced on the printing press in real time through a high-resolution camera or sensor.

[0130] In some specific examples, the image acquisition device 200 can be a CCD camera (for example, an industrial camera with the model number XG-5000) or a line scanner.

[0131] The image acquisition module 4200 ensures that the collected image has a high enough resolution (usually above 300 dpi) to ensure the accuracy of subsequent calibration and modeling.

[0132] The online modeling module 300 is connected to the image acquisition module 200 to receive the image of the first printed matter and perform modeling processing in real time. The modeling processing is to generate a standard template according to the collected image of the first printed matter. This template will be used as the standard for the quality detection of printed matter in the subsequent production process.

[0133] In some specific examples, the online modeling module 300 usually includes computer vision algorithms and processing units (such as CPU, GPU), and supports the modeling function based on image analysis. Commonly used image processing algorithms include edge detection, feature matching, color correction, etc.

[0134] The online modeling module 300 can construct an accurate digital detection template by extracting key information (such as text, color, graphic elements) in the image. This detection template improves the quality control efficiency in the production process and can ensure the quality consistency of each printed matter.

[0135] The first proof module 400 is connected to the image acquisition module 200 and the printing control module 100, and its main function is to perform proofreading inspection according to the collected image of the first printed matter. It can receive the electronic sample file from the printing control module 100 and compare it with the actually collected image of the first printed matter, and automatically detect the differences between the first printed matter and the design requirements. The first proof module 400 can discover and mark problems such as ink dots, character discrepancies, color deviations, etc., and transfer the results to the subsequent modules for processing.

[0136] In some specific examples, the first proofing module 400 may include an image processing unit (such as a GPU-accelerated processor) and a proofing software system that can automatically read the plate electronic file (such as a PDF file or other design file) and compare it with the image.

[0137] The first proofing module 400 can significantly reduce the workload of manual inspection, ensuring that potential quality issues are detected before the start of printing production. It can provide real-time feedback on the proofing results and offer detailed defect reports to the operators. By automating the proofing process, production efficiency can be effectively improved, human errors can be reduced, and the quality standards of each batch of printed products can be ensured to be consistent.

[0138] The human-machine interaction module 500 (HMI) is connected to the first proofing module 400 and the printing control module 100. Its main function is to display the online proofing results to the operators through a visual interface and send control instructions to the printing control module 100 based on the proofing results. The human-machine interaction module 500 not only shows the image comparison results but also allows the operators to confirm or modify the proofing results and adjust the production parameters in a timely manner.

[0139] In some specific examples, the human-machine interaction module 500 is usually composed of a touch screen, an operation panel, an industrial computer, or a PC. The touch screen can display real-time proofing data, image comparison results, and other important parameters, such as color difference and defect areas. Through the interface, the operators can view real-time data, receive alarms, and make operation adjustments.

[0140] The human-machine interaction module 500 provides a user-friendly interface, enabling the operators to track the proofing results in real time, promptly discover problems in production, and adjust the device settings according to the system prompts. This module effectively improves the operation convenience, reduces operation errors, and enhances the visibility and controllability of the production process.

[0141] The online detection module 600 is connected to the online modeling module 300. Its main function is to use the online modeling results as the detection standard to conduct real-time quality detection on the continuous printing production process. This module can automatically detect each subsequent batch of printed products and ensure that they meet the preset quality standards. The online detection module will automatically compare the images according to the modeling results. If any non-compliant areas are found (such as pattern misalignment, color deviation, missing graphics and texts, etc.), operations such as alarm and error marking will be carried out.

[0142] In some specific examples, the online detection module 600 is usually composed of an image processing unit, sensors, and analysis algorithms, and can automatically scan the printed products and compare them with the templates. The detection algorithms can include machine learning models that can adapt to different printing modes and quality standards.

[0143] The online detection module 600 ensures that each product meets the quality standards by monitoring the quality of each batch of printed products in real time. It can detect quality problems in a timely manner and prevent the production of a large number of defective products. This module not only improves the automation level of the production line but also reduces the need for manual quality inspection, improving production efficiency and the accuracy of quality control.

[0144] In some specific examples, the printing control module 100 can receive instructions from the human-machine interaction module 500, such as controlling the printing equipment to print or stop printing, adjusting color configuration, resolution, paper type, etc., and can also transmit the plate electronic file to the first proofing module 400 for proofing.

[0145] In some embodiments, referring to Figure 3 , the first proofing module 400 includes: a calibration unit 410, a registration unit 420, and an evaluation unit 430.

[0146] The calibration unit 410 is connected to the image acquisition module 200 and the printing control module 100, and is used to receive the first printed product image and the plate electronic file, and calibrate the first printed product image according to the plate electronic file.

[0147] In some specific examples, the operations performed by the calibration unit 410 include:

[0148] Preprocess the plate electronic file to obtain an electronic file comparison image with a set resolution: convert the plate electronic file into a first image with a set resolution; segment the pattern and background of the first image and perform blurring processing; extract and merge the pattern texture of the first image according to the three color channels of RGB, and generate a texture mask image through a threshold to obtain an electronic file comparison image with a set resolution.

[0149] Adjust the resolution of the first printed product image to make it consistent with the resolution of the electronic file comparison image with a set resolution;

[0150] Perform geometric correction on the first printed product image: select rough corner points on the first printed product image through an algorithm, and further screen out fine corner points as calibration base nodes; perform similarity comparison and correction reduction with the electronic file comparison image within the range of action of each calibration base node to restore the local deformation of the first printed product image to the greatest extent;

[0151] Perform color correction on the first printed product image to further reduce the difference between the first printed product image and the electronic file comparison image;

[0152] Perform filtering processing on the first printed product image to make the blurring degree of the first printed product image close to that of the electronic file comparison image.

[0153] The registration unit 420 is connected to the calibration unit 410 and is used to combine the calibrated first printed image and register the plate electronic file and the calibrated first printed image.

[0154] In some specific examples, the operations performed by the registration unit 420 include: feature point recognition: automatically detecting and extracting significant feature points in the plate electronic file using image processing algorithms (such as SIFT, SURF, or ORB). These feature points can be the corners of patterns, the edges of text, the intersections of graphics, etc.; positioning kernel selection: selecting the most representative and reliable points as "positioning kernels" from the large number of extracted feature points through a series of screening rules (such as stability, repeatability, etc.); matching positioning kernels: for each positioning kernel, finding its corresponding best matching point in the calibrated first printed image to establish multiple position correspondence relationships; multi-region mapping: realizing the mapping of multiple regions between the plate electronic file and the calibrated first printed image through the matching of a large number of positioning kernels to ensure global consistency; local mapping optimization: performing weighted processing on the local mapping relationships to optimize the matching accuracy and obtain the accurate mapping relationships of each position between the images, ensuring the consistency of the overall structure.

[0155] The evaluation unit 430 is respectively connected to the registration unit 420 and the human-computer interaction module 500 and is used to compare and evaluate the structure of the registered first printed image with the plate electronic file and output the online proofing result to the human-computer interaction module 500.

[0156] In some specific examples, the operations performed by the evaluation unit 430 include: one-by-one comparison: based on the registered position correspondence relationships, comparing the structural elements of the first printed image with those of the plate electronic file one by one to find the difference points. These structural elements include but are not limited to key visual features such as text, graphics, color distribution, etc.; difference detection: identifying and marking any inconsistencies between the two images, such as ink dirt, character non-conformity, graphic and text missing, color deviation, etc.; preset evaluation method: screening the found difference points according to the preset evaluation criteria (such as tolerance range, threshold, etc.) to distinguish real problems from small errors that can be ignored; priority ranking: ranking the screened difference points according to the severity level to ensure that the most important problems are first noticed and processed; report generation: automatically generating a detailed online proofing report and feeding it back to the human-computer interaction module 500, listing all the found problems and their positions and providing corresponding suggestions. The report content may include problem descriptions, position information, correction suggestions, etc.

[0157] It should be noted that the technical solutions in the various embodiments of the present application can be combined with each other, but the basis for the combination is that those skilled in the art can implement it; when the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist, that is, it does not belong to the protection scope of the present application either.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. An online detection method with the function of the first proof of printed matter, characterized in that, Including: Collecting the image of the first printed product while printing the first printed product; Performing online modeling based on the image of the first printed product; Performing online proofreading based on the image of the first printed product; When the result of online proofreading is qualified, taking the result of online modeling as the detection standard and carrying out continuous printing production; Among them, performing online modeling based on the image of the first printed product and performing online proofreading based on the image of the first printed product are carried out simultaneously; Performing online proofreading based on the image of the first printed product includes: Preprocessing the plate electronic file to obtain an electronic file comparison image with a set resolution; Calibrating the image of the first printed product according to the electronic file comparison image; Registering the electronic file comparison image and the calibrated image of the first printed product; Comparing and evaluating the structure of the registered image of the first printed product with the electronic file comparison image, and outputting the result of online proofreading; Calibrating the image of the first printed product according to the electronic file comparison image includes: Performing geometric correction on the image of the first printed product to restore the local deformation of the image of the first printed product; Specifically, performing geometric correction on the image of the first printed product includes: Obtaining multiple rough corner points on the image of the first printed product through a corner point detection algorithm; screening out fine corner points from the rough corner points by using a clustering algorithm; taking the fine corner points as a benchmark, comparing the similarity in the corresponding area with the electronic file comparison image; adjusting the position, rotation and scaling of the image of the first printed product according to the comparison result to correct the local deformation.

2. The online detection method with the function of the first proof of printed matter according to claim 1, characterized in that, Preprocessing the plate electronic file to obtain an electronic file comparison image with a set resolution includes: Converting the plate electronic file into a first image with a set resolution; Segmenting the pattern and background of the first image and performing blurring processing; Extracting and merging the pattern texture of the first image according to the three color channels of RGB, and generating a texture mask image through a threshold to obtain an electronic file comparison image with a set resolution.

3. The online detection method with the first proof function of printed matter according to claim 1, characterized in that, Calibrating the image of the first printed product according to the electronic file comparison image includes: Adjusting the resolution of the image of the first printed product to make it consistent with the resolution of the electronic file comparison image with a set resolution; Performing color correction on the image of the first printed product to further reduce the difference between the image of the first printed product and the electronic file comparison image; Performing filtering processing on the image of the first printed product to make the blurring degree of the image of the first printed product close to that of the electronic file comparison image.

4. The online detection method with the function of the first proof of printed matter according to claim 1, characterized in that, Registering the electronic file comparison image and the calibrated image of the first printed product includes: Detecting and extracting the significant feature points in the electronic file comparison image; Selecting the positioning kernels from the feature points; Determining the best matching points corresponding to each positioning kernel in the image of the first printed product to establish a position correspondence relationship; Realizing the mapping of multiple regions between the plate electronic file and the image of the first printed product based on the matching of a large number of positioning kernels; Performing weighted processing on the local mapping relationship to obtain an accurate mapping relationship between each position of the images.

5. The online detection method with the first proof function of printed matter according to claim 1, characterized in that Comparing and evaluating the structure of the registered image of the first printed product with the electronic file comparison image, and outputting the result of online proofreading includes: Based on the registered position correspondence relationship, comparing the structure elements of the image of the first printed product with the structure of the electronic file comparison image one by one to find out the difference points; Screen the difference points according to the preset evaluation method and generate the online proofing result.

6. The online detection method with the first proof function of printed matter according to claim 1, characterized in that Compare and evaluate the structure of the first printed image after registration with the electronic file reference image. After outputting the online proofing result, it also includes: The online proofing result is shown through the human-computer interaction module. The operator reviews the online proofing result. If the online proofing result is unqualified, corresponding measures need to be taken for correction, and then the calibration, registration, and comparison operations are repeated until it is qualified.

7. An online detection system with the function of the first proof of printed matter, which is used for online detection of printing production controlled by a printing control module, is characterized in that Including: An image acquisition module for acquiring the first printed image; An online modeling module connected to the image acquisition module for online modeling based on the first printed image; A first proofing module connected to the image acquisition module and the printing control module for online proofing based on the first printed image; A human-computer interaction module connected to the first proofing module and the printing control module for displaying the online proofing result and sending control instructions to the printing device according to the online proofing result; An online detection module connected to the online modeling module for detecting the printed products in continuous printing production with the online modeling result as the detection standard; Among them, the first proofing module includes: A calibration unit connected to the image acquisition module and the printing control module for receiving the first printed image and the plate electronic file, preprocessing the plate electronic file to obtain an electronic file reference image with a set resolution, and calibrating the first printed image according to the electronic file reference image; The operations performed by the calibration unit include: Perform geometric correction on the first printed image: select rough corner points on the first printed image through an algorithm, and further screen out fine corner points as calibration base nodes; compare the similarity with the electronic file reference image and perform correction and restoration within the range of action of each calibration base node to restore the local deformation of the first printed image to the greatest extent.

8. The online detection system with the first proof function of printed matter according to claim 7, characterized in that The first proofing module includes: A registration unit connected to the calibration unit for combining the calibrated first printed image and registering the electronic file reference image and the calibrated first printed image; An evaluation unit connected to the registration unit and the human-computer interaction module respectively for comparing and evaluating the structure of the registered first printed image with the electronic file reference image and outputting the online proofing result to the human-computer interaction module.

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