1582 results about "Binary image" patented technology

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention relates to the technical field of industrial product surface quality detection, in particular to a flexible display module surface defect image recognition method, which comprises the following steps: acquiring a plurality of surface images of a flexible display module under different light sources and carrying out distortion removal processing on the surface images; reconstructing and generating three-dimensional reference point cloud data representing the current curved surface form of the module; re-projecting the distorted image to the ideal rigid plane according to the relationship, generating a plurality of corrected images, and generating a plurality of corrected images to eliminate geometric and luminosity distortion introduced by flexible deformation; obtaining a defect candidate area binary image; and extracting a multi-dimensional feature vector of the defect candidate region from the binary image of the defect candidate region, and classifying the feature vector by using a pre-trained defect classification model to obtain a defect identification result. Through the three-dimensional reference point cloud reconstruction and image re-projection technology, the problem of geometric distortion caused by surface deformation of the flexible display module is effectively solved, and misjudgment and missed judgment are avoided.

The invention relates to the technical field of image data processing, in particular to a forge piece surface defect detection method and system based on machine vision. The method comprises the steps of obtaining a gray image of a to-be-detected forging surface; determining a boundary significance weight; determining a path consistency weight; screening the boundary significance weight and the path consistency weight to obtain a final weight; and carrying out local adaptive threshold segmentation on the final feature map to obtain a binary image, and carrying out defect identification on the surface of the forge piece to be detected based on a connected region in the binary image. According to the method, the boundary significance weight and the path consistency weight are constructed and are respectively used for accurately positioning defect edges and verifying structure continuity, texture interference is effectively inhibited, and false alarms are reduced; through adaptive Gabor filtering, the problem of a response blind area of a traditional method is solved, finally two weights are fused to modulate filtering response, and the accuracy of forging surface defect detection is improved.

The invention relates to the technical field of road pavement disease recognition, in particular to an intelligent road pavement disease recognition method and system based on a multispectral image, and the method comprises the steps: obtaining road pavement image data collected by a multispectral camera carried by an unmanned aerial vehicle; generating a registered multispectral fusion image; performing enhancement processing on the multispectral fusion image based on a dark channel algorithm to generate an enhanced image; performing shallow feature extraction and deep feature extraction on the enhanced image according to a convolutional neural network to generate an initial disease extraction image; performing segmentation processing on the initial disease extraction image through adaptive threshold segmentation to generate a binary image; performing morphological processing on the binary image to generate a candidate disease image; extracting edge information of a disease area from the candidate disease image based on an instance segmentation technology, and generating a segmented image; the segmented image is subjected to crack refining processing, a crack refined image is generated, and more comprehensive disease information support is provided for collection and fusion of multispectral data.

The invention discloses a steel-wood combined beam-column joint damage identification method and system applying machine vision, and the method comprises the steps: S1, obtaining a visible light, near-infrared and thermal imaging multi-mode image sequence of a steel-wood combined beam-column joint, and carrying out the denoising processing through adaptive anisotropic diffusion filtering, and obtaining a denoised multi-mode image sequence; s2, performing Gaussian pyramid decomposition and Laplacian pyramid feature fusion on the de-noised multi-modal image sequence tensor to obtain a fusion feature map; s3, performing adaptive threshold segmentation and morphological processing on the fused feature map to obtain a final binary image; s4, training the deep learning network to obtain a trained deep learning network; and S5, performing damage identification by using the trained deep learning network. According to the method, the steel-wood combined beam column joint damage can be accurately identified, the segmentation probability graph is obtained, the segmentation result is marked, and the visualization of the damage area is realized.

The invention discloses a highly dense broken ice image segmentation method based on an iteration MGAC and an SAM model, and the method comprises the steps: carrying out the sea ice instance segmentation of a preprocessing image based on the SAM model, and obtaining a sea ice mask image corresponding to the preprocessing image; according to the binary image and the sea ice mask image, obtaining an initial sea ice residual region image which is not identified by the SAM model; obtaining initial sea ice residual region images under different gray threshold values to obtain an initial seed mask graph; taking the initial sea ice residual region image and the initial seed mask image as inputs of a preset MGAC contour model, and obtaining an MGAC sea ice recognition result based on a multi-round iteration partitioning mechanism; and performing union operation on the MGAC sea ice identification result and the sea ice mask image to obtain a crushed ice segmentation mask result. The method solves the problem that the existing method is insufficient in structure extraction precision and boundary integrity of the dense broken ice area.

The invention discloses a crack identification method and device based on multi-scale infrared thermal feature fusion, and the method comprises the steps: obtaining the gray images of cracks and noise for a single-frame infrared thermal image; histogram equalization is adopted to adjust image gray level distribution, and a gray level enhanced image is obtained; setting a larger local feature sensitivity weight coefficient for the background dominant window and the noise dominant window, and setting a smaller local feature sensitivity weight coefficient for the target composite window; counting the number of foreground pixels in each window, and calculating the ratio of the number of foreground pixels to the total number of pixels; determining a background dominant window, a noise dominant window and a target composite window; setting local feature sensitivity weight coefficients of a background dominant window, a noise dominant window and a target composite window; segmenting the grayscale enhanced image to obtain a binary image, and extracting a connected domain of foreground pixels; for each foreground pixel connected domain, calculating the total number of pixels, randomly selecting a plurality of seed points, determining corresponding maximum inscribed circles, taking the average value of the diameters of all the maximum inscribed circles as the width, taking the ratio of the total number of pixels to the width as the length, and screening out connected domains with the length-width ratio smaller than 10 as noise; and optimizing the foreground pixel connected domain, removing fine noise, smoothing the contour, and regarding the finally obtained foreground pixel connected domain as a crack.

The invention discloses an improved filtering and adaptive edge detection steel plate gap identification method for a hull surface image, and the method comprises the steps: 1, carrying out the denoising of a hull surface image containing a steel plate gap through employing a local weighted Gaussian blur and dynamic bilateral filtering fusion algorithm, and obtaining a denoised hull surface image; 2, carrying out the edge detection of a local region of the denoised hull surface image through employing a self-adaptive dynamic threshold Canny edge detection method, and obtaining a binary image; and step 3, carrying out edge extraction on the binary image to obtain a steel plate gap image. According to the method, through a local weighted Gaussian blur and dynamic bilateral filtering fusion denoising algorithm, the noise in the image is efficiently removed in a proportional fusion mode, meanwhile, through self-adaptive adjustment of filtering parameters, the noise influence of a local area is improved in a targeted mode, loss of detail information is avoided, and the image quality and detail presentation are remarkably improved.

The invention discloses a thermal printing image processing method, device and equipment and a medium, and relates to the field of thermal printing. The method comprises the following steps: acquiring an original image to be printed, and converting the original image into a grayscale image; calculating a gradient difference value of the grayscale image, and generating a threshold distribution map; generating a heat accumulation risk map based on an average gray value in a preset neighborhood window taking each pixel point of the gray image as a center; generating a preliminary binary image according to the threshold distribution map; extracting image features of the preliminary binarization image, and classifying the original image to obtain an image classification result; calling a target processing parameter set corresponding to the image classification result from a plurality of preset processing parameter sets according to the image classification result; performing optimization processing on the preliminary binary image according to the target processing parameter set and the heat accumulation risk map to obtain an optimized binary image; and the final printing image adaptive to the resolution of the target printer is generated, so that the printing definition is improved.

The invention provides a core permeability prediction method based on time sequence decomposition and multi-dimensional feature interaction, and belongs to the technical field of core permeability prediction. The method comprises the following steps: performing CT scanning on multiple sandstone samples to obtain high-resolution three-dimensional rock core original data; the method comprises the following steps: performing image preprocessing on original data, performing adaptive threshold segmentation, and dividing pores and matrixes to obtain three-dimensional binary image data; performing small window sliding sampling on the binary image data to obtain three-dimensional core small cubes, and calculating the permeability of each core cube as a label to obtain a final experimental data set; a core image sequence is decomposed from an experimental data set, fusion features are extracted, information is extracted from the fusion features from three dimensions of channel, height and width, features of different dimensions are combined, and the permeability is predicted through a full connection layer. According to the method, lightweight design is adopted, multi-dimensional information of the core image is fully utilized, prediction efficiency and precision are remarkably improved, and computing resource consumption is reduced.

The invention discloses a capillary pressure and contact angle synchronous characterization method and device of a microfluidic fluid interface, and relates to the field of interface analysis and multiphase seepage in pore media, and the method comprises the following steps: based on a pre-configured pore, particle and fluid binary image, distinguishing and extracting a fluid-particle contact interface contour and an attack / defense fluid interface contour; fitting and determining the curvature radius of each fluid interface contour through a watershed region division algorithm and reordering smoothing of Fourier descriptors; generating a multi-level size pore space based on the pore binary image, and obtaining a pore overlook radius; calculating a necking angle in combination with the curvature radius and the multi-stage size pore space to obtain an apparent contact angle and an intrinsic contact angle; and calculating the capillary pressure of the attack / defense fluid interface by combining the curvature radius, the pore overlook radius, the chip depth and the interfacial tension. According to the invention, high-precision technical support is provided for micro-fluidic multiphase seepage research.

The invention designs a road crack detection method based on thermo-optical matrix fusion, and the method comprises the steps: obtaining a temperature matrix and a visible light image based on a visible light-temperature matrix collection device, extracting crack edge features through temperature gradient analysis, and carrying out the binarization processing, thereby obtaining a temperature matrix binary image; a thermal-optical matrix image fusion module is provided, and a temperature matrix and a visible light image are effectively fused; a thermal-optical matrix fusion crack image obtained based on the fusion module is used as the input of an improved YOLOv8-AMPN network model, the semantic enhancement effect of a temperature matrix on a visible light image is further improved through a feature weight adaptive weighted fusion and region block level dynamic fusion method in the network, and crack features are highlighted. The method has the advantages that a temperature matrix branch is introduced, the limitation of a visible light image under environment change is effectively overcome, the problem that multi-modal image information is redundant and complex is solved, and the sensitivity of a model to crack characteristics and the detection response speed are improved through temperature information.

The embodiment of the invention provides a pose estimation method and device and electronic equipment, and belongs to the technical field of visual inspection and positioning. The method comprises the following steps: acquiring a target image in real time, wherein the target image is an image of a target object containing a similar two-dimensional code ARuco; performing image preprocessing according to the target image to obtain binary image data; performing mark detection processing according to the binarized image data and a pre-established multi-scale image pyramid so as to identify and position a similar two-dimensional code ARuco mark; performing camera calibration processing according to preset camera parameters to obtain camera calibration data; performing corner detection processing according to the ARuco mark to obtain positioning corner position information; and according to the positioning corner point position information, the preset target three-dimensional space position information and the camera calibration data, carrying out mark attitude calculation processing to obtain target pose estimation information. The method is used for achieving the effect of improving the accuracy of the pose estimation result.

The invention discloses a light guide plate dot density detection method, system and device and a storage medium, and relates to the technical field of light guide plate dot density detection, and the method comprises the following steps: converting a historical light guide plate image into a historical light guide plate binary image based on a graying processing method and a binary processing method; obtaining a standard dot area and a dot area range based on the historical light guide plate binary image; obtaining a detection normal fitting area and a detection incomplete fitting area based on the standard dot area, the dot area range and the detection light guide plate binary image; calculating and obtaining the dot density of the detection light guide plate based on the detection normal fitting area and the detection incomplete fitting area; the method is used for solving the problem that errors are generated in dot density calculation due to the fact that edge incomplete dots are not analyzed and calculated in an existing light guide plate dot density calculation technology.

The invention discloses a metallographic detection method and system based on image recognition, and belongs to the technical field of metallographic detection. The detection method comprises the following steps: image acquisition and preprocessing: acquiring a metal metallographic image by an optical microscope, performing median filtering and denoising, and extracting a grain boundary by adaptive binarization; and field-of-view and scale calibration: selecting a corresponding scale according to the shooting magnification. The multi-parameter automatic measurement comprises grain size analysis, grain size rating, second phase analysis and intelligent report generation, a report is automatically generated based on a preset template and comprises an original image, a binary image, measurement data and a histogram, and report fonts and layout can be adjusted in real time through a visual control. According to the detection method disclosed by the invention, the efficiency, the precision and the reliability of metallographic structure analysis of the metal material are remarkably improved through the full-process design of image acquisition standardization, preprocessing intellectualization, data analysis automation and report generation templating.

The invention discloses a PDF engineering drawing structured recognition method and device based on multi-dimensional feature fusion and a storage medium, and belongs to the technical field of engineering drawing intelligent processing. The method comprises the following steps: PDF analysis and preprocessing: analyzing a PDF engineering drawing file, extracting a vector graph drawing instruction, text block contents and respective original coordinate information, and carrying out image rendering and standardized preprocessing on a drawing page to generate a binary image with a standard resolution; multi-dimensional feature extraction: extracting the following four types of features from the binary image: a visual depth feature, a text semantic feature, an industry specific symbol feature and a spatial topology feature between elements; feature fusion and relation reasoning: inputting the visual depth features, the text semantic features, the industry specific symbol features and the spatial topological features into a graph neural network fusion module, and constructing a graph structure taking each identified element as a node and taking a spatial topological relation between the elements as an edge, performing feature interaction and fusion among nodes through an attention mechanism, and reasoning a semantic association relationship among drawing elements; and generating structured data: classifying and grouping the drawing elements according to the semantic association relationship, and outputting a structured data file according to a predefined structured format. Therefore, the technical problems of low recognition efficiency and accuracy in the prior art are solved, and the technical effect of efficient and accurate engineering drawing structured recognition is achieved.

The invention relates to an intelligent ship repair quotation method and system based on multi-modal analysis and a large language model, and the method comprises the steps: firstly receiving a ship repair inquiry sheet file uploaded by a user, and carrying out the feature enhancement of image data through a DocEnTR model, and obtaining a binary image; automatically extracting unstructured text data, semi-structured table data and text information in the binarized image by adopting an OCR (Optical Character Recognition) technology, and performing multi-modal analysis on image features of the binarized image and the extracted text information by adopting a large language model to generate an initial data stream; synchronously generating a standard semantic tag and a non-standard content tag by adopting semantic association of the large language model, automatically generating a dynamic cue word according to the standard semantic tag, inputting the dynamic cue word into the large language model, and outputting a standardized engineering description; performing manual auditing on the non-standard content mark to correct the engineering item field so as to generate structured engineering data; and finally, calculating the total price based on the structured engineering data and the material quantity parameters, and further generating a final quotation list to complete ship repair quotation.

The invention discloses a semi-rigid asphalt pavement crack depth estimation method, equipment and a medium, and relates to the technical field of road detection. The method comprises the steps of collecting a high-resolution image of a road surface through a mobile detection platform, segmenting a crack region in the high-resolution image by adopting an image segmentation model, and obtaining a binary image of a crack; pixel accumulation, edge detection, skeletonization, skeleton trimming and orthogonal projection are carried out on the crack binary image to extract surface parameters of the crack, and the surface parameters comprise the type, the length, the width and the area; inputting the fracture surface parameters into the fracture depth prediction model, and outputting a fracture depth prediction value; and generating a crack depth grade label according to the depth predicted value, and outputting a corresponding maintenance strategy. According to the method, the crack surface parameters and the pavement structure parameters are combined, the prediction result has good accuracy and consistency through the coupling relation with the depth index, and the method adapts to different crack types and working conditions.

The invention discloses a monitoring method and system for identifying crop drought based on leaf morphology, and relates to the field of agricultural drought monitoring, and the method comprises the following steps: selecting a completely unfolded healthy leaf, cleaning with ultrapure water, blow-drying with nitrogen, and fixing on a standard objective table to obtain a standardized leaf sample; based on the standardized leaf sample, a high-resolution scanner is used for obtaining a front gray image and a back gray image of the leaf, and a binary image with a clear vein network is obtained through image splicing and binarization processing; skeletonizing the binary image with the clear vein network, and calculating the vein fractal dimension by adopting a box counting method to obtain a fractal dimension change rate; the method is in a fixed detection area of a standardized leaf sample. Through innovative leaf vein fractal dimension analysis and contact angle dynamic measurement, a crop drought monitoring system is constructed, and in the aspect of leaf vein analysis, skeletonization processing and a box counting method are adopted to realize accurate quantification of a leaf vein network topological structure.

The invention discloses a tumor segmentation method based on Mama-guided multi-encoder fusion. The method comprises the following steps: 1, constructing a multi-encoder segmentation model fusing Mama and a convolutional neural network (CNN); 2, training the model on a liver and lung cancer CT image data set and optimizing model parameters; and 3, carrying out rapid positioning and accurate segmentation on an input CT image by using the trained model so as to obtain a binary image of a tumor segmentation result. According to the method, local and global information is fully utilized, multi-scale features are modeled, and the tumor segmentation precision is improved.

The invention discloses an automatic deviation correction method and system for operation of an underwater robot, a medium and a product, and relates to the field of general control or adjustment systems.The method comprises the steps that a weld seam image is obtained, filtering parameters are adjusted according to the noise intensity of a local image in the weld seam image, and a noise reduction image is obtained; performing double-layer threshold segmentation on the noise-reduced image to obtain a binary image; setting a plurality of detection areas in the binarized image, determining an effective detection area, converting edge feature points in the effective detection area into coordinate data, and performing partition weighted fitting to obtain an initial weld direction; constructing a dynamic error band, taking the coordinate points outside the dynamic error band as candidate abnormal points, determining a final abnormal point in the candidate abnormal points in combination with continuity criteria of adjacent feature points, and re-fitting the remaining coordinate points to obtain a target weld direction; and the target welding seam direction is compared with the reference direction, and a graded deviation correction control signal is generated according to the deviation degree. According to the invention, the correction accuracy can be improved.

The invention provides a hierarchical table detection and recognition method and system, and the method comprises the steps: S1, analyzing and processing a table object, constructing a binary image, and obtaining the contour information of a cell; s2, according to the contour information of the cells, performing hierarchical division on the cell format to obtain a row unit set list; s3, according to the row unit set list, dividing a cell format through levels to obtain a column unit set list; and S4, performing hierarchical division on the column unit set list again, and forming a comprehensive list with a preset specification. The method can be applied to the fields of document image processing, intelligent office automation, information extraction and management and the like, is used for accurately detecting and identifying the table content in the document, and provides basic support for subsequent data processing and analysis.

The invention discloses a method for estimating plant biomass based on map multi-modal feature extraction and fusion. The method comprises the following steps: acquiring a plant RGB image and a multi-spectral image; inputting the preprocessed RGB image and NIR wave band image into a double-model cooperation segmentation framework to realize image segmentation; binary image features, color features, texture features, reflectivity and the like are calculated, feature splicing is carried out, and high-dimensional features are constructed; carrying out dimension reduction on the high-dimensional features; and training a deep neural network through the effective features and the biomass to realize biomass estimation. According to the method, a zero sample learning-based double-model cooperation segmentation framework is utilized to realize accurate segmentation of a single plant on the premise that a large number of training sets are not needed; multi-modal feature information is extracted based on the segmented single plant image, an improved SHAP model is introduced to reduce the feature space dimension, and the inversion precision and the operation efficiency are improved while the information effectiveness is ensured; through a high-precision deep neural network model, rapid, lossless and accurate biomass acquisition is realized.

The invention relates to the technical field of image data processing, in particular to an endoscope image intelligent optimization method and system. The method comprises the following steps: acquiring a grayscale image of an endoscope image of a stomach, acquiring an edge image of the grayscale image, and performing region growth on pixel points with gradient values greater than a preset gradient in the edge image to obtain a plurality of first connected domains; according to the area of the minimum enclosing rectangle of the first connected domain and the number of edge pixel points, determining a suspected lesion area in the grayscale image, and taking the minimum grayscale value in the suspected lesion area as an initial segmentation threshold value; and gradually increasing the segmentation threshold value from the initial segmentation threshold value, segmenting the suspected lesion area to obtain a plurality of binary images, and determining a target lesion area according to the difference degree of newly added pixel points between the adjacent binary images in discreteness characteristics. By means of the technical scheme, the accurate focus area in the endoscope image of the stomach can be obtained.

The invention discloses a handwritten mathematical formula dynamic recognition method based on multi-modal feature fusion, which comprises the following steps of: 1, performing code conversion on stroke track sequence information contained in data by adopting an HSV color model to generate an HSV pseudo-color image, and fusing the HSV pseudo-color image with a data binary image to obtain a time sequence rich image; 2, feature extraction is performed on the time sequence rich image and the stroke track sequence through a convolutional neural network, image features and stroke features are obtained, the stroke features are enhanced by using the image features, and multi-modal features are obtained after the image features and the enhanced stroke features are fused; step 3, enhancing the input multi-modal features by adopting position coding through a decoder, and performing symbol prediction in combination with an attention mechanism and a multi-scale symbol counting module to generate a mathematical formula symbol sequence; and 4, designing rules, and correcting the generated mathematical formula symbol sequence. According to the invention, the recognition accuracy of confusable characters can be improved.

The invention provides a slide scanning bar code identification optimization method and device and a readable storage medium thereof. The method comprises the following steps: carrying out image preprocessing downsampling and carrying out illumination compensation through sub-region mean value estimation; a bar code area is positioned through morphological processing, and accurate angle correction is achieved by combining a minimum enclosing rectangle and Hough straight line clustering; quickly calculating a local mean value and a standard deviation based on the integrogram, and carrying out adaptive binaryzation through a dynamic threshold formula; and after morphological operation is carried out on the binary image, according to a 39-code rule that 10 units / group contains 3 wide strips, counting the black-and-white interval width and reconstructing the bar codes in groups. According to the scheme, the problems of uneven illumination, angle deviation and bar code adhesion loss are solved, the recognition accuracy and robustness are improved, and the method is suitable for rigid carrier bar code recognition.

The invention belongs to the technical field of image processing, and provides an image processing method and an image processing terminal, and the method comprises the following steps: obtaining an original binary image from an image source, and carrying out the preprocessing of the original binary image; performing run-length coding on the preprocessed binary image to obtain an image data result after run-length coding; performing morphological processing on the image in the row direction by using the structural element matrix; re-integrating each row of data processed in the row direction into a complete image representation, and performing morphological processing in the column direction; taking a processing result in the column direction as a binary morphological calculation result of the original binary image; according to the method, the image is independently processed in the row direction and the column direction, specific shapes and structures can be more flexibly extracted and enhanced, proper structural elements are adopted according to specific application requirements, and the processing effect can be optimized; by using the run-length coding to represent the image, the overhead of data storage and transmission can be significantly reduced, and the processing speed is improved.

The invention provides a gait identity information desensitization method and system based on sparse contour hopping. The method comprises the steps that S1, a gait video is deframed into an image sequence, and binarization processing is conducted on the image sequence; s2, extracting identity features in the binary image sequence by using a gait recognition model, and calculating an adversarial disturbance matrix of each image according to a loss function; s3, binary image sequence key frames are selected at equal intervals, and adversarial disturbance meeting the pixel jump constraint is added to the human body contour edge in the key frames; s4, embedding the finally added disturbance value into the binary image sequence to obtain a reversible human body contour image sequence; and S5, pixel color backfilling is carried out on the modified binary image sequence, and a desensitized gait video is obtained. According to the method, the loss function of the image sequence before and after desensitization is maximized by means of the gait recognition model, so that the pixel directly generates binary hopping towards the gradient rising direction, non-directional or directional interference on the gait recognition result is realized, and the identity information privacy is protected.

The invention provides a stacked cell hierarchy judgment method based on contour concavity and convexity. The method comprises the following steps: performing contour extraction on a cell binary image; calculating a convex hull for the contour point set of each cell, and taking the convex hull as an ideal form reference of the cell in an unshielded state; detecting the convexity defect between the contour point set of each cell and the corresponding convex hull, and quantifying the characteristic parameter of each convexity defect; screening the convexity defects according to the characteristic parameters to determine significant recesses, marking cells with at least one significant recess as concave cells, and marking cells without significant recesses as convex cells; screening candidate cell pairs according to the spatial position relationship between the cells; according to the convex-concave attributes of the two cells in the candidate cell pair and the position relation between the coordinates of the center of mass and the remarkable concave area, the shielding relation between the two cells is judged; and constructing an attribute graph by taking each cell as a node and taking the occlusion relationship as an edge, and generating a cell stacking hierarchy sequence through topological sorting.

The invention discloses a defect detection method for a photovoltaic screen printing plate, which comprises the following steps: S1, acquiring an image through image acquisition equipment; s2, performing graying processing on the image obtained in the step S1; s3, carrying out edge detection on the image, and detecting the shape and size of a gap; s4, selecting a structural element, and scanning the image by using the structural element; s5, scanning the whole binary image by using the selected structural elements; the problems that in the prior art, different types of defects cannot be accurately distinguished and recognized, defect classification is wrong, and accurate evaluation of the quality of the photovoltaic panel is affected are solved, the screen printing plate condition is presented from multiple aspects, hidden defects are found more easily, and the detection comprehensiveness is improved; according to the method, different types of defects of the photovoltaic panel are distinguished more finely through extraction of various features, and recognition is carried out by expressing lines and region edges through region feature filling aiming at the following tiny fuzzy defects.