1473 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 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 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 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 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 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 an image contour recognition method and device for a cell cluster, and relates to the technical field of biological image processing, and the method comprises the steps: converting an input cell cluster image into a binary image; carrying out expansion operation and distance transformation on the image after morphological operation, and carrying out subtraction operation on the two obtained images to obtain a third image containing an unknown region; detecting connected regions in the accurate foreground region, and marking each independent connected region, background region and unknown region to obtain a marked image; performing region segmentation on the original image to obtain segmented marked regions, generating a corresponding binary mask image, and extracting the contour of each marked region by using the binary mask image; identifying and filtering the contour of each marked area to obtain an effective contour; the size of each effective profile is converted into an equivalent sphere diameter. According to the invention, accurate segmentation of adjacent or adhesion areas in the image can be efficiently realized, and accurate counting and size measurement of cell clusters can be obtained.

The invention provides a stone gradation automatic detection method based on image processing, belongs to the technical field of image processing, and designs a two-dimensional convolution kernel capable of enhancing features according to the features that small-particle stones are bright in center, dark in edge and approximate to a circle by acquiring a gray image and a background image of a stone field through a video stream. Convolution operation is carried out on the image to improve the contrast ratio of the stone and the background, then an accurate binarized image is obtained through double-threshold segmentation and background difference processing, in order to further separate the adhered particles, the particle center is positioned by adopting distance transformation, and effective segmentation is carried out by combining a watershed algorithm. And according to the extracted particle contour, geometric parameters are calculated and the mass is estimated, so that a stone grading curve for evaluating the filling quality is automatically generated. According to the invention, rapid and non-contact automatic analysis of rockfill material grading is realized.

The invention relates to the technical field of video recognition and analysis, and discloses a video recognition and analysis method based on physical characteristics. Video frame pixels are mapped to a two-dimensional coordinate system with the upper left corner as an original point, and mirror image expansion and median filtering are carried out on a gray level image; constructing a binary image based on a gray threshold value, and analyzing and extracting a target region by using a four-neighborhood connected domain; using neighborhood search and polar angle sorting to close the tracking contour, and generating equidistant re-sampling points based on Euclidean distance and an interpolation method; the curvature of the re-sampling points is estimated through a three-point difference algorithm, and zero denominator is avoided through numerical protection; performing discrete Fourier transform on the curvature sequence to extract a frequency spectrum, and normalizing an amplitude to form a standardized feature vector; and finally, inter-frame similarity is calculated based on the feature vector, and the most similar frame is automatically retrieved. By processing unified data standards in stages, edge noise is suppressed, sampling uniformity is ensured, and feature stability and cross-frame comparability are improved.

The invention provides a road foreign matter detection method based on tunnel inspection robot vision and related equipment, and relates to the technical field of intelligent traffic tunnel inspection robots. The method comprises the following steps: training by using an existing tunnel robot video data set to obtain an ROI region and a road object instance segmentation model; acquiring a video stream data frame of the inspection robot, generating an edge binary image through edge detection, obtaining related mask data through model reasoning, and recording classified foreign matter surrounding frame data; processing and combining various data, judging residual objects through morphological operation and contour detection, and determining unclassified foreign matter bounding box data; then reporting two types of foreign matter bounding box data according to rules; and finally, generating an annotation file according to the unclassified foreign matters, and updating the road object instance segmentation model. By implementing the method, tunnel road foreign matters can be timely and accurately detected, potential safety hazards are reduced, smooth tunnel traffic is guaranteed, and the intelligent and automatic level of tunnel inspection is improved.

The invention discloses a terahertz image registration method fusing morphological characteristics. The terahertz image registration method comprises the following steps: filtering and denoising a terahertz image; performing adaptive threshold segmentation processing on the reference image and the to-be-registered image, and respectively generating three types of images, namely a grayscale image, a binary image and an edge image; extracting texture features, geometric features and contour features from the generated grayscale image, the binary image and the edge image, and carrying out integration and feature description on the extracted feature points; mismatching points are removed through bidirectional feature point matching, registration parameters are calculated based on correct matching points, and image registration and verification are completed. According to the method, the number of extracted feature points in terahertz can be increased, multiple features are fused for registration, the registration precision is improved, image registration can be achieved in similar images, the practicability is high, and the application range is wide.

The invention relates to a pavement crack image skeleton extraction method based on an improved Zhang-Suen refinement algorithm. The method comprises the following steps: S1, carrying out binarization processing on a pavement crack image; s2, carrying out boundary expansion on the crack binary image; s3, carrying out run-length coding on the crack binary image in the horizontal direction, and carrying out compression storage on continuous non-zero pixel segments in each row; s4, using an improved Zhang-Suen refinement algorithm to process the crack binary image processed in the step S3; s5, removing boundary extension; s6, performing simplification processing on the preliminarily refined crack skeleton image by using four predefined 3 * 3 L-shaped structure templates; s7, removing burrs in the image; and S8, outputting a crack skeleton image. The technical problems that according to an existing skeleton extraction method, a topological structure is prone to being damaged, the calculation efficiency is low, the extraction result cannot be completely a skeleton with the single pixel width, and burrs exist in the skeleton extraction result are solved.

The invention provides a GAN-based digital doctor expression, action and emotion interaction simulation system. The system comprises a data set construction module, an audio encoder, an action mask encoder, a Transform converter, a training module and a digital doctor generation module. The data set construction module is used for constructing a high-quality medical scene data set; the audio encoder extracts advanced feature representation from the audio by adopting a pre-training model; the motion mask encoder is used for extracting motion feature information in a binary image with the same size as a video frame; the Transform converter is used for realizing the fusion of the multi-modal data; the training module is used for training the GAN network; and the digital doctor generation module generates a vivid whole body action and expression sequence through a trained generator. According to the system, expressions and actions of the digital doctor can be more accurately generated according to voice dialogues in the current system, and the problems that in a traditional online diagnosis and treatment system, users are not satisfactory, and the expressions of the digital doctor are not generated well are solved.

The invention discloses an intelligent artificial limb movement intention recognition method based on multi-modal information and a D-S evidence theory, and belongs to the technical field of artificial intelligence, mode recognition and intelligent artificial limb control. According to the method, firstly, electromyographic signals and kinematics signals of user limbs and three-dimensional point cloud information of the terrain where the user limbs are located are synchronously collected; filtering, segmenting and time domain / frequency domain feature extraction are carried out on the physiological signals, rotation correction and dimension reduction are carried out on the environment point cloud, and a binary image is generated; using a deep learning model to identify a preliminary motion mode from the physiological features and identify a terrain category from the environment image; and finally, inputting the two recognition results as evidence bodies into an improved D-S evidence theory model for decision-level fusion, and outputting a final comprehensive motion intention recognition result. According to the method, the human physiological signal and the external environment information are fused, so that the ambiguity and uncertainty of single information source identification are effectively solved, and the intention identification accuracy and adaptability of the intelligent artificial limb in different complex scenes are remarkably improved.

The invention discloses a micro-through-hole quality detection method and system based on data model feedback, and relates to the technical field of semiconductor detection, and the method comprises the steps: obtaining micro-through-hole image data, carrying out the gray processing of the micro-through-hole image data, carrying out the binary conversion of a processed gray image, obtaining micro-through-hole binary image data, and carrying out the detection of the micro-through-hole quality. And obtaining two-dimensional binary image data of the micro-through hole according to the binary image data of the micro-through hole. According to the method, the micro-through hole is roughly detected through the shortest diameter of the micro-through hole, the monitoring efficiency is improved, resource waste is avoided, the point cloud spacing information is obtained through the minimum defect size information and the shortest diameter of the micro-through hole, dynamic adjustment of point cloud model construction is achieved, the accuracy of the model is improved, and the accuracy of the model is improved. The micro through hole is further analyzed through the contour curve function of the micro through hole instead of directly comparing the aperture of the micro through hole with the production standard, so that misjudgment of the micro through hole is avoided, and the quality detection efficiency of the micro through hole is improved.

The invention provides a method for eliminating metal artifacts in a cone beam CT (computed tomography) projection domain, which comprises the following steps of: converting a current frame projection image IC into a binarization image IL and an edge detection image IG, marking connected domains in the IL and the IG, and deleting non-metal artifact area connected domains, so that binarization and edge detection results can be mutually verified; the accuracy of metal artifact area detection is improved; obtaining an extended bounding rectangle Ri, ext of each connected domain in the IM, and effectively eliminating metal artifacts according to the extended bounding rectangles Ri, ext; by segmenting the metal artifact area in the projection domain image, the resolution of the image is protected, and the artifact elimination effect is improved.

The invention discloses a space target main body centroid positioning method and system, and belongs to the technical field of space exploration, and the method comprises the steps: extracting a target region grey-scale image Iray from an original grey-scale image, carrying out the binarization, obtaining a binary image Ibin, respectively inhibiting the star point noise in the Iray and Ibin, then rotating the target regions in the Iray and Ibin to the level of a solar wing skeleton, and carrying out the rotation of the target regions in the Iray and Ibin. A grey-scale image Igrot and a binary image Ibrot are obtained; dividing the target area into a plurality of areas along the skeleton direction of the solar wing, calculating comprehensive similarity measurement between each area and other areas according to Irot and Ibrot, and determining the area with the minimum comprehensive similarity measurement as a main body area; if the mass center of the main body area is located on the space target, taking the mass center of the main body area as the main body centroid of the space target; and otherwise, taking the centroid of the space target in the Ibin as the main body centroid of the space target. According to the invention, accurate and stable main body centroid positioning can be carried out on space targets with various shapes and various postures in a complex imaging environment.