1004 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 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 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 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 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 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 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 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 stem cell fusion degree detection method and system based on artificial intelligence, and a storage medium. The method comprises the following steps: carrying out image preprocessing on a cell microscope image; automatically calculating an optimal threshold value by using an image threshold value segmentation algorithm to obtain a cytoplasm mask; median filtering is carried out on the original image to reduce noise, then an adaptive threshold segmentation method is adopted, a local threshold is calculated according to local area gray level distribution, and a cell nucleus binary image is generated; performing connected region marking on the cell nucleus binary image, calculating the area attribute of each region, and performing filtering according to a cell nucleus removal ratio parameter to obtain a cell nucleus mask; performing logic OR operation on the cytoplasm mask and the cell nucleus mask to obtain a complete cell segmentation result; and calculating the fusion degree of the stem cells based on the cell segmentation result. Therefore, the problems of accuracy and consistency of judging the fusion degree of the stem cells by observing microscope images with human eyes in the prior art are solved, and accurate detection of the fusion degree of the stem cells is realized.

The invention belongs to the technical field of image processing, and particularly relates to a casting surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a gray-scale image and a brightness background image of a vortex casting to be detected, and calculating the illumination deviation of each position in the image; segmenting the grey-scale map of the vortex casting to be detected by using an Otsu method to obtain a binary image; constructing a circular structural element according to the width of the optical interference region and the resolution of the imaging equipment; performing corrosion operation on the binary image based on the structural elements to generate a structural saliency image; obtaining a defect saliency value by using a product of a gray value and an illumination deviation in the structure saliency map; and calculating a segmentation threshold value based on the statistical characteristics of the defect-free sample, and identifying the position of which the defect saliency value is greater than the segmentation threshold value as a defect point. Through physical constraint and illumination feature fusion, the problem that a dark vortex structure and a real defect are difficult to distinguish is effectively solved, and the detection precision is improved.

The invention relates to the field of integrated circuit manufacturing, in particular to a light source mask collaborative optimization method and device, and the method comprises the steps: obtaining an initial light source and an initial mask pattern; the initial mask pattern is a binary image; determining the adjusted initial light source and the adjusted initial mask pattern corresponding to the minimum function value of the light source mask collaborative optimization loss function as an optimized light source and an optimized mask pattern; the light source mask collaborative optimization loss function comprises a main pattern edge error item and a sub-resolution auxiliary pattern printing item; obtaining a simulation photoetching image, and judging whether a sub-resolution auxiliary pattern is printed in the simulation photoetching image or not; when the printed sub-resolution auxiliary pattern exists, outputting the latest optimized mask pattern as a process iteration mask pattern; calculating a proportion value; and when the proportion value does not exceed the qualified upper limit value, outputting an optimized light source and process iteration mask pattern. The method can ensure that no sub-resolution auxiliary pattern is printed and a larger process window is reserved.

The invention discloses a monitoring camera image anomaly detection method and system, and relates to the technical field of intelligent monitoring, and the method comprises the steps: obtaining an original video stream, extracting a pixel matrix of a current frame, carrying out the joint coding of the pixel matrix and a motion vector field of an adjacent frame into a quantum state bit vector, inputting the quantum state bit vector into a pre-constructed anomaly detection operator, and carrying out the detection of the anomaly. Executing quantum state evolution calculation in the Hilbert space, and outputting a two-dimensional distribution matrix of each pixel region; integrating the marked binary image and the physical verification conclusion, and when the quantum anomaly probability value reaches the alarm standard and passes the physical verification, outputting an anomaly alarm signal; and the abnormal alarm signal is converted into a confrontation training sample, a cross-scene virtual sample is generated through quantum noise injection, and an abnormal detection operator is dynamically updated. According to the method, the Hilbert space quantum state evolution step is combined, unitary transformation is applied to the quantum state bit vector, the orthogonal projection operation is executed, the accurately quantified abnormal probability distribution matrix is generated, and accurate assessment of the abnormal risk is achieved.

The invention relates to the technical field of flatness detection of metal parts, and discloses a method and a jig for detecting the flatness of a metal part, and the method comprises the steps: generating laser stripes by using a laser stripe emitter for the surface of a steel sheet being detected, and irradiating the surface of the steel sheet at a fixed incident angle; receiving the reflected laser fringes by using an industrial camera; according to the received laser stripes, a flatness judgment strategy is executed, and whether the surface of the steel sheet is flat or not is judged; if it is judged that the surface of the steel sheet is uneven, a color image of laser stripes shot by the industrial camera is obtained, and the color image is converted into a grayscale image; obtaining a denoised grayscale image, executing an edge gradient calculation strategy, calculating the gradient feature of each pixel, and obtaining a binary image of the laser stripes; and executing a flatness quantification strategy according to the binary image of the laser stripe, and quantifying a flatness index into regression data. And the efficiency and the precision of detecting the flatness of the metal piece are improved.

The invention relates to a star map stray light processing method based on multidirectional gradient, belongs to the technical field of space situation awareness, and solves the technical problems of low processing speed and inaccurate target edge information processing when the existing method is used for processing stray light under a complex illumination condition. Comprising the following steps: performing multidirectional gradient calculation on each pixel point of an original star map image containing stray light to obtain a multidirectional gradient value of each pixel point, and performing normalization processing to obtain a normalized gradient image; carrying out adaptive threshold segmentation on the normalized gradient image to obtain a binary image; performing morphological closed operation on the binarized image to obtain a contour image after the closed operation; and carrying out contour filling on the contour image after the closed operation, and carrying out mask processing to obtain a pure star map image with a reserved target area. The influence of strong stray light on target detection is effectively eliminated, and the precision and robustness of star map image target positioning are improved.

The invention discloses an OCR-based medical document intelligent identification method and system, and relates to the technical field of document identification, and the method comprises the steps: collecting a medical document image through a mobile terminal, generating a binary image based on the collected image through U-Net in combination with multi-scale feature fusion and an attention mechanism, and cutting the binary image; based on the cut binary image, text information is extracted through an OCR model, and a structured field is extracted according to the typesetting rule and geometric distribution of the medical document; and performing deterministic rule judgment and risk assessment on the structured data. According to the method, the image calculation complexity is reduced through standard graying processing, the text region feature extraction precision is improved by fusing a U-Net structure of a CBAM attention mechanism, effective fusion and noise suppression of multi-scale features are realized in combination with Attention Gate, text direction correction is realized in combination with Hough transform, and the text detection robustness and recognition accuracy are improved.

The invention relates to the technical field of image recognition, and provides a PCB edge size detection method and system based on image recognition. The method comprises the following steps: splicing a plurality of PCB units into an integrated jointed board; after the image is transmitted to a detection station, an optical sensor triggers an industrial camera to acquire a panoramic image; after the image is preprocessed, Canny is adopted to identify the contour, and a binary image is generated; performing multi-dimensional dimension measurement, comparing with a CAD drawing, and positioning out-of-tolerance coordinates; the out-of-tolerance coordinates are converted into physical coordinates, and defect units are recognized and identified; and during board splitting, defective units are independently scrapped, and the PCB without defects is output. The technical problem that the whole board is scrapped due to the fact that the defective units cannot be accurately positioned in the board splitting post-processing stage in traditional PCB jointed board production is solved, accurate positioning of the defective units is achieved through optical detection and coordinate conversion, and the defective units are independently scrapped in combination with the board splitting process; the treatment efficiency after board separation is improved; and the production cost is reduced.

The invention relates to the technical field of measurement and detection, in particular to a test sieve calibration method based on machine vision. Comprising the following steps: acquiring a test screen image by a microscope, firstly performing graying processing on an original image, and converting a color image into a grayscale image; the method comprises the following steps: carrying out binarization processing on a grey-scale image, carrying out binarization on the image, setting a grey-scale value of a pixel point on the image to be 0 or 255, enabling the whole image to present an obvious visual effect which is only black and white, and better analyzing the shape and the contour of an object through binarization; and performing connected domain analysis on the binary image, finding a pixel point to which each sieve hole belongs, endowing each pixel point with a label through the connected domain analysis, and forming a connected domain by the pixel points with the same label value so as to realize segmentation of the region of interest. The method is applied to the measurement calibration work of the test sieve, the working efficiency of verification and calibration personnel can be greatly improved by using the method, and human resources are saved.

The invention relates to the technical field of intelligent control, and discloses a hip joint exoskeleton control method and system based on gait mode prediction and gait phase estimation, a terminal and a storage medium, and the method comprises the steps: constructing and training an initial unsupervised terrain detection model, and obtaining a target unsupervised terrain detection model; inputting the environment binary image into a target unsupervised terrain detection model to obtain a terrain detection result, and obtaining a gait mode prediction result based on a majority voting strategy; according to the hip joint angular velocity, target gait phase estimation is obtained based on a self-adaptive oscillator system; on the basis of the expected auxiliary torque curve, net auxiliary torque is obtained according to the gait mode prediction result and target gait phase estimation, and hip joint exoskeleton assistance modes are switched according to the net auxiliary torque. According to the method, marking of a large amount of data is avoided through the unsupervised terrain detection model, terrain detection and exoskeleton control are combined, and the terrain self-adaptive walking capacity of the exoskeleton is enhanced.

The invention discloses a machine vision-based micro-plastic intelligent identification and measurement method, which comprises the following steps: filtering a water sample containing micro-plastic or not containing micro-plastic, and acquiring images under a uniform amplification factor by using a microscope to obtain a micro-plastic image to be detected and a contrast image; calculating the absolute value of the color centroid value of the contrast image and the color difference value of the micro-plastic image, generating a difference image, and carrying out denoising, graying and binarization processing on the difference image to obtain a binary image with highlighted micro-plastic; extracting pixel coordinates of the micro-plastics, setting a threshold value to carry out connected clustering, and distinguishing the micro-plastics and image impurities in combination with a preset micro-plastic recognition threshold value to realize micro-plastic recognition and quantity statistics; and further shooting a standard objective lens micrometer image, calculating the actual physical size represented by a single pixel, and automatically calculating the area and the length of the micro-plastic by combining each clustering enclosing rectangle and the number of the pixels. The method is accurate in recognition and high in operation automation degree, and can be widely applied to intelligent recognition and analysis of water body micro-plastics.

The invention provides a workpiece surface target merging and partitioning method, system and device, and relates to the technical field of image processing, and the workpiece surface target merging and partitioning method comprises the steps: obtaining a surface image of a target region of a target maintenance workpiece; performing graph sampling and visual processing on the surface image to generate a binary image of the target area, and analyzing the binary image to obtain a plurality of connected domains of the target area; performing convex hull generation according to the connected domains to obtain a convex hull corresponding to each connected domain; generating a rotation enclosing rectangle set according to the leading edge vector of each convex hull; and through a variant ant colony optimization algorithm, carrying out global optimization on the rotating enclosing rectangle set, and generating a combined partition of the target area. According to the invention, through accurate capturing of the original image and subsequent layer-by-layer optimization, the redundancy of the laser maintenance path is reduced, and the operation cost is reduced; through construction and global optimization of a high-adaptation form carrier, precise coverage of a complex form target is realized, and the maintenance precision is improved.