959 results about "Image edge" 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 image defect detection, in particular to a building engineering quality detection and evaluation system based on machine vision, which comprises an image acquisition module, a crack main shaft identification module, a space packaging module, a structure overlapping detection module and a building quality evaluation module. According to the method, a crack boundary layer with a spatial positioning function is constructed through image edge scanning and pixel position marking, pixel segments with high continuity are screened to generate a path line, the continuity and representativeness of crack trend are improved, and a rectangular block is packaged to enhance organization and complete expression of cracks in a spatial level; performing coordinate comparison with a structure stress area to form corresponding association between defect influence and a structure function, judging the influence of the crack on a key area through a space coverage relation between a total block amount and an overlapping amount, and establishing multi-dimensional building quality grade division by combining crack form, position distribution and structure association information; and comprehensive evaluation of building structure defects from geometry to structure level is realized.

The invention relates to the technical field of nondestructive testing, in particular to a leak detection and partial discharge digital imaging detection method and system based on acousto-optic fusion, and the method comprises the following steps: based on channel microphone array sound wave data in a partial discharge signal suspicious region, extracting a sound wave abnormal section, positioning a sound source, matching image edge features, and synchronously marking; and analyzing the phase change of the multi-frequency signal to judge a sound source concentration area, tracking the moving trend of a disturbance point, and outputting an acousto-optic fusion positioning trend track. According to the method, the partial discharge feature recognition sensitivity is improved through high-frequency peak paragraph screening and dominant frequency recognition, the abnormal region positioning precision is enhanced in combination with image edge extraction and sound source space matching, and acousto-optic synchronous positioning and trend trajectory display are achieved through multi-frequency signal phase analysis and image frame disturbance tracking. Through fusion of frequency domain feature extraction, image recognition, dynamic comparison and other actions, the spatial precision of abnormal source recognition and the multi-source fusion analysis efficiency are improved, and the partial discharge traceability and dynamic monitoring capability are enhanced.

The invention relates to the technical field of power transmission line detection, and discloses a power transmission line key component defect identification method based on cloud edge cooperation. The method comprises the following steps: acquiring a multi-source inspection data set consisting of a power transmission line component image acquired by an unmanned aerial vehicle, edge end sensor data and a cloud historical defect database; at the edge end, extracting component region features through an image preprocessing algorithm, and generating environment correlation parameters by using a multi-modal feature fusion algorithm; and at the cloud, performing space-time correlation analysis on the historical defect database to generate a part defect evolution graph. And inputting the information into a cloud edge collaborative recognition model to obtain a component defect feature vector, constructing a multi-stage defect recognition network through a dynamic optimization algorithm, and outputting a component defect classification result and confidence. According to the method and the system, the accuracy, the real-time performance and the reliability of defect identification of the key component of the power transmission line are improved, and the method and the system have good application prospects.

The invention relates to the technical field of automatic driving, in particular to an automatic homeward voyage method for multi-sensor data fusion of an unmanned vehicle, which comprises the following steps: acquiring laser radar obstacle points, image edges and attitude data, completing synchronous registration, identifying space mapping, extracting track and boundary trends, screening forward sections, and judging track offset and trafficability. Screening an error band boundary, adjusting a steering instruction, analyzing path overlapping change, and outputting a path keeping label. According to the method, through time synchronization and space registration of various sensor data, high-precision fusion of the data under a unified space-time framework is realized, the reliability of environment perception is improved, accurate mapping of the data in a unified reference coordinate system is ensured, the reliability of path planning and decision making is enhanced, and the optimal advancing direction is determined by combining a homing point; the accuracy of return route planning is improved, the traffic feasibility is dynamically evaluated, the effective error band boundary is screened to improve the safety, and the reliability of the return route is ensured through track stability analysis.

The invention provides a rock image classification method based on edge enhancement and multi-scale feature fusion. By combining the edge enhancement and multi-scale feature fusion technology, the accuracy of mineral classification in the rock slice image is remarkably improved. According to the method, rock slice image interference is eliminated through filtering and denoising, and mineral particle edges are enhanced by fusing morphological top-hat transformation and a Laplace operator; and performing multi-scale pyramid decomposition on the enhanced image to extract high-frequency information, performing multi-direction response enhancement to generate a direction feature map, and performing channel-level fusion on the original image, the edge enhanced image and the direction feature map. And finally, inputting the fused image into a convolutional neural network to complete rock type classification. According to the method, mineral boundary expression is effectively enhanced, the classification accuracy is improved, and a reliable technical scheme is provided for geological analysis and lithology identification.

A method for measuring the size of a slide block of a guide rail with a complex shape based on intelligent vision belongs to the field of machine vision, and comprises the following steps: DXF file data acquisition: reading by using Python; image acquisition and preprocessing: acquiring image data in real time by using an industrial camera, and performing image preprocessing; hole detection and measurement: adopting a Hough transform algorithm to realize sliding block hole detection, introducing a sub-pixel counting method to improve the algorithm, and realizing aperture high-precision measurement; edge line detection and measurement: extracting edge and line segment features of the sliding block image by adopting Canny edge detection and LSD line segment detection technologies, automatically removing invalid line segments, determining an optimal fitting line segment, and realizing accurate detection and measurement of a target edge line; and detection and measurement result visualization: displaying a sliding block size measurement result in an image, comparing the sliding block size measurement result with a standard parameter of a DXF file, judging the qualification of the sliding block, constructing a visual interface by adopting Pyside2 to record the measurement result, and supporting a plurality of input source detection functions.

The invention relates to the field of phytoplankton image edge detection, in particular to a marine phytoplankton image edge detection method based on a non-local mean denoising mode. The method comprises the following steps: putting a marine phytoplankton image file needing to be detected into a folder of a program item, automatically reading a code into an image in the folder by a program, and loading a to-be-processed image; edge details related to the marine phytoplankton image are reserved based on a non-local mean denoising algorithm; aiming at the image data after the edge details are reserved; carrying out edge extraction on the image based on a Canny edge detection algorithm; and outputting a marine phytoplankton image with edge details reserved. Through combination of non-local mean denoising and Canny edge detection, noise can be more effectively removed, image details and structure information can be kept, and the influence of noise on edge detection is reduced, so that clearer and more accurate edges are extracted; and in an image with relatively large noise, the edge extraction precision and the image quality can be remarkably improved.

The present invention relates to the field of computer vision technology, and specifically to a pathological slice image analysis method and system, comprising the following steps: based on pathological slice image information, combined with image preprocessing, edge information in the image is detected and extracted, color information is analyzed, color distribution characteristics in the image are evaluated, and image edge and color information is generated. In the present invention, by analyzing edge and color information, combined with the extraction of texture features, the identification process of tissue structure is optimized, the identification and segmentation of lesion tissue is made more accurate, the identification and segmentation of lesion tissue is performed using tissue structure feature data, the location of the lesion area and the identification of the type are achieved, and the visual presentation quality of the data and the intuitiveness of the diagnosis are improved by mapping the pathological slice image into a three-dimensional model and annotating it, helping medical professionals to fully understand the pathological conditions in the slice image and make accurate treatment decisions.

The invention discloses a circuit breaker image edge detection method fusing spatial constraint fuzzy clustering and lightweight network optimization, and the method carries out the local adaptive threshold calculation through combining spatial constraint FCM and Otsu algorithms, and optimizes the edge detection process of a Canny operator. According to the method, fuzzy classification is carried out on a circuit breaker image by adopting spatial constraint FCM to obtain a strong marginal probability graph; the circuit breaker image is subjected to block processing through local adaptive threshold calculation, a global threshold is generated through integration, and then the global threshold is input into a Canny operator for accurate edge extraction. In order to further improve the detection effect, a lightweight neural network PiDiNet is used to correct a Canny output image. According to the method, the edge detection precision of the circuit breaker image can be effectively improved, and the method is suitable for edge extraction tasks in high-noise and complex background environments.

The invention discloses a large-size grinding wheel concentricity error measurement method and system based on machine vision, and relates to the technical field of grinding wheel concentricity error measurement, and the method comprises the steps: calibrating an industrial CCD camera through PyCharm software, collecting a part of images of a grinding wheel, and carrying out the RGB three-channel extraction, gray conversion, filtering and binary segmentation processing of the collected images; secondly, a Canny edge operator is adopted to carry out coarse positioning on the edge of the image, and then a novel edge judgment criterion is established to construct an edge detection model with sub-pixel-level precision; and finally, carrying out feature fusion on sub-pixel contours of RGB channels, clustering extracted discrete point sets by using an improved DBSCAN algorithm, and then carrying out ring fitting by using improved RANSAC to realize accurate measurement of the inner and outer diameters and concentricity of the grinding wheel.

The invention relates to the technical field of mining area safety monitoring, in particular to a mining area settlement monitoring method and system based on image data, and the method comprises the steps: collecting a mine hole image in a mining area, and carrying out the edge detection of the mine hole image, and obtaining an edge image; high-definition images and to-be-enhanced images are screened out according to the definition degree of the edge images, and the definition degree reflects the pixel value and the position change degree of pixel points at the same position of two adjacent frames of edge images. According to the method, the supplementary coefficient of each pixel point is calculated by utilizing the characteristics between the high-definition image and the to-be-enhanced image, the to-be-enhanced image is enhanced, so that a complete mine hole edge can be obtained in the to-be-enhanced image, and finally, the time when the mine hole edge is abruptly changed is calculated. According to the difference value between the area of the mine hole edge at the sudden change moment and the area of the mine hole edge at the current moment and the change time, the settlement coefficient of the mine area can be accurately calculated so as to monitor the safety of the mine hole.

The invention discloses a graph neural network embedding method combining a heterogeneous graph and multi-modal data, a medium and equipment, and the method is characterized in that the method comprises the following steps: carrying out the feature initialization of nodes and edges in the heterogeneous graph, employing a text pre-training model, an image convolution network or an audio processing model to initialize the node features according to the node types, carrying out weight distribution and feature coding on the semantic edge, the image edge or the social edge according to the edge type; multi-modal data is mapped to the same potential space through a modal self-adaptive fusion mechanism, weights of different modals are dynamically adjusted, and the modal self-adaptive fusion mechanism comprises construction of a shared embedding space and a multi-head attention mechanism; performing weighted aggregation on node information based on heterogeneous edge types, updating node embedding through propagation of a multi-layer graph neural network, and defining different weighting functions according to the edge types in an aggregation process; and embedding and applying the final node to a downstream task, and optimizing the model through a task-related loss function.

The invention relates to the field of infrared image processing, in particular to an infrared blind pixel detection method based on temperature self-adaption and time-frequency domain synthesis, which comprises the following steps of: traversing an infrared image by using a sliding window to obtain gray values of pixel points around a current pixel point; carrying out image edge judgment by using a Sobel-Otsu algorithm based on working temperature dynamic adjustment, and reserving a non-image edge window to participate in blind pixel judgment; grouping pixel points in the window according to horizontal and vertical directions; calculating the alpha filtering value of the gray scale of each group of pixels; calculating the deviation between the gray value of each pixel in the window and the alpha filtering value of the corresponding row coordinate and column coordinate group; if the deviation is greater than an adaptive threshold, setting the blind pixel as a potential blind pixel, and constructing a potential blind pixel distribution matrix; a time domain accumulation method for pre-screening periodic noise based on FFT is adopted, periodic noise interference is eliminated through frequency domain analysis, and then high-probability blind elements are screened through time domain accumulation; and obtaining a final blind pixel detection result. The method can effectively cope with the working temperature change of the sensor and the noise interference in a complex environment, and remarkably improves the accuracy and anti-interference capability of blind pixel detection.

The invention relates to the field of remote sensing image processing and computer vision, and discloses a satellite image cascade matching method and system based on double-branch context awareness, and the method comprises the following steps: constructing a heterogeneous double-branch feature extraction network, extracting multi-scale detail features through a main feature network, and capturing global semantic information through a context coding network; fusing multi-scale main features and context features to construct a group-related cost body, and adaptively enhancing key region matching response in combination with a channel incentive mechanism; optimizing the cost body by adopting a cross-scale information transfer and multi-dimensional attention fusion strategy, and generating a multi-scale initial disparity map; image edge geometric features and semantic contexts are fused, and high-resolution parallax details are recovered through a multi-stage residual decoder. According to the method, the problem of matching fuzziness of a traditional method in weak texture, repeated structure and parallax abrupt change areas of a satellite image is solved, and the three-dimensional reconstruction precision and the edge detail integrity in a complex scene are remarkably improved.

The invention provides a marine radar image denoising method and device based on a multistage filtering algorithm, and the method comprises the steps: carrying out the preprocessing of a to-be-processed marine radar image, and obtaining a first image; performing edge gradient detection on the first image based on the gradient direction corresponding to each pixel point in the first image and the target identified in the first image, and performing noise detection on the first image based on the gray value corresponding to each pixel point in the first image after the edge gradient detection is passed; after the noise detection is passed, processing the first image by adopting bilateral filtering to obtain a second image; and image enhancement is carried out after image sharpening is carried out on the second image, and a denoised marine radar image is obtained. According to the method, multiple filtering technologies such as Gaussian filtering, median filtering and bilateral filtering are combined, multiple kinds of noise in the marine radar image are effectively removed, especially image edge and target information can be kept in a complex environment, and the target detection and navigation precision is remarkably improved.

The invention records a multi-scale attention and median enhancement semantic segmentation method, and the method comprises the following steps: S1, extracting the initial features of an original image, and forming the output features of an Inception block; s2, inputting the output features into a multi-scale feature enhancement module, and outputting multi-scale features; s3, inputting the multi-scale features into a median enhancement space channel attention module, and outputting an attention feature map; s4, generating a final fusion feature of the high-level feature and the low-level feature through the cross feature fusion block; s5, performing up-sampling on the attention feature map, adding the attention feature map with the final fusion feature, and performing up-sampling to obtain a fusion feature map; s6, performing up-sampling and splicing on the fused feature map, and outputting a spliced feature map; and S7, performing up-sampling and full-connection layer expansion on the spliced feature map to obtain a segmentation prediction map of the original image. According to the method, feature distribution information can be captured more comprehensively, and a clearer and more accurate original image edge segmentation result is obtained.

The invention provides a saliency target detection method and system based on edge detection and an attention mechanism, relates to the technical field of computer vision, and utilizes a target detection network to carry out saliency target detection. The target detection network emphasizes the selectivity and invariance of the features during detection of a salient edge and a salient region through parallel edge branches and salient detection branches; the edge branch performs interactive fusion on the low-level features with spatial structure details and the high-level features with rich semantic knowledge to obtain edge features; the saliency detection branch generates a region feature containing multi-scale key information through multi-scale attention, and improves the interior and boundary of the region feature through an edge guide learning strategy by using edge features as guidance to obtain a final saliency target region; according to the method, edge detection and an attention mechanism are combined, saliency target identification and positioning and image edge definition are better realized, and the accuracy of saliency target identification of the system is improved.

The invention discloses an image edge cutting method and device based on boundary detection, equipment and a storage medium, and relates to the technical field of image boundary detection, and the method comprises the steps: carrying out the preprocessing of an original image, and obtaining a target image; determining a scene type of the target image; based on an edge detection strategy corresponding to the scene type, performing edge detection on the target image to obtain an edge image; performing straight line detection on the edge image to obtain a plurality of candidate straight line segments; and based on a boundary positioning rule, determining a target boundary line segment from the plurality of candidate straight line segments, so as to carry out edge cutting on the original image according to the target boundary line segment. According to the invention, a more reliable image edge cutting effect can be generated.

The invention belongs to the technical field of coal mine underground mining, and discloses a working face roof periodic weighting identification method and system based on a YOLO algorithm. The method comprises the following steps: mapping data into a three-dimensional space to generate a stope working face 3D stress nephogram, and generating a uniformly distributed and visualized stope three-dimensional grid stress distribution nephogram; the high-stress area, the low-stress area and the weighting area are marked with corresponding labels; performing real-time image recognition on the high and low stress areas by adopting a YOLO algorithm, and accurately positioning a roof weighting area; through an image edge detection technology, a time range of a roof weighting area and a corresponding hydraulic support distribution range are extracted, and intelligent analysis of area pressure change is realized. According to the method, image recognition algorithms such as YOLO and the like are combined, so that a coal mine does not depend on a traditional data processing mode any more, potential errors caused by manual intervention are reduced, data processing reliability and real-time performance are greatly improved, and safety monitoring efficiency and accuracy are improved.

The invention discloses a lightweight single-image super-resolution reconstruction method based on local and global feature collaborative enhancement perception, which is used for solving the problems of insufficient utilization of high and low frequency clues in single-image super-resolution, low feature fusion efficiency and difficulty in consideration of structural consistency and visual fidelity of reconstructed images. The network adopts a double-flow heterogeneous architecture; a local branch uses multi-type differential convolution explicit coding image edge and texture prior to enhance detail characterization capability; the global branch effectively models long-range dependency and low-frequency semantic information by integrating local, cross-regional and global multi-level spatial self-attention mechanisms. The frequency sensing fusion module provided by the invention generates a channel specific space weight based on frequency characteristics, self-adaptively fuses double-branch characteristics, accurately balances structure maintenance and detail enhancement requirements in a characteristic fusion process, and effectively reduces characteristic redundancy. According to the super-resolution network, the model complexity is remarkably reduced, high-resolution images which are consistent in structure, vivid in vision and rich in details can be generated while lightweight design is kept, and an effective scheme is provided for efficient and high-performance lightweight single-image super-resolution reconstruction model design.

The invention discloses a BEV perception optimization method based on semantic projection compensation and edge structure enhancement, and the method comprises the steps: constructing an image edge feature pyramid under convolution perception through an MSEII module, and enhancing the boundary significance of a weak texture region; and then image semantic embedding is extracted by using a deep semantic segmentation network, geometric projection is carried out based on camera-radar external participation internal reference, image semantic features are mapped to a point cloud space, and an SPE module is constructed to complete semantic completion and structure alignment. After the image and the point cloud features are fused in a unified BEV coordinate system, a spatial context relation is further modeled through sparse point convolution, and a three-dimensional target bounding box and a semantic tag are output through a decoupling detection head. According to the method, on the basis of guaranteeing model lightweight and deployment efficiency, the long-distance target detection capability and semantic consistency in a shielding scene are remarkably improved, and the method is suitable for multi-mode BEV perception application in other resource-constrained equipment such as a mobile robot.

The invention discloses a cloud type inversion method and system based on cavity window attention, and the method comprises the following steps: obtaining satellite remote sensing image data, extracting multi-dimensional spectral features, converting the latitude and longitude information of an image into spatial features through a position coding module, carrying out the feature splicing of the two features, and obtaining a fusion feature; a convolutional layer is input to extract basic features of image edges, textures and the like, a plurality of cavity window attention modules are stacked, each cavity window attention module comprises a cavity window division module and a weighted attention recovery module, the cavity window attention modules are used for extracting multi-scale image features, image resolution is recovered step by step through up-sampling, feature representation is refined, and a multi-scale image is obtained. According to the method, the features are mapped to the category space, the classification probability is output, cloud type inversion with higher accuracy and robustness is achieved, and an effective solution is provided for cloud information processing in the remote sensing image.

The invention discloses a micro-sensing trapezoidal method, device and equipment based on Aruco recognition and a medium, and relates to the field of computer vision. The method comprises the following steps: S1, projecting an Aruco grid and collecting an image, and extracting marked angular points to construct a position set; s2, calculating a trapezoid tension factor based on the trapezoid area and the opposite side distance, and quantifying local deformation; s3, fusing multi-labeled spatial parallax and tension information, constructing a continuous tension equilibrium domain, and generating a final mapping position through gradient correction basic inverse projection mapping and boundary disturbance compensation; and S4, outputting an inverse perspective matrix based on a fusion result, and estimating a microscopic pose offset parameter for fine adjustment of the projector. And through tension factor and spatial parallax modeling, adaptive correction of a distortion region is realized, the edge consistency and overall symmetry of the image are improved, and the stability and precision of the projected image are enhanced.

The invention discloses a camouflage target segmentation method based on multi-scale cross-spectral feature interaction, and belongs to the technical field of computer vision. The implementation method comprises the following steps: 1, constructing a camouflage target detection network; 2, obtaining visible light and infrared dual-mode features for the network subjected to feature extraction; 3, performing multi-layer feature fusion on the visible light image features and the infrared image features; therefore, bimodal complementary information extraction is realized; 4, constructing an edge sensing network for performing detail processing on the edge of the image so as to obtain an edge prediction result; 5, training the camouflage target detection network to obtain training parameters of the camouflage target detection network; optimizing training parameters of the camouflage target detection network; 6, inputting the image pair into the training optimization camouflage target detection network to obtain a predicted camouflage target detection result; compared with the prior art, the accuracy of boundary fuzzy recognition and appearance similarity recognition of camouflage target detection is improved.

The invention discloses an image edge feature enhancement correction fusion method based on a guide filter, and aims to solve the problems that detail features of a low-illumination visible light image and an infrared image are not obvious, focusing edge information is not clear, and registration of a multi-focus image is wrong. The invention provides an edge feature enhancement correction fusion method based on a guide filter. In the illumination enhancement stage, a visible light image is divided into a base layer and a detail layer through a guide filter, and the image contrast and detail information are enhanced. For an infrared image, an infrared background is reconstructed by using a quadtree decomposition and Bezier interpolation method, unclear focusing edge feature information is extracted, and the visibility of image details is enhanced. And finally, reconstructing the two processed images by using a multi-scale weighted gradient method, and performing fusion by calculating large-scale and small-scale weight scales.

The invention belongs to the field of photovoltaic panel hot plate image processing, and particularly relates to a photovoltaic panel defect detection method fusing multi-scale wavelets and a lightweight attention mechanism. According to the method, an RHDWT discrete wavelet transform module based on multi-scale decomposition is added in a model input stage to strengthen image edge and texture detail representation; a Mix Structure Block module is introduced into a backbone network of the YOLOv11, so that multi-scale features are fused, and the feature expression capability is improved; an LWGA lightweight global attention mechanism is introduced into a neural network connection layer to enhance the context modeling capability, and the detection effect on small target defects such as fine cracks and hot spots is improved. According to the model, through collaborative optimization in three aspects of input preprocessing, feature extraction and an attention mechanism, the precision and robustness of defect detection in a complex photovoltaic module infrared or visible light image are remarkably improved, and the model is suitable for scenes such as high-precision photovoltaic panel image detection and intelligent maintenance.

The invention relates to the technical field of digital photoetching equipment, in particular to an LDI machine image segmentation and exposure splicing method, which comprises the following steps: carrying out primary segmentation on an original complex graph, and carrying out segmentation again after refinement processing; in the exposure process, periodically collecting a gray histogram of an exposure pattern, aligning the exposure images of adjacent areas to form a continuous exposure image, and dynamically adjusting exposure parameters by a light beam modulator according to the characteristics of each second segmentation area to realize closed-loop control on the exposure quality; all exposed images are spliced through a search algorithm and a multi-frame fusion technology, smooth transition of image edges is realized, discontinuity of local features is effectively avoided, the overall consistency and integrity of the spliced images are ensured, the imaging quality and precision are further improved, finally, the spliced images are verified, and the image quality is improved. And seamless splicing of each segmented region is ensured, so that high-quality graphic output is generated.

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention relates to a point cloud-image-based three-dimensional imaging method and system, and belongs to the technical field of optical imaging. Comprising the following steps: inputting an image, and carrying out two-dimensional image instance segmentation on the image to obtain a two-dimensional mask; extracting a point cloud corresponding to the two-dimensional mask from the three-dimensional point cloud data of the image, processing the point cloud and performing three-dimensional modeling to obtain a three-dimensional research target; triangulation is carried out on the three-dimensional research target, and discrete point cloud is converted into a grid structure; projecting the grid structure into a two-dimensional coordinate system, and searching intersection points of edges of triangles in the grid structure and image edge information; wherein the image edge information is obtained by performing edge detection on the two-dimensional research target; the two-dimensional research target is obtained by extracting a two-dimensional mask; and adding the intersection points into the point cloud structure and performing triangulation to complete three-dimensional imaging. According to the method, the three-dimensional space information and the texture features can be obtained at the same time, the difference between the point cloud and the image data scale is reduced, and the loss of the texture information is reduced.