190 results about "Region growing algorithm" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention provides an engineering construction defect automatic detection and classification method based on deep learning, and the method comprises the steps: obtaining a welding seam surface image through the shooting of an unmanned plane, and carrying out the denoising and illumination normalization processing of the welding seam surface image, and obtaining a standardized image; welding seam surface texture features are extracted from the standardized image, a convolutional neural network is adopted to analyze the spatial distribution characteristics of textures, and vectorization processing is carried out to obtain texture feature vectors; segmenting a weld surface corresponding to abnormal region distribution by adopting a region growing algorithm, and analyzing pore and weld discontinuity in combination with the texture feature vector to obtain a defect candidate region; performing threshold division on the sizes and the numbers of the defects according to the defect types and the feature vectors of the candidate regions to obtain a severity grading result of each type of defects; and severity features are extracted from a grading result, and a Bayesian network is adopted to fuse texture feature vectors and defect type labels to obtain a welding quality evaluation score.

The present invention belongs to the technical field of digital mine safety, and particularly relates to a blasting area surface morphology inversion method based on an unmanned aerial vehicle. The method comprises: S01, on-site data collection; S02, blasting area morphology inversion; S03, a muck pile throw distance; and S04, a muck pile surface fragmentation distribution. In the present invention, oblique photography by an unmanned aerial vehicle is used, and three-dimensional model reconstruction is performed by capturing a blasting area image, so that a feasible aerial survey scheme is formulated, and the integrity of collected data is good; and reverse modeling of the blasting area is performed by means of the steps of feature point extraction, spatial information conversion, point cloud generation, grid generation, etc.; voxel grid downsampling, point cloud matching and error detection are used to perform registration on point clouds of a target area before and after blasting, so that a good effect is achieved; and a color-based region growing algorithm is used to perform coarse segmentation of point cloud features on an ore-rock block on the surface of a muck pile, and a PointNet++ algorithm is used to perform fine segmentation of point cloud features on the ore-rock block on the surface of the muck pile, so that the muck pile throw distance and the muck pile surface fragmentation distribution are calculated.

The invention discloses an SMT welding spot defect detection method based on image data. The method comprises the following steps: S1, acquiring an image of an SMT welding spot area; s2, carrying out image preprocessing; s3, carrying out welding spot region segmentation by adopting a self-adaptive region growing algorithm, and carrying out modeling on a welding spot region contour in combination with a boundary fitting optimization algorithm to generate a welding spot contour model; s4, multi-level comprehensive features are extracted from the welding spot area, and the multi-level comprehensive features are constructed into a feature vector set; and S5, inputting the feature vector set into a welding spot defect detection network, predicting a defect mode existing in the current batch of welding spots by adopting an improved ProtoNet model, and completing classification and identification of welding spot defects by adopting adaptive feature filtering. According to the method, the adaptive region growing algorithm and the improved ProtoNet model are adopted, accurate detection of the welding spot defects is achieved, and the method has the advantages of being high in detection precision, high in adaptability and good in robustness.

The invention relates to the technical field of foamed silicone rubber surface detection, and discloses a foamed silicone rubber surface detection method based on image visual identification, which comprises the following steps: acquiring a color image of a foamed silicone rubber surface, carrying out graying and normalization processing, and generating a diffuse reflection effective detection area by using a polarization filter difference technology; color abnormity is detected based on an image gridding and gray statistical method, meanwhile, pore and microbubble defects are detected in combination with minimum value seed point extraction and an eight-neighborhood region growing algorithm, finally, various masks are fused, the defect area and number are counted, and a quality inspection report is generated; the method can effectively inhibit highlight interference and accurately identify uneven colors and pore microbubbles, the detection process does not need external training, calculation is efficient, and the method is suitable for rapid detection and quality control of surface defects of foamed silicone rubber.

The invention discloses a desertification monitoring method and system based on multi-source remote sensing data fusion, and the method comprises the steps: carrying out the weighted scoring of multi-source satellite remote sensing data according to the spectrum, cloud coverage and time resolution, and automatically switching to a data source with a higher priority when the score is lower than a threshold value. Thirdly, performing initial segmentation on the image by using a multi-scale segmentation algorithm in combination with spectrum and texture features, optimizing boundaries through a region growing algorithm, and merging regions with small spectrum differences into desertification plaques; and extracting construction land expansion and agricultural activity intensity indexes from the time series data, and establishing a quantitative relation model with desertification plaque change. And adopting a Kriging space-time interpolation algorithm for missing data to generate a continuous curved surface. And finally, fusing a multi-period segmentation result and human factor data, and outputting a desertification boundary dynamic change diagram and a human influence weight distribution diagram. According to the invention, accurate monitoring of the desertification process and quantitative evaluation of human influence are realized, and a scientific basis is provided for desertification control.

The invention provides a prefabricated dish semi-finished product defect identification method and system with an AI algorithm, and the method comprises the steps: extracting semantic features from a refined defect candidate region set, carrying out the feature mapping of each region through a deep convolutional network according to the demands of atypical defect identification, and obtaining the defect description represented by a high-dimensional feature vector; marking original image data through a final defect identification result, and for inhibition of complex background interference, performing outward expansion from defect edge features by adopting a region growing algorithm to obtain complete defect region boundary information; after complete defect area boundary information is obtained, defect distribution changes of continuous batches of images are compared through a time sequence analysis method according to the monitoring requirement of production process fluctuation, and the quantitative basis of process adjustment is determined.

The invention relates to the technical field of remote sensing geological prediction, in particular to a slope support stability prediction method and system based on remote sensing data, and the method comprises the steps: obtaining a multispectral remote sensing image of a target slope region, and generating a vegetation mask through employing a normalized vegetation index; removing interference pixels of a vegetation coverage area in combination with morphological filtering and connected area analysis, then identifying support structure features through an improved edge detection algorithm, delimiting an influence area by adopting a region growing algorithm based on machine learning, inputting an area image into a convolutional neural network model, and obtaining an image of the support structure; a deformation probability graph is output by using a data enhancement technology and a weight optimization layer, finally, a threshold value is determined according to historical data, slope support stability categories are divided in combination with spatial neighborhood information and a voting mechanism, vegetation interference is effectively eliminated, a support structure is accurately recognized, prediction precision is improved, slope support stability can be accurately judged in time, and the method is suitable for popularization and application. And a reliable basis is provided for early warning and protection of slope disasters.

The invention relates to a historical building disease diagnosis method and device assisted by artificial intelligence, equipment and a medium. The method comprises the steps of obtaining a multi-angle image sequence of a historical building and performing preprocessing to obtain a standardized building structure image, performing edge feature extraction on the standardized image to identify dislocation feature points to generate an edge information graph, and segmenting a dislocation region through a region growing algorithm based on the edge information graph to generate an initial dislocation region distribution graph. Performing deformation degree and displacement calculation on each region in the distribution diagram to determine a dislocation severity level, comparing dislocation region changes in each period through a time sequence analysis algorithm by combining an image sequence to generate a dynamic change mode, and optimizing boundary division according to the severity level and the dynamic change mode to generate a diagnosis report; automatic identification and evolution tracking of historical building dislocation diseases are realized, the problems of high subjectivity, fuzzy boundary judgment and lack of disease evolution prediction of traditional manual investigation are solved, and the scientificity and long-term effectiveness of repair decision are improved.

The invention relates to an insulator image segmentation method based on an infrared enhancement image of an unmanned aerial vehicle, and relates to the technical field of image processing, and the method comprises the steps: collecting an infrared image sequence through an infrared enhancement camera, obtaining a standard infrared image sequence, and carrying out the state recognition through an image perception prior engine, outputting a predicted insulator attention heat map and a predicted boundary confidence map; optimizing and adjusting the initial threshold segmentation algorithm and the initial region growing algorithm, and obtaining an adaptive threshold segmentation algorithm and an adaptive region growing algorithm; and carrying out image segmentation on the standard infrared image sequence to output an insulator image sequence, and carrying out early warning judgment on the insulator image sequence based on an adaptive early warning mechanism. The method solves the problems that a traditional insulator image segmentation method cannot effectively deal with the problems of low infrared image temperature contrast ratio, large noise interference and complex background, so that the insulator segmentation is easy to cause mistaken segmentation and missing segmentation, and the high-precision requirement of fault detection is difficult to meet.

The invention belongs to the technical field of image processing, and particularly relates to an urban garden insect pest early warning method and an early warning system thereof, which can automatically identify an insect pest area in an image by collecting a plant surface image and sequentially carrying out de-noising processing, image enhancement, edge detection, texture analysis, feature extraction and mode matching. And a region growing algorithm is adopted to extract an insect pest influence range, and an insect pest area proportion is calculated, so that quantitative judgment of the insect pest degree is realized. And on the basis, early warning data is generated in combination with the geographical position information and is pushed to the terminal equipment through the network, so that a manager can timely obtain the occurrence position and severity of the insect pests, and the insect pest response efficiency and management precision are improved. Therefore, according to the scheme, the problems that in the prior art, insect pest recognition depends on manpower, the recognition accuracy is low, and early warning is not timely are solved, and intelligent and automatic monitoring and early warning of urban garden insect pests are achieved.

The invention relates to the technical field of vertical shaft guide detection, in particular to an industrial intelligent visual detection and diagnosis system for a vertical shaft guide. The coal dust shadow quantization unit extracts a coal dust region by adopting a double-peak self-adaptive threshold segmentation combined region growing algorithm, and introduces a space weight factor to calculate an average gray value and a coverage area proportion, and the dynamic coupling calibration unit depends on a three-dimensional lookup table calibrated by multiple working conditions and nonlinear interpolation; the optimal deviation compensation coefficient of the jitter and coal dust combination is matched, a dynamic gray segmentation threshold value is generated, and a defect diagnosis decision unit accurately calculates the abrasion depth through partition self-adaptive segmentation, multi-scale morphological filtering and denoising and gradient amplitude and curvature double-constrained sub-pixel edge detection. A hierarchical control instruction is output according to the real-time depth and the historical trend, coupling interference is eliminated, the cage guide abrasion detection accuracy is improved, and a reliable basis is provided for safe operation and maintenance.

The invention provides an intelligent labeling method for digital slices at a browser side and application of the intelligent labeling method, and relates to the technical field of medical image processing. The invention aims to solve the problem of poor interaction experience caused by data privacy risk and network delay due to dependence on a back-end server in the prior art. The method comprises the following steps: determining a seed point in response to an interactive operation of a user at a browser end, and dynamically loading a local slice image based on a spatial index; and calling a GPU (Graphics Processing Unit) by utilizing WebGL to calculate pixel similarity in parallel, adaptively adjusting a similarity threshold according to a current view zooming level, generating a segmentation mask in combination with a scanning line region growing algorithm, and performing vectorization rendering. According to the method, data does not need to be uploaded to a server, pure front-end, lightweight and real-time intelligent labeling of the GB-level high-resolution pathological section is achieved, data privacy is effectively guaranteed, and labeling efficiency is improved.

The invention discloses an image feature processing method for fire source detection in a dynamic scene, and belongs to the field of image feature processing. Comprising the following steps: acquiring an original infrared image of a dynamic scene, and preprocessing to obtain a normalized infrared image; generating a binary image through a dynamic region segmentation algorithm; generating a weighted feature image through a radiation gradient weighted feature extraction algorithm; generating a dynamic enhanced feature image through a dynamic boundary feature enhancement algorithm; carrying out region growth by taking edge pixels as seed points through an edge-guided region growth algorithm, and finally outputting a complete fire source region image; a self-adaptive threshold segmentation method is adopted, accurate segmentation of a high-temperature area and a background area is achieved based on local temperature statistical characteristics, the segmentation robustness is remarkably improved by dynamically adjusting a threshold and adapting to temperature distribution changes of different scenes, the high-temperature area and the background area can be accurately distinguished in a complex dynamic scene, and the segmentation accuracy is improved. And the influence of environmental interference on fire source detection is reduced.

The invention provides a method, apparatus, and storage medium for reconstructing three-dimensional models of buildings based on missing point cloud data. The method includes integrating image-based point cloud generation, neural network techniques, and skeleton line extraction methods, offering a novel approach to handling missing point cloud data. The generation of point cloud data is achieved using principles of Structure from Motion based on video or panoramic image data. The point cloud is sampled and segmented using a region growing algorithm. A neural network based on PointNet is constructed, utilizing cross-entropy loss functions to assess the missing points in the point cloud. For mapping high-confidence point clouds from sampled points, Truth Points is employed to complete the entire process of real-world three-dimensional reconstruction. The integration of images into the three-dimensional scene is achieved with strict geometric relationships.

The invention relates to an example-based clothing template automatic generation method and device. The method comprises the following steps: firstly, constructing a standard clothing model database containing semantic tags based on a graph structure; constructing a graph neural network segmentation framework based on a surface adjacent graph, converting the triangular mesh model into graph structure data, and constructing a surface patch classification probability prediction model; then training the framework and semantic tag prediction, iteratively optimizing semantic tags by adopting a graph cut optimization algorithm, and clustering to form an independent cut piece set through a region growing algorithm based on the optimized tags; performing least square conformal parameterization on each cutting piece to obtain an initial two-dimensional sample plate, and constructing a geometric constraint energy function containing fabric characteristics and sample plate process requirements; and finally, solving a four-dimensional comprehensive optimization energy function containing physical characteristics and geometric constraints, and obtaining a final sample plate by minimizing the function. According to the method, highly-automatic end-to-end model generation is achieved, and the industrial platemaking standard is met more accurately.

The invention discloses a camera potential safety hazard patrol identification method based on video frame image segmentation, and belongs to the technical field of image processing. The method comprises the following steps: acquiring data through a video cloud storage system, classifying the data to form a basic data pool, and selecting a target video source set by combining a daily safety patrol mechanism and a special safety patrol mechanism; a region growing algorithm and a U-Net network are adopted to carry out image segmentation and feature extraction, and judgment and recognition operations are executed based on different selection mechanisms. And meanwhile, dynamically evaluating and updating a potential safety hazard list item and an easy-to-occur time period, generating a potential hazard report record and optimizing a patrol strategy. According to the invention, potential safety hazards in the construction environment of the construction site can be comprehensively and automatically identified, and the early warning accuracy and efficiency are improved.

The invention discloses a post-processing method for a binocular point cloud map containing low-speed dynamic obstacles, the post-processing method can effectively remove point cloud data of the low-speed dynamic obstacles in the point cloud map, and the post-processing method specifically comprises the following steps: S1, mapping in a working scene containing the low-speed dynamic obstacles, obtaining a prior map and data of a working scene; s2, constructing an auxiliary map by walking the same path again according to the mapping completion trajectory; s3, selecting a region of interest for each frame of auxiliary point cloud data and corresponding point cloud data in the prior map, then performing grid division on the region of interest and calculating a height difference descriptor; s4, comparing the height difference descriptor in the prior map with the height difference descriptor of the auxiliary point cloud data at the same position, and if the height difference descriptor of the auxiliary point cloud data is far smaller than the height difference descriptor in the prior map, considering that the point cloud data existing in the current prior map is low-speed dynamic obstacle point cloud data; and S5, performing ground fitting by adopting a region growing algorithm and a principal component analysis method, and deleting point cloud data on the ground in the current grid. According to the method, the point cloud data of the low-speed dynamic obstacles can be effectively removed from the point cloud map containing the low-speed dynamic obstacles, and more accurate and more real conditions are created for tasks such as working path planning of a subsequent robot.

The invention relates to the technical field of image processing, and discloses a port loading and unloading risk identification method and system based on image processing. The method comprises the following steps: acquiring standardized image data through multispectral image acquisition and sea wind disturbance compensation processing, extracting multidimensional risk characteristics of suspension arm swinging, cargo deviation and personnel violation, performing dual evaluation of collision trajectory prediction and violation behavior detection, identifying comprehensive potential safety hazards by adopting a spatio-temporal correlation adaptive region growth algorithm, and determining whether the potential safety hazards exist or not. And generating port loading and unloading composite risk early warning information. The technical problems that multiple risk factors are difficult to accurately identify and the composite risk state cannot be effectively predicted in a complex marine environment in port loading and unloading operation are solved, and the environmental adaptability of port loading and unloading risk identification and the accuracy of composite risk assessment are remarkably improved.

The invention relates to the technical field of medical imaging omics analysis, in particular to a pancreatic perivascular fat image feature-based operation area inflammation degree grading method, which comprises the following steps of: determining clinical risk factors for pancreatic operation area inflammation degree grading through a statistical method; respectively segmenting ROI (Region of Interest) 1-6 in the preprocessed CT vein phase image and the preprocessed vein phase image through the combination of a TotalSegmentor segmentation model, an nnUNet segmentation framework and a region growing algorithm, and extracting cross-region image omics characteristics; and constructing an inflammation degree grading model based on the cross-regional radiomics characteristics and the clinical risk factors through a plurality of machine learning algorithms. According to the method, in the fusion model constructed by combining the risk factors and the radiomics characteristics, the clinical risk factors are found by using retrospective research, and meanwhile, the clinical risk factors and the radiomics characteristics are spliced by adopting an attention mechanism, so that the grading precision and efficiency of the inflammatory degree of the fusion model are ensured.

The invention provides an electrical equipment infrared image anomaly recognition method and system based on parallel operation characteristics, and belongs to the technical field of image anomaly recognition. And processing the obtained infrared image by using a pre-trained anomaly identification model to obtain an identification result of whether the electrical equipment is abnormal or not. According to the invention, a YOLO target detection algorithm is utilized to accurately segment key parts of equipment, and then an improved region growing algorithm is combined to position an abnormal temperature region in an infrared image. By fully utilizing the high consistency of the two parallel devices in electrical response and thermodynamic behavior, the local temperature abnormal region can be accurately identified even under the condition of no fault sample, and a new technical support is provided for online state monitoring and intelligent operation and maintenance of the electrical device.

The invention discloses a DBSCAN unmanned aerial vehicle path planning method based on depth estimation and optical flow analysis, and relates to the technical field of unmanned aerial vehicle autonomous navigation, and the method comprises the steps: extracting an image depth feature through a VGG16 model, carrying out the enhancement and segmentation of an image through Gamma transformation and a region growing algorithm, optimizing a seed group through optical flow analysis and a conditional random field algorithm, and carrying out the optimization of the depth feature of the image. A DBSCAN algorithm is used for carrying out path clustering, an A * algorithm is used for carrying out obstacle avoidance path planning, and a double-loop PID control algorithm is combined for execution. According to the invention, through fusion of multi-modal data and path clustering optimization, navigation precision and robustness in a complex environment are improved, seed group dynamic updating is realized based on seed probability calculation and optical flow analysis of a conditional random field, sensing precision, environmental adaptability and real-time reaction capability are improved, and through combination of double-loop PID control and an A * algorithm, the real-time response capability of the system is improved. And the real-time obstacle avoidance capability is optimized.

The invention provides a target identification method and device, electronic equipment and a storage medium. The method comprises the following steps: screening seed points according to a first distance between a second pixel point and a central point of a target mask area obtained by original point cloud projection and a depth value of an original point corresponding to the second pixel point; determining an adaptive search radius according to a second distance between the seed point and the original point corresponding to each second pixel point; carrying out adaptive region growth based on the adaptive seed point and the search radius to search the original point on the target; and determining a three-dimensional perception result of the target based on all the searched original points. According to the method, the adaptive seed point and the search radius can be obtained based on the characteristics of different targets, and an adaptive region growing algorithm is formed; therefore, the search requirements of different targets are met, and the target identification precision and speed are improved.

The invention provides an intelligent design method for a subway room layout based on Stable Diffusion. The method comprises the following steps: carrying out feature extraction on multiple types of plane drawings of actual engineering, generating a space division heat map based on a room center point by utilizing a region growing algorithm, and constructing a heat map-layout sketch pairing data set; a two-stage training strategy is adopted, a ControlNet model is firstly trained to learn a spatial constraint relation, and then LoRA is trained to carry out efficient fine tuning on parameters; room agent points are set according to design requirements, a heat map is generated through a region growing algorithm, and the trained ControlNet model is combined with LoRA to achieve accurate translation from the heat map to an arrangement diagram. According to the method, the condition control capability of the ControlNet and the parameter efficient fine tuning advantage of the LoRA are creatively fused, the generation quality is guaranteed, meanwhile, the computing resource requirement is remarkably reduced, and the design efficiency and the resource utilization rate are improved.

The invention relates to the technical field of digital X-ray image processing, in particular to a defect extraction and three-dimensional reconstruction method for a pipeline digital X-ray image. The method comprises the following steps: acquiring an X-ray original image of a pipeline welding seam and carrying out graying processing to obtain a grayscale image; performing noise reduction processing on the grayscale image; performing contrast enhancement processing on the grayscale image after noise reduction processing by using a contrast-limited adaptive histogram equalization algorithm; carrying out edge feature extraction, and segmenting the defect region based on a region growing algorithm to obtain sub-segmented images; performing defect three-dimensional reconstruction based on pipeline geometric parameters, and mapping two-dimensional defect points on the sub-segmented images to a three-dimensional pipeline wall; and identifying and calculating the defect after three-dimensional reconstruction, and determining the type and size of the defect. According to the defect extraction and three-dimensional reconstruction method, noise suppression, edge reservation and geometric distortion correction can be realized at the same time, and the accuracy of pipeline weld defect detection can be remarkably improved.

The invention discloses a steel pipe defect detection method based on artificial intelligence, and belongs to the technical field of pipe detection, and the method comprises the following steps: S1, obtaining original data from a steel pipe surface image, extracting initial features through a convolutional neural network, and generating a first feature set; s2, aiming at the first feature set, adopting an adaptive threshold segmentation algorithm to generate a second feature set; s3, according to the second feature set, performing preliminary division on the defect region through a region growing algorithm to obtain a plurality of defect region images; s4, extracting local texture features of each defect area image, and generating a third feature set corresponding to each defect area image; and S5, for the third feature set, performing defect type classification by adopting a pre-trained support vector machine classifier to obtain a defect type classification result. The steel pipe defect detection method based on artificial intelligence solves the problem that the precision and robustness of current steel pipe surface defect detection are difficult to improve.

The application provides an electrical equipment infrared image abnormality recognition method and system based on parallel operation characteristics, belongs to the technical field of image abnormality recognition, and acquires an infrared image of electrical equipment in parallel operation; an acquired infrared image is processed by using a pre-trained abnormality recognition model to obtain a recognition result of whether the electrical equipment is abnormal or not. The application utilizes a YOLO target detection algorithm to accurately segment key components of equipment, and then combines an improved region growing algorithm to position an abnormal temperature region in the infrared image. By fully utilizing the high consistency of two parallel equipment in electrical response and thermodynamic behavior, even under the condition of no fault sample, a local temperature abnormality region can be accurately recognized, and new technical support is provided for online state monitoring and intelligent operation and maintenance of electrical equipment.

The invention provides an unstructured road segmentation method based on region growth and Gaussian process regression. The method comprises the following steps: preprocessing an image by adopting Gaussian filtering; converting the RGB color features of the image into HSV color features; selecting a middle triangular region below the image as a road region, and randomly sampling HSV color features of m samples in the region as road region samples; sampling HSV color features of n samples in a non-road area as non-road area samples, and combining the HSV color features to form a training set; training the model by using a training set, and optimizing model hyper-parameters by using an elastic back propagation algorithm; road region segmentation is realized based on a region growing algorithm of pixel blocks; and carrying out hole filling on the segmented road area by adopting a flooding filling method, and visualizing the segmented road area and the boundary on the original image. According to the method, the unstructured road can be accurately recognized, and meanwhile, the problems that a traditional region growing algorithm is large in randomness and calculation amount and needs to manually set a segmentation threshold value are solved.

The invention discloses a dangerous rock mass discontinuous edge point extraction method and device based on improved central axis transformation. The method comprises the following steps: acquiring discontinuous edge line candidate points of a rock mass from three-dimensional point cloud data with a normal in a target area; performing point cloud blocking on the three-dimensional point cloud data to obtain three-dimensional point cloud cluster data; calculating the curvature of discontinuous edge line candidate points in the selected three-dimensional point cloud cluster data, and separating the three-dimensional point cloud cluster data based on a region growing algorithm to obtain a separation region corresponding to each piece of three-dimensional point cloud cluster data; extracting three-dimensional point cloud cluster data and three-dimensional boundary point clouds of the partition areas; and fusing the three-dimensional point cloud cluster data and the three-dimensional boundary point clouds of the separated areas, and carrying out filtering to obtain a final dangerous rock mass discontinuous line. According to the method, the precision and reliability of discontinuous feature extraction of the dangerous rock mass are remarkably improved, the harsh requirement for the uniformity of initial data is reduced, and the robustness and universality under complex geological conditions and different acquisition environments are improved.

The invention discloses a highway meteorological data acquisition method and system based on meteorological bulletin images, and belongs to the technical field of meteorological data processing and geographic information systems. The method comprises the following steps: acquiring a meteorological bulletin image from the China Meteorological Bureau, and extracting effective meteorological data through preprocessing operations such as image boundary determination, scale-pixel conversion and data denoising; converting image pixel coordinates into latitude and longitude coordinates, and superposing the latitude and longitude coordinates with a road vector map to generate a road weather chart; identifying meteorological values based on a color threshold method, and extracting a longitude and latitude sequence of a target road section in combination with a region growing algorithm; and calculating continuous meteorological data by adopting a point-by-point interpolation method, and finally displaying meteorological distribution along the highway through a visualization technology. The problems that an existing meteorological data acquisition mode is high in cost, complex in operation and low in reliability are solved, low-cost and high-precision automatic extraction of the highway meteorological data is achieved, and real-time data support is provided for traffic management and disaster early warning. The system can be widely applied to the fields of highway meteorological monitoring, geological disaster early warning and the like.