179 results about "Boundary contour" patented technology

The invention discloses a medical image segmentation method based on high-resolution modal guidance and cross-modal boundary perception, and the method comprises the steps: carrying out the data preprocessing and enhancement of multi-contrast magnetic resonance imaging data, obtaining a boundary mask through a Canny operator and a Dilatation operation, constructing a multi-modal low-resolution data set, and carrying out the recognition of the multi-modal low-resolution data set; meanwhile, a high-resolution T2f modal data set is reserved, and the data set is divided into a training set, a verification set and a test set; a segmentation model is constructed, and the segmentation model comprises a high-resolution mode-guided double-encoder architecture module, a cross-level attention collaboration mechanism module, and a segmentation branch and boundary prediction branch decoder module; designing a training strategy of joint optimization of boundary contour detection and region segmentation, training the segmentation model by using a training set, and storing optimal model parameters on a verification set; and carrying out model performance verification in the test set, and segmenting a to-be-tested medical image by using the verified segmentation model.

The invention discloses a self-adaptive three-dimensional scene reconstruction method and system based on a single panorama, and belongs to the field of computer vision processing. The method comprises the following steps: firstly, generating a depth map through an indoor scene panorama and constructing an initial three-dimensional grid; then generating a multi-view image and a shielding mask thereof based on view conversion, forming a training sample pair for fine tuning of the diffusion completion model, and injecting scene prior information for the model; then extracting a grid boundary contour and calculating a central axis, and adaptively constructing a camera pose set; after the integrity of the grid is optimized through iteration completion, the grid is converted into a 3D Gaussian sputtering field, and Gaussian point parameters are optimized through multi-view data; in the optimization process, an up-sampling strategy of rendering error feedback and edge Gaussian points is adopted, and finally a scene model with complete geometric textures is output. According to the method, a high-quality three-dimensional scene is reconstructed from a single panorama through a structure self-adaptive completion and Gaussian refining technology, and the method is particularly suitable for immersive roaming reconstruction of indoor scenes.

The invention provides a cultural relic auxiliary restoration decision-making method and system based on computer three-dimensional modeling, and relates to the technical field of computers.The method comprises the steps that firstly, three-dimensional model data including geometric shape and texture distribution information of a cultural relic to be restored are obtained, then damaged area feature extraction is conducted on the three-dimensional model data, and a damaged area feature set is obtained; the method comprises the following steps: acquiring a damaged region feature set, covering boundary contour, depth distribution and surface texture missing features, generating a repair candidate scheme set based on the damaged region feature set, including repair path planning and repair material matching information, performing repair effect evaluation on the candidate scheme set, and obtaining a scheme evaluation result containing structural stability and appearance consistency indexes. And outputting a cultural relic auxiliary repair decision report according to the scheme evaluation result, including recommended repair scheme priority ranking and implementation step description, thereby improving scientificity and accuracy of cultural relic repair decision.

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

The invention discloses a high-precision image detection method for a micro-drill blade surface based on deep learning, and the method comprises the following steps: S1, collecting a visible light image and a structured light image of the micro-drill blade surface, and completing the image preprocessing; s2, performing spatial alignment on the image, executing cross-modal fusion, and generating a feature fusion tensor; s3, inputting the feature fusion tensor into a multi-scale residual backbone network, and extracting a hierarchical semantic feature set; s4, inputting the semantic feature set into three task branches of defect detection, region segmentation and type classification, and outputting a corresponding prediction result; s5, calculating a multi-task loss function, dynamically adjusting task branch weights, and optimizing a feature sharing structure; and S6, generating a detection report according to a prediction result, and outputting defect coordinates, a boundary contour, a type label and a confidence value. According to the method, multi-modal fusion, high-precision identification and structured output of the micro-drill blade surface are realized, and the accuracy, efficiency and automation level of defect detection are remarkably improved.

The invention provides an underwater target detection method and system based on deep learning. The method comprises the following steps: acquiring an underwater image sequence; depth information is generated based on the underwater image sequence, and boundary contours of different underwater objects are extracted through a deep learning algorithm in combination with the depth information; constructing a reflection mode of underwater light according to the surface roughness of various underwater objects, and then performing semantic division according to the reflection mode to obtain mask regions corresponding to different semantic levels of each image frame; for each image frame, determining texture direction features, determining main reflection directions of underwater light in different mask areas, and further determining semantic coordination degree of each mask area according to all the texture direction features and all the main reflection directions; and generating confidence semantic tags of different underwater objects in the underwater image sequence based on all semantic coordination degrees. By adopting the scheme provided by the invention, the anti-distortion detection can be performed on the underwater target under the interference of the underwater environment.

The invention relates to the technical field of defect detection, in particular to an inner package defect detection system which comprises an illumination control module, a boundary recognition module, a defect gathering module, a grade mapping module and a response linkage module. According to the method, through the convergence degree and Euclidean vector distribution of the boundary contour form, boundary offset distribution information is established, tiny offset or abnormal positions are accurately captured, aggregation extraction and spatial clustering of defect boundaries are achieved, abnormal clusters are screened through distance and number distribution parameters, and the defect detection accuracy is improved. And a defect level is mapped by combining a structure level and a functional risk factor, and differential station response operation is triggered according to level evaluation, so that on the basis of improving image quality, the component identification precision is guaranteed, the space representation capability of defect clustering is enhanced, and the grading judgment granularity of defect detection in multi-component assembly is enhanced. Dynamic linkage response control in the inner packaging process is achieved, and the accuracy of overall defect recognition and the timeliness of control decision making are improved.

The invention provides a time-space evolution data processing method and system applied to tunnel deformation analysis, and the method comprises the steps: obtaining a time sequence monitoring sequence of different monitoring dimensions in a tunnel construction process, constructing a time-space evolution correlation model of tunnel deformation based on the time sequence monitoring sequence, and analyzing a dynamic coupling relation between structure response data and geological environment action data, generating a space-time correlation feature matrix, generating a feature evolution sequence of tunnel deformation based on the space-time correlation feature matrix, and performing nonlinear mutation feature extraction on the feature evolution sequence to obtain a mutation feature index set; according to the sudden change characteristic index set and a preset accumulated deformation rate threshold value, performing dynamic division of tunnel deformation safety levels, generating a safety level division result, and based on the safety level division result and the spatial distribution data of the tunnel construction section, executing spatial positioning and boundary contour extraction of the potential risk damage area. And obtaining a risk damage area distribution map. According to the invention, the tunnel construction safety risk can be accurately evaluated and effectively controlled.

The invention discloses a chip packaging quality detection optimization method based on 2D and 3D composite imaging. The method comprises the following steps: S1, collecting a two-dimensional image and a three-dimensional structure image of a chip packaging area; s2, carrying out spatial registration and normalization processing, extracting a brightness gradient, a depth gradient and a structure curvature, and generating an image fusion feature set; s3, constructing an improved Mumform-Shah image energy functional model on the basis of the image fusion feature set; s4, setting a space attention adjustment mechanism, and adjusting the weight of a boundary length item; s5, variational optimization is executed to generate an edge response diagram, and a boundary contour is extracted to form a chip packaging structure boundary diagram; s6, fusing the boundary graph and the three-dimensional structure image to construct a boundary graph, and extracting graph structure features; and S7, constructing a packaging quality scoring function, and outputting a defect type, a defect position and a packaging quality scoring value. According to the method, two-dimensional and three-dimensional structural features are fused, and the modeling capability of chip packaging defect identification and scoring is improved.

The invention relates to the technical field of machine vision industrial detection, and discloses an all-welded heat exchanger industrial detection method and system based on machine vision, and the method comprises the steps: obtaining a welding line region image of a to-be-detected heat exchanger, and extracting a boundary contour of a welding line region through an image preprocessing algorithm; calculating a welding line center line according to the boundary contour of the welding line area, and dividing the welding line center line into a plurality of detection units with equal length; in each detection unit, linear light scanning data of the surface of the welding seam and ultrasonic echo signals of a welding seam area are collected; generating a three-dimensional shape model of the surface of the welding seam according to the linear light scanning data, and extracting acoustic characteristic parameters in the welding seam; preprocessing the three-dimensional shape model and the acoustic characteristic parameters, and inputting the preprocessed three-dimensional shape model and acoustic characteristic parameters into a pre-trained defect evaluation model to obtain a comprehensive defect index of the welding seam area; and according to a comparison result of the comprehensive defect index and a specified threshold group, judging whether obvious defects exist in the welding seam area, and completing comprehensive evaluation of the welding seam quality.

The invention relates to the technical field of image segmentation, in particular to an intensive care unit video image processing method based on image semantic segmentation, which comprises the following steps of: acquiring a video image through monitoring camera shooting, dividing a semantic region, calculating a pixel displacement direction of each part, identifying an abnormal track, erasing interference, recombining a boundary contour, and comparing shape change. And constructing a trend and re-drawing a structure, evaluating stability in combination with directions, speeds and amplitudes, screening and correcting inconsistent labels, and outputting an index result. According to the method, track features are constructed by introducing pixel direction coding, a deviation region is identified and interference is eliminated in combination with a direction change trend, an edge structure is reconstructed by using a boundary connection sequence and curve fitting, contour division is optimized through deformation direction consistency, and labels are corrected by synthesizing direction change and boundary speed. Dynamic tracking and accurate labeling of the patient state are achieved, the abnormal action recognition efficiency and the label updating accuracy are improved, and the time sequence integrity and the space expression ability of image data are enhanced.

The invention discloses a seismic source inversion and clustering reconstruction integrated unstable structural plane identification method and system, and the method comprises the following steps: obtaining original sound wave data, carrying out the positioning calculation and seismic source mechanism inversion of the original sound wave data, and obtaining coal rock fracture source core parameters directly related to fracture; calculating a fracture penetration possibility index BCI in combination with the standardized fracture volume, the seismic source radius and the spatial distance; the similarity between the fractures is calculated according to the multi-dimensional features of the fractures, and density clustering is conducted on the fractures through a DBSCAN clustering algorithm; by calculating a fracture and penetration possibility index value on each fracture cluster, the fractures with relatively high fracture and penetration possibility indexes are screened out, so that a potential instability surface is identified; generating a macroscopic instability surface by adopting a B-spline surface fitting algorithm; a control point is selected through a principal component analysis method to determine a fracture main direction, a B-spline curve is constructed based on fracture distribution to generate a boundary contour, and a B-spline algorithm is used for accurately fitting an unstable structural surface.

The invention belongs to the field of virtual image display, particularly relates to a spherical reflection cavity virtual calibration method based on a neural radiation field, and aims to solve the problems that micron-level deviation is difficult to accurately capture due to interference of a high-reflection surface, and an executable correction instruction cannot be generated by fusing process constraints. The method comprises the following steps: deploying image acquisition equipment around a cavity to synchronously capture a multi-view optical image sequence; inputting a neural radiation field model, reconstructing a three-dimensional geometric structure and optical properties through a volume rendering technology, and dynamically optimizing and fitting reflection characteristics and curvature changes of a carbon fiber material; extracting spherical radius, local curvature and boundary contour parameters, and comparing the parameters with design specifications point by point to generate a deviation distribution diagram; generating a multi-level calibration instruction set based on the deviation distribution diagram in combination with the laying layer tolerance and the assembly clearance; and outputting an instruction to the manufacturing system to execute physical adjustment, and re-collecting the image to verify the calibration effect until the acceptance index is met. According to the invention, the deviation capturing precision is improved, and a correction instruction can be generated.

The invention discloses a visual analysis and screening method for fish maw appearance features, and relates to the technical field of computer vision and intelligent sorting, and the method comprises the steps: carrying out the polarization reflection feature analysis of a fish maw main region image, constructing a surface direction-reflection relation tensor, and carrying out the clustering to generate an anisotropic reflection texture feature pattern; gradient pyramid fusion is carried out on the anisotropic reflection texture feature map and the RGB image, contour information is extracted, and a fish maw boundary enhanced image is generated; inputting the fish maw boundary enhanced image into a contour detection network, performing automatic modeling, outputting a high-precision boundary thermodynamic diagram, and generating a high-precision boundary contour diagram; and carrying out feature fusion on the anisotropic reflection texture feature map and the high-precision boundary contour map, constructing a fish maw appearance description vector, carrying out appearance grade classification through a multi-layer neural network model in combination with a historical evaluation sample, and outputting a fish maw sample classification result. The accuracy and efficiency of fish maw quality detection are remarkably improved.

The invention provides a quantitative detection method and a quantitative detection system for mesoscopic defects of a soil-rock aggregate based on CT image multi-threshold segmentation, and belongs to the technical field of nondestructive testing and digital image processing of geotechnical engineering materials. The method comprises the following steps: firstly, carrying out multi-threshold segmentation on a CT image by adopting a fuzzy C-means clustering algorithm, and analyzing the image into four types of regions including a background region, a block stone region, a matrix soil body region and a mesoscopic defect region according to gray features; then, based on the heterogeneity characteristic of the material, the mesoscopic defects are divided into matrix defects and interface defects according to defect space distribution; the boundary contour of the block stone is extracted through morphological operation, and intelligent judgment of defect types is realized by combining spatial topology analysis of a defect binary image; and finally quantifying the volume ratio parameters of the two types of defects based on pixel statistics. According to the method, the limitation of a traditional homogeneous material defect analysis method is broken through, and a new technical means is provided for research on damage evolution of the earth-rock aggregate multiphase structure.

The invention discloses a visualization method and system for an oversized in-situ filling space under a composite roof, and belongs to the technical field of information processing. The method comprises the following steps: performing multi-angle data acquisition on a filling space under a composite roof structure to obtain original point cloud data; obtaining complete three-dimensional point cloud data based on the original point cloud data; preprocessing the three-dimensional point cloud data to obtain standard point cloud data; performing boundary contour recognition based on the standard point cloud data to obtain space boundary contour data; obtaining a spatial three-dimensional geometric model based on the spatial boundary contour data; performing section slicing based on the space coordinate range of the space three-dimensional geometric model, and obtaining size distribution data based on the obtained contour size parameters of the section; obtaining a total volume and a segmented volume of the filling space based on the size distribution data; and obtaining a three-dimensional visual model of the filling space based on the total volume and the segmented volume. The method provides reliable technical support for mine filling design and resource evaluation.

The invention relates to the technical field of remote sensing image processing, in particular to a remote sensing driven land utilization monitoring system. According to the invention, by extracting a ground feature boundary contour in a remote sensing image and constructing a morphological parameter set with geometric structure proportion characteristics, a path category data system for dividing a land spatial form based on structural characteristics is established, and normalization processing and a Gaussian mixture model are introduced to realize ground feature aspect ratio clustering. Constructing a structural difference index set in combination with the distribution concentration ratio, clearly identifying the path type to track the land parcel direction or the structure change trajectory, and calculating a disturbance factor in the multi-temporal remote sensing image based on the land type number change and the connection direction number of the patch adjacent region; and the main direction, the boundary contour similarity and the spatial disturbance of the land parcel in the specified period are comprehensively judged, so that the dynamic change identification of the land utilization state is realized, and the land utilization form change amplitude and the time period characteristics can be accurately reflected by setting a judgment reference and a change state marking mechanism.

The invention relates to the technical field of laser cutting machining, and discloses a laser cutting typesetting optimization method and system.The method comprises the steps that geometric information and plate information of parts are obtained; a nesting value index is calculated, and an initial typesetting sequence arranged in a descending order is generated for all the parts; obtaining a boundary contour of a residual available area on the plate, performing morphological matching calculation on the contour line of the part and the boundary contour of the residual available area, calculating an internal available space score of the concave area, and identifying a secondary typesetting space; calculating a plate utilization rate, and calculating a local typesetting quality index; when the local typesetting quality index is lower than a preset second threshold value, local rearrangement is carried out, the combined convex hull area of a minimum set is used as an optimization target, an optimized layout is obtained, and the parts are placed in the remaining available areas of the plates according to the optimized layout. According to the invention, linkage effect and irreparable material waste caused by poor local placement can be prevented, and the overall utilization rate of the board and the quality of a typesetting scheme are improved.

The invention discloses a double-background static throwing object detection method based on automatic segmentation of a target area, and the method comprises the steps: obtaining a frame sequence of a to-be-processed video, generating a mask with a target area as a guide, and carrying out the constraint of the frame sequence of the video; extracting the ROI region of each frame in the constrained frame sequence of the video to obtain an image with enhanced edge continuity; obtaining an enhanced video frame sequence corresponding to the constrained video frame sequence, constructing a long-term buffer area, generating a long-term background image, and performing foreground difference processing to obtain a foreground mask; constructing a short-term buffer area, loading the foreground masks into the short-term buffer area, when the short-term buffer area reaches a preset capacity, executing pixel-by-pixel logic and operation on all the foreground masks, generating static masks, performing morphological closed operation processing, obtaining enhanced static masks, performing contour detection, and obtaining a boundary contour of a target area; and screening and marking a target area. According to the invention, accurate identification of the target area can be realized.

The invention discloses a fissure surface reconstruction method and device based on a dynamic graph neural network. The reconstruction method comprises the following steps: acquiring core parameters of a coal rock fracture source; the method comprises the following steps: preprocessing core parameters of a fracture source by adopting a standardized processing method, constructing a fracture space-time topology analysis model based on a DGCNN graph neural network, and taking fractures as graph nodes; an edge convolution mechanism is utilized, an adjacent graph is constructed through the space-time relation between nodes, neighborhood features are aggregated to update node representation, and a local structure and global topology are captured; crack feature similarity is calculated, and clustering is carried out according to a density threshold value by using a DBSCAN algorithm; and in combination with a crack magnitude optimization clustering result, identifying a main crack surface crack with high expansion potential. Reconstructing a macroscopic crack surface by adopting a B-spline surface fitting algorithm; a control point is selected through a principal component analysis method to determine a crack main direction, a B-spline curve is constructed based on crack point distribution to generate a boundary contour, and a crack surface is accurately fitted by using a B-spline algorithm.

The invention discloses a Thangka image target detection method based on improved YOLOv11. The method comprises the steps of S1, acquiring a traditional art data set and performing preprocessing; s2, a backbone network part is improved based on a YOLOv11 network model, specifically, multi-scale edge feature enhancement conduction module design is added, and the sensitivity of the model to a boundary contour and the positioning precision of a target in a complex structure are improved; s3, the neck network part is improved based on a YOLOv11 network model, specifically, a windmill convolution module is adopted to replace traditional down-sampling convolution, a direction perception and asymmetric convolution strategy is adopted, and a receptive field is expanded on the premise that significant calculation overhead is not increased; s4, a detection head part is improved based on a YOLOv11 network model, specifically, a lightweight scale adaptive detection head is adopted, a convolution sharing mechanism and SimAM non-parameter attention are fused, and the parameter quantity of the detection head is reduced; and S5, according to the improvements in S2, S3 and S4, integrating into an improved model after structure optimization.

Disclosed in the present invention is a three-dimensional circle detection method for a freeform surface, comprising: acquiring freeform surface point cloud data, and using a normal vector-guided rolling ball method to extract a freeform surface circular-hole point cloud boundary contour from the point cloud data; performing Euclidean clustering-based boundary segmentation on the point cloud boundary contour to obtain a clustering result of all boundary point clouds, the clustering result consisting of a plurality of circular-hole contours; performing a density-based weighted iterative algorithm on each circular-hole contour to obtain a freeform surface circular-hole normal vector; and projecting a freeform surface circular hole along the freeform surface circular-hole normal vector onto a plane, and then performing circular-hole iterative fitting based on solving an overdetermined equation on the projected circular-hole point cloud to obtain final positioning information. The present invention is applicable to circular-hole positioning on freeform surfaces of different curvatures, has strong robustness, and effectively improves the accuracy of circular-hole positioning on the freeform surfaces.

The invention relates to the technical field of image data processing, in particular to a medical image-oriented 3D modeling data processing method and system, and the method comprises the steps: obtaining boundary contour information in continuous image frames in medical image data, recognizing a morphological change region between adjacent frames, calculating a morphological change rate, and judging the boundary smoothness of a medical image. And obtaining data space change characteristics. According to the invention, by analyzing the boundary contour information of the continuous image frames and combining the pixel texture and contrast characteristics, the morphological change area is identified, and the analysis capability of the image data space structure is improved. The key areas are screened according to the morphological change rate and the fluctuation intensity, dynamic optimal distribution of computing resources is achieved, and the processing precision of the complex morphological areas is improved. The task scheduling strategy adjusts the calculation batch and load according to the data characteristics, optimizes the utilization of calculation resources, and enhances the processing efficiency. The hierarchical storage and dynamic adjustment of the cache data improve the data access rate and accelerate the data loading and transmission.

The invention relates to the technical field of workpiece alignment, in particular to a machined workpiece automatic alignment method and system based on image measurement, and the method comprises the following steps: collecting a workpiece image, analyzing a local curvature change rate, adjusting sampling density, reconstructing surface features, generating micro-topography data, calculating a stability score, and outputting an attitude correction parameter. And tracking a time sequence deviation derivation instruction response deviation track, fitting a deviation curve to invert delay mapping to generate an error correction table, and mapping a control signal to adjust a step length to generate an adaptive driving instruction. According to the method, the morphological stability score is calculated through the ratio of the boundary curvature continuity to the area compactness of the equal-optical-density region, accurate judgment on the boundary contour under different image conditions is achieved, the response offset function is constructed with the help of the least square method, the execution error correction table is mapped, the response precision and execution consistency of the alignment instruction are improved, and the accuracy of the alignment instruction is improved. And the alignment efficiency and the posture stability of the workpiece under the complex morphology condition are improved.

The invention discloses a pipeline defect identification method, and relates to the technical field of pipeline defect identification, and the method comprises the following steps: obtaining a second judgment region based on a pipeline binary image; acquiring a boundary contour based on the second judgment region; acquiring a reference coordinate point based on the boundary contour; acquiring a first real-time reference value and a second real-time reference value based on the reference coordinate point and the boundary contour; acquiring a first reference threshold value and a second reference threshold value based on the images of the second number of pipeline defects; based on the first real-time reference value, the second real-time reference value, the first reference threshold value and the second reference threshold value, judging whether a defect area exists or not; the method is used for solving the problem that in an existing pipeline defect recognition technology, the defect recognition accuracy is reduced due to the fact that the repaired image of the pipeline is highly similar to the image of the pipeline defect.

The invention relates to a method and system for identifying a projectable area of a projector. The method comprises the steps of establishing a coordinate system taking the center of a projector lens as a base point based on boundary contour parameters of the projector, generating corresponding contour line data by utilizing the boundary contour parameters, and projecting contour lines on a projection surface to identify a movable axis range and / or a variable focus range of the projector lens. The system comprises a projection optical module, a boundary detection unit, a contour generation unit, and an independent contour projection unit and a control unit which are arranged on a shell. According to the scheme, the contour line data is generated based on the boundary contour parameters and is projected on the projection surface, so that a user can intuitively perceive the adjustable limit range, blind adjustment is avoided, the risks of image distortion, dark corners and content missing are reduced, the adjustment efficiency and accuracy are improved, practicability and flexibility are both achieved, and the user experience is improved. The usability of the projector and the user interaction experience can be effectively improved, and the defect of lack of visual boundary prompt in the prior art is overcome.

The invention discloses a security and protection box video monitoring system and method with an intelligent discrimination function, and relates to the technical field of video monitoring. Images are continuously collected and an image frame average brightness sequence and an image frame time sequence are constructed, so that dynamic trend extraction can be performed on an image frame brightness gradient change rate set; an abnormal shielding image frame data set is used as an input basis, a shielding behavior possibility score is output in combination with spatial change trends of an optical flow displacement vector set and an image frame brightness gradient change rate set, probability judgment of a shielding intention is realized, misjudgment of accidental non-man-made interference is avoided, and the probability of the shielding intention is improved. Edge structure analysis is executed in the optical flow disturbance concentrated area, an optical flow disturbance concentrated area boundary contour morphological feature data set is generated, the shielding area proportion is calculated and then fused with the shielding behavior possibility score to form an optical flow disturbance concentrated area feature fusion input set, and finally intelligent classification is performed through interference behavior discrimination labels.

The invention provides a spiral chute ore belt boundary identification method capable of dynamically adjusting a weight and a threshold value, and relates to the technical field of image processing. Comprising the following steps: collecting spiral chute flow field images at different moments and corresponding environmental parameters, and preprocessing the images; marking the boundary contour of the ore belt through manual marking, recording the boundary contour as a sample verification image, and performing one-to-one mapping on the sample verification image and the corresponding sample image to form a training image set; performing normalization and sliding window filtering on the environmental parameters, and constructing an environmental influence parameter function; and establishing an edge detection deep learning model by taking a sample image in the training image set and a corresponding environmental parameter as input and taking a sample verification image as a label, dynamically adjusting the weight and threshold of a convolutional layer, and finally identifying the boundary of the spiral chute to be detected through a loss function optimization model. According to the scheme, efficient boundary recognition can be achieved under different environmental conditions, and the separation efficiency of the spiral chute is remarkably improved.

The invention provides an automatic process control method and system applied to high-precision special-shaped target material binding, and relates to the technical field of image processing, and the method comprises the steps: obtaining multi-view image data of a target material, generating a three-dimensional geometric structure model of the target material, and generating point cloud data of an outer boundary contour and a surface appearance of the target material; and a matched temperature and pressure strategy template in the binding process parameter library is called, fitting clamping is achieved, different materials are adapted, and a closed-loop control strategy is executed to dynamically adjust heating / pressure output. The problems that in the prior art, due to the fact that the target material is complex in shape, heterogeneous in material and non-uniform in technological parameter, the temperature and pressure control precision in the binding process is insufficient, the clamping adaptability is poor, thermal stress distribution is not uniform, and high-consistency and high-strength binding of the special-shaped target material is difficult to achieve are solved, the intelligence and the adaptability of the binding technology are improved, and the binding efficiency is improved. Accurate hot-pressing binding and process closed-loop control between multiple types of target materials and back plates in different areas are achieved.

The invention discloses a full-automatic measurement and strength testing method and system for a concrete core sample, and the method comprises the steps: obtaining an end face image and a cylindrical surface image of the concrete core sample, and extracting the boundary contour of the images; according to the boundary point set of the core sample image, identifying core sample end face deformation abnormity and cylindrical surface deformation abnormity; constructing a core sample end face defect identification model, and identifying core sample end face defect types and defect severity; extracting texture features, color features and geometric features of the core sample, and constructing a joint feature matrix; constructing a core sample cylindrical surface defect identification model, and identifying the defect type of the core sample cylindrical surface; performing core sample discarding, finishing and inspection flow distribution according to the deformation and defect identification information of the end surface and the cylindrical surface of the core sample; and determining the compressive strength of the concrete core sample by using an automatic centering and servo loading device. According to the invention, the integrated full-process unmanned closed-loop operation of size detection and strength test is realized, and the requirements of efficiency, consistency and traceability of mass engineering core sample detection are met.