2315 results about "Edge detection" patented technology

The invention discloses an electric power system abnormal remote signaling detection method based on a graph auto-encoder model, and the method comprises the steps: collecting measurement data of an electric power system, carrying out the preprocessing, obtaining a graph data set with an abnormal label, and dividing the graph data set into a training set and a test set; inputting the graph data in the training set into the graph auto-encoder model for training; after training is completed, abnormal score distribution is counted based on normal edge samples in a training set, a threshold value is set to serve as a follow-up judgment basis, and threshold value selection takes the accuracy rate and the recall rate on a test set as an adjustment and optimization target; in a test stage, image data in a test set are input to carry out edge feature reconstruction and anomaly scoring, anomaly judgment is carried out on edges in combination with a set threshold value, and a preliminary abnormal edge detection result is output; and the output abnormal edge detection result is input into the graph restoration module, the restored edge structure and edge features are output, the damaged remote signaling state in the power grid is restored, and the integrity of the graph structure and the operation credibility of the power system are improved.

Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

The invention discloses a double-branch edge detection method and system based on CNN and Transform deep interactive fusion, and relates to the technical field of computer vision and deep learning, and the method comprises the steps: extracting multi-scale local features through a CNN-based fine semantic edge branch by employing an OfficientNet-B2 backbone network, and carrying out the deep supervision in combination with a side output structure; meanwhile, through a global context branch based on lightweight Transform, an efficient local attention mechanism is adopted to capture a long-distance dependency relationship of the image; the core of the invention lies in that a cross-attention module is designed and realized, local features extracted by a CNN branch are used as a Key and a Value, and features of a Transform branch are used as Query, so that deep interaction and fusion of the two branches on the feature level are realized, instead of simple feature splicing; in addition, the method also adopts a uniform loss function for balancing dynamic weighted combination of binary cross entropy loss, focus loss, Dice loss and the like to perform model optimization.

The invention relates to the technical field of image processing, in particular to an anti-collision beam weld defect detection method and system based on image processing, and the method comprises the steps: carrying out the image collection of a weld region of a produced anti-collision beam, and obtaining a gray image; the method comprises the following steps: performing initial partitioning on a grayscale image, respectively obtaining a local complexity index of each initial sub-block, obtaining at least two adaptive sub-blocks based on the local complexity index of each initial sub-block, and performing adaptive local histogram equalization on each adaptive sub-block to obtain a target grayscale image; the method comprises the steps of performing edge detection on a target grayscale image to obtain at least two edge pixel points, performing frequency domain conversion on the target grayscale image according to a gradient direction of each edge pixel point to obtain a frequency domain image, performing filtering processing and time domain conversion on the frequency domain image to obtain a denoised image, and identifying defects in the denoised image by using a neural network. And the defect detection efficiency is improved by inhibiting the periodic texture in the weld seam image.

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention relates to the technical field of image processing, and discloses a film lamination defect detection method and system based on image processing. The method comprises the following steps: acquiring and preprocessing a film-conducting layer interface by using a polarization enhanced imaging device to obtain an enhanced image; applying double-layer gradient edge detection to extract the edge of a fitting area, and marking a micro-defect sensitive area; an interface smoothness index and a fitting uniformity feature are obtained through multi-scale morphology feature extraction; inputting the features into a support vector classifier to identify bubbles-wrinkles; performing three-dimensional reconstruction on an identification result to generate a bubble distribution and stress field graph; and finally evaluating the fitting quality, and outputting an integrity score. According to the invention, automatic accurate detection and quantitative evaluation of tiny defects of the thin film-conducting layer bonding interface are realized.

The invention relates to the technical field of image processing, in particular to a feature point automatic labeling method and system based on point cloud data. The method comprises the following steps: acquiring regional multi-source point cloud data, and performing multi-modal data fusion to obtain point cloud fusion data; performing shoreline structure ground point cloud segmentation based on the point cloud fusion data to obtain a ground point cloud segmentation data set; performing edge detection on the ground point cloud segmentation data set, and identifying geometric salient points according to an edge detection result so as to obtain a feature point candidate set; performing ground feature type classification based on the feature point candidate set to obtain a target classification data set; distributing a symbolic pattern for the target classification data set and binding a point cloud feature attribute to obtain a symbolic data set; and performing multi-level symbol dynamic adjustment and interactive labeling based on the symbolized data set to obtain a symbol dynamic view. The method is helpful for improving the accuracy and efficiency of symbolization expression of the shore feature points, and has strong interactivity.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention discloses a building structure crack intelligent detection method, which comprises the steps of S1, constructing a multi-dimensional intelligent sensing array, and obtaining four-dimensional spatio-temporal data including vision, stress, vibration and temperature; s2, performing feature enhancement processing on the multi-modal data; s3, realizing cross-modal crack identification and positioning based on a space-time attention neural network; s4, adopting a sub-pixel edge detection and ultrasonic genetic inversion algorithm; s5, constructing a coupling dynamics prediction model; s6, establishing a dynamic threshold evaluation and multi-dimensional self-calibration mechanism; 0.05 mm micro-crack identification and 3mm depth detection precision are achieved and are improved by 50% compared with a traditional method, the detection accuracy under the complex environment is larger than or equal to 98% through the multi-modal data fusion and GAN enhancement technology, the RMSE is smaller than or equal to 0.03 mm through short-term prediction, the accuracy is larger than or equal to 85% through long-term prediction, double guarantees of a physical mechanism and data driving are established, and the detection accuracy is improved by more than or equal to 98% through the multi-modal data fusion and GAN enhancement technology. And the system has self-calibration, self-adaptive sampling and multi-modal fusion decision-making capabilities, so that the manual intervention cost is greatly reduced.

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 provides a road marking quality intelligent evaluation system based on image analysis, and relates to the technical field of road facility monitoring, and the system comprises a detection unit, a marking quality evaluation unit, a marking wear prediction unit, a GPS positioning unit, a vehicle driving information unit, a control unit and a remote central control unit. The marking quality evaluation unit is constructed based on a differential geometry theory and comprises a curvature flow edge detection module, a marking geometric characteristic manifold representation module and a multi-scale differential invariant evaluation module, the system regards a marking as a two-dimensional manifold, and the marking quality is evaluated by calculating differential geometric quantities such as a Gaussian curvature, an average curvature and a shape index. And constructing geodesic distance measurement on the feature manifold, and evaluating the completeness, visibility and reflective performance of the marked line. And the marking wear prediction unit predicts the service life of the marking based on the traffic flow information and the environment characteristic mapping relation model, and generates a maintenance suggestion.

The invention relates to the technical field of welding seam positioning, and discloses a structured light and line laser fused welding seam thickness grading three-dimensional positioning method and system, and the key point of the technical scheme is that the method comprises the following steps: S1, shooting a workpiece through a structured light camera, and obtaining a workpiece image; s2, based on the workpiece image, through a channel space attention mechanism and a Center Net convolutional neural network, welding seam position coarse identification is carried out, and the welding seam position is determined; s3, according to the identified position of the welding seam, scanning the position of the welding seam through a line laser camera to obtain three-dimensional point cloud data of the welding seam; and S4, based on the three-dimensional point cloud data, through a Marr-Hildreth edge detection algorithm, identifying to obtain three-dimensional coordinates of the welding seam, and rapidly and accurately positioning the three-dimensional data of the welding seam of the workpiece.

The invention discloses a medical image fuzzy boundary segmentation method based on an edge perception Mama network. The method aims at solving the problems that a camouflage pathological structure in a medical image is visually similar to surrounding healthy tissues, so that segmentation is difficult, and clinical deployment is difficult due to secondary calculation complexity of an attention mechanism in a traditional camouflage target detection (COD) method. According to the invention, a boundary guiding module inspired by COD is creatively combined with linear complexity state space modeling, and an E-Mama edge guiding framework is provided. The framework adopts an encoder-edge guide-decoder structure, and accurate boundary description is realized while the computational efficiency is kept. The system comprises five core components: a Mama encoder; an adaptive fusion processing module (AFM); an edge detection module (EDM); an edge guidance module (EGM); the invention relates to a Mama decoder. Experiments show that advanced segmentation precision is realized on a plurality of medical data sets, and meanwhile, the calculation complexity is reduced from O (n2) to O (n), so that clinical deployment becomes possible.

The invention discloses a magnetic field measurement method and system based on a multi-sensor fusion technology, and relates to the technical field of sensor fusion and magnetic field measurement, and the method comprises the steps: deploying a multi-sensor data array, carrying out the adaptive initialization of a bistable SR parameter range, defining an SR system differential equation, and carrying out the iterative optimization through employing an MPA population. Carrying out Hilbert transform edge detection on enhanced signal component data, calculating an array inclination angle, carrying out abbe error and bidirectional projection error compensation, and carrying out metasurface grid coordinate quantization mapping; the collected and cross-scale magnetic field data set is preprocessed and packaged into data cells, quality evaluation and weight distribution are carried out on the data cells, and extended Kalman filtering data fusion is carried out; by introducing a bistable stochastic resonance system and an MPA population optimization algorithm, a weak magnetic field signal is obviously enhanced, and by calculating an array inclination angle and compensating an Abbe error and a bidirectional projection error, the space consistency of a measurement result is improved.

The invention provides an aluminum profile defect analysis method and system based on texture features, and relates to the technical field of edge detection, and the method comprises the steps: obtaining an aluminum profile surface image, and determining an extrusion direction; dividing the image into a plurality of local blocks, carrying out gradient direction and amplitude calculation on each block, and judging whether the block belongs to a high-confidence-coefficient texture region or not by combining a direction difference value and confidence coefficient; a first suppression coefficient is executed on the high-confidence-coefficient texture region for primary reduction to form a first processing image, then the updated image is judged again, a second suppression coefficient with higher strength is applied to the region still having obvious texture features, and a second processing image is generated; and finally, defect identification is carried out on the weakened image through edge detection, and residual texture false edges are removed in combination with direction consistency or connectivity analysis. The method has the advantages of light weight, low computing power consumption and high accuracy, and can be applied to online detection and quality control of the surface flaws of the aluminum profile in industrial production.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

The invention relates to the technical field of computer vision and three-dimensional space modeling, and discloses a real-time multi-instance segmentation method and device based on a Gaussian splash radiation field model. The method comprises the following steps: based on a two-dimensional Gaussian splash radiation field model, rendering a visual angle with continuous spatial change to obtain an image sequence, and obtaining a multi-visual-angle consistent two-dimensional instance segmentation mask of the image sequence; and assigning an instance tag to each Gaussian primitive based on the two-dimensional instance segmentation mask. And for a two-dimensional Gaussian splash radiation field model with an instance label, acquiring a coarse instance segmentation mask and a color image at any view angle by using a Gaussian splash algorithm, inputting the mask and the image into a lightweight post-processing network for edge detection and region connectivity repair, and outputting a target two-dimensional instance segmentation mask with a complete structure and a label consistent with a three-dimensional scene. The method does not depend on any training or distillation process, directly acts on a Gaussian splash radiation field model, and has the advantages of high reasoning speed, high semantic consistency, support of multi-target continuous tracking and the like.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention discloses an electric power system safety early warning method and system based on multi-modal cooperation, and relates to the technical field of electric power system safety early warning, and the method comprises the steps: collecting multi-source operation data, carrying out the preprocessing, carrying out the multi-modal feature extraction and fusion based on the preprocessed data, and carrying out the multi-modal feature extraction and fusion. Inputting an edge detection model and outputting an abnormal confidence score in combination with an attention mechanism; and performing alarm grading according to the abnormal confidence score, constructing a causal diagram for alarms with high risk levels in combination with associated security events, and performing future attack path prediction by adopting a time sequence diagram neural network. According to the method, multi-scale convolution and a channel attention mechanism are fused, the extraction capability of the multi-source data time sequence features of the power system is enhanced, the anomaly detection precision is improved, dynamic attack path prediction is realized in combination with RMTPP and causal atlas topological constraints, sequence modeling is enhanced through self-attention and position coding, and the detection accuracy is improved. And the perspectiveness and the reliability of the safety early warning of the power system are obviously enhanced.

The invention belongs to the technical field of plastic part quality detection, and discloses a plastic part gold stamping mark accurate positioning method and system based on visual identification, and the method comprises the steps: collecting environment illumination parameters, equipment operation parameters and image data, and processing the image data to obtain regional image data; performing edge detection and fracture repair operation on the regional image data to obtain identification edge feature data; calculating to obtain a position deviation quantized value according to the identification edge feature data; based on the position deviation quantized value, in combination with preset inclination direction information, adjusting an image visual angle and correcting an angle deviation to obtain corrected image data; executing edge path tracking on the corrected image data to obtain path complete data; obtaining an identifier positioning result based on the path complete data in combination with the environment illumination parameters and the equipment operation parameters; and carrying out stability evaluation on the identification positioning result, and obtaining a final system identification stability result in combination with preset detection fluctuation characteristics.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention discloses an intelligent identification system for an osteoporosis area, belongs to the field of image processing calculation, and aims to solve the problems of limited technical coverage and lack of a dynamic optimization mechanism. In a feature extraction stage, a system combines traditional image processing and deep learning technologies in parallel, extracts bone trabecula multidirectional texture features by using a Gabor filter and a local binary pattern algorithm, analyzes bone contour curvature by combining Sobel edge detection and morphological operation, constructs geometric morphological parameters, and performs feature extraction on the bone trabecula. The local branch focuses on the porosity and arrangement rule of the bone trabecula by adopting a 3D convolutional network, the global branch is embedded into a compression excitation module based on an improved MobileNetV3 network to strengthen the overall morphological expression of the bone, the response intensity of a local microstructure is enhanced by space attention, the weight of global bone topological characteristics is calibrated by channel attention, and a multi-scale splicing strategy is combined, so that the overall morphological expression of the bone trabecula is optimized. And finally, outputting a fusion feature matrix after noise suppression, and remarkably improving the expression ability of pathological features.

The invention relates to the technical field of machine vision image analysis, in particular to a lubricating oil abrasive particle quality analysis method and system based on machine vision, and the method comprises the steps: obtaining a thickness distribution diagram of an oil residual layer on the surface of an abrasive particle through a gray ratio relation between a first transmission image and a second transmission image; performing optical compensation on the first transmission image to obtain a final compensation image, and performing phase consistency edge detection to obtain an abrasive particle edge enhanced image; calculating the polarization reflectivity ratio of each abrasive particle area; extracting the mirror reflection intensity difference of each abrasive particle in the abrasive particle edge enhanced image; and when the abrasive particles are analyzed as metal attributes according to the polarization reflectivity ratio and the specular reflection intensity difference, identifying the morphological characteristic parameters of the abrasive particles, matching the morphological characteristic parameters with a pre-stored abrasive particle wear characteristic library, and outputting a quality evaluation report of the lubricating oil according to a matching result. The influence of oil film interference is effectively inhibited, the metal particles are accurately distinguished, and high-precision analysis on the lubricating oil abrasive particles is realized.

The invention relates to the technical field of substation defect detection, in particular to a substation defect detection method and system and a storage medium. The method comprises the following steps: acquiring a substation equipment image by adopting real-time image acquisition equipment, and establishing an equipment image database; performing denoising and edge detection through image processing software to generate preprocessed image data; detecting an abnormal area of the substation equipment through feature extraction and texture analysis, and training a defect recognition model; after real-time image information is input into the model, an abnormal defect area of the transformer substation is recognized, and fault risk gradient deterioration is estimated; calculating a risk index based on a pre-estimation result, and if the risk index exceeds a set threshold value, triggering an early warning function, providing defect types and positions, and suggesting repair measures; according to the invention, the transformer substation defect detection method is optimized, so that the transformer substation defect identification is more accurate.

The invention relates to the technical field of multi-sensor identification, in particular to a coal gangue detection and classification method and system based on multi-sensor fusion discrimination, and the method comprises the following steps: obtaining visible light and infrared images, calculating brightness and intensity judgment feature conditions, executing edge detection to extract gray segments, and fusing textures and a thermal field to generate a vector set. And performing clustering analysis to finish classification judgment, and outputting a coal gangue detection classification result. According to the method, a precise trigger mechanism is established through brightness and thermal radiation double-feature screening, boundary recognition sensitivity is enhanced through gray abrupt change point division, salient region extraction capacity is enhanced through weighted fusion of texture energy and gray gradient, and a cross-modal consistency feature group is constructed through combination of two-dimensional vector construction and similarity screening. The recognition expression integrity is improved, static threshold classification is replaced by vector distribution clustering, accurate mapping and classification decision making of material attributes in a complex scene are achieved, and the stability and the recognition rate of a coal gangue detection result are guaranteed.

The invention discloses a method for high-precision measurement of the diameter of an inhibition zone of a culture medium. The method comprises the following steps: S1, image acquisition and pretreatment: acquiring a culture medium image and carrying out standardized pretreatment on the acquired image; s2, semantic segmentation of the inhibition zone: performing network training on the preprocessed petri dish image based on deep learning; and S3, accurate edge detection: extracting an accurate boundary of the inhibition zone based on a semantic segmentation result, and adopting a sub-pixel-level edge detection algorithm. And S4, scale identification and calibration: automatically detecting and positioning the measurement scale in the image by adopting an intelligent image identification algorithm, and integrating a calibration precision verification mechanism to realize real-time automatic calibration of measurement data. And S5, diameter calculation and result output: based on the results of S3 and S4, calculating the equivalent diameter of the inhibition zone by adopting a geometric fitting algorithm, and generating a standardized measurement report. According to the method, deep learning and image processing technologies are fused, high-precision automatic measurement of the diameter of the inhibition zone is realized, and the method has strong environmental adaptability.

The invention discloses a deburring method combining a 2D image and a 3D point cloud, and relates to the technical field of image processing, and the method comprises the steps: carrying out the point cloud registration under the verification of the surface orientation consistency of a standard point cloud and an actual point cloud; constructing a pixel mapping matrix between the workpiece image and the RGB image; semantic-level edge coarse extraction is carried out on the workpiece image, and extraction of a sub-pixel-level actual edge contour is carried out on the basis of coarse extraction in combination with a traditional edge detection algorithm; aligning the actual edge contour to the actual point cloud through the pixel mapping matrix; and the target polishing surfaces are aligned to the actual point cloud, to-be-polished edge contours are screened based on the distances between the contours of all the target polishing surfaces in the actual point cloud and all the actual edge contours, and a polishing track is obtained in the workpiece image. According to the method, the sub-pixel edge positioning capability of the 2D image and the spatial topology information of the 3D point cloud are fused, and the inherent defect of a single mode is overcome.

The invention provides a landslide classification method and system based on a visual language model and a cross attention mechanism. The method and the system specifically comprise the following steps: data preprocessing: carrying out Canny edge detection on an RGB image, and calculating terrain attributes such as a gradient and a slope direction for a DEM (Digital Elevation Model); feature extraction: capturing local features by adopting a reflection filling convolution layer and multi-scale residual connection; a visual language model is introduced, wherein semantic enhancement features are extracted through image-text alignment by means of the visual language model; cross self-attention fusion: capturing a global context through self-attention, and focusing heterogenous data complementary information by cross attention; and classifying and outputting: outputting a result by using global average pooling and a linear classifier. Through the visual language model and the cross self-attention mechanism, the landslide recognition capability under the complex terrain is effectively improved, an efficient and reliable technical means is provided for geological disaster monitoring, and the method can be widely applied to the fields of landslide recognition, risk assessment and the like.