90 results about "Difference-map algorithm" patented technology

The invention relates to a building prefabricated part quality detection method based on multi-modal vision. According to the method, multi-modal data including 2D image data and 3D point cloud data are obtained, an improved YOLOv8 model is used for performing defect coarse positioning on the 2D image data, defect parameters are calculated, defect areas such as cracks and exposed ribs can be quickly locked, and the parameters of the defect areas can be obtained. And by means of SIFT feature matching and ICP point cloud registration technologies, comparing with a two-dimensional template and three-dimensional geometric parameters of the BIM standard component model to obtain a two-dimensional registration difference chart and a three-dimensional deviation thermodynamic chart. And finally, according to the defect confidence coefficient, the two-dimensional registration difference chart and the three-dimensional deviation thermodynamic diagram, a preset dynamic weighting rule is adopted to carry out joint decision making, and a quality detection result is obtained. According to the method, through the multi-modal data, the improved YOLOv8 model, the point cloud registration technology and the preset dynamic weighting rule, the false detection problem can be effectively solved, the detection reliability and accuracy under the complex working condition are improved, and the quality management level of the building prefabricated part is improved.

The invention discloses a teaching course recommendation method and system based on English learning data, particularly relates to the field of semantic processing, is used for solving the problem of poor pertinence of a traditional English learning course, and comprises the following steps: aiming at systematic difference of native language and English expression on a syntactic structure, constructing a language structure difference map and extracting a structure offset label; the method comprises the following steps: performing dependency syntactic analysis on semantic consistent sentence pairs of multi-language aligned corpora to generate a structural difference feature set; on the basis, a semantic-grammar dimension mapping graph is constructed in a classified mode, and a general structure expression vector is established for graph nodes. A teaching course is divided into knowledge point units in combination with a context label, and a mapping relation between a structure label and the course is established. The system performs structure analysis and semantic matching on sentences input by the learner, identifies structure migration type expression errors, and recommends accurate teaching content according to context and structure labels.

The invention discloses a low-voltage distributed photovoltaic group dispatching and group control method and system based on multi-source information fusion, and relates to the technical field of photovoltaic group dispatching and group control, and the method comprises the following steps: obtaining the operation information of each photovoltaic node in a low-voltage power distribution network, and converting the operation information into a state vector; based on the state vector and a system optimization target, generating a photovoltaic group control instruction set, and establishing an expected physical behavior model; monitoring an actual behavior track of each node after the instruction is executed, comparing the actual behavior track with an expected physical behavior model to construct a response difference atlas, and quantifying response deviation; for nodes with response deviation, the difference type is judged according to a deviation map, a compensation instruction is generated by reconstructing a local optimization target based on a type matching correction strategy, and a correction path is output; and inputting the historical behavior deviation sample and the correction path into the graph structure time sequence learning model, and updating the node control weight and the regulation priority. According to the method, the response difference atlas is constructed based on the actual behavior track, and the control strategy execution deviation can be accurately recognized.

The invention discloses a government affair knowledge graph updating and version control method and system, and belongs to the technical field of artificial intelligence and knowledge graphs, Git-based file change drives incremental updating of knowledge graphs, and the method is implemented by the following steps: change monitoring: integrating Git version management; capturing file change through double modes of timing scanning and event triggering, establishing one-to-one mapping between a file Git version and a map version, and transmitting difference contents; difference preprocessing: standardizing difference contents into an analysis unit containing positions, types, original contents and updated contents, and filtering meaningless differences through a G-NF algorithm; constructing a difference subgraph; carrying out difference atlas fusion; and version control: storing version archives, and providing a multi-dimensional query tracing function. According to the method, version closed-loop management is achieved, change traceability is accurate and efficient, incremental atlas updating is achieved, total processing resource consumption is reduced, meanwhile, multi-scene version switching is supported, and repeated construction and maintenance cost is reduced.

The invention relates to a heterogeneous remote sensing image change detection system and method based on a Mama model. The system comprises a feature extractor, a codec network and a change detector. The feature extractor is used for extracting first feature maps of an optical mode and an SAR mode respectively; the codec network is used for mapping and reconstructing the first feature map based on a Mama model to generate a second feature map of an optical mode and an SAR mode; the change detector subtracts the second feature maps of the same mode and calculates an L2 norm along the channel dimension, and weighted fusion is carried out on calculation results of the L2 norms of different modes, so that a difference map is obtained; the difference map processor optimizes the difference map based on a full-connection conditional random field method, and segments the optimized difference map into a varying region and a non-varying region based on an optimal threshold selected by an adaptive threshold segmentation algorithm. According to the method, a feature alignment process and difference chart generation are coupled into an end-to-end optimization task, and the problem of error accumulation caused by two-stage decoupling in a traditional unsupervised method is avoided.

The invention discloses a lightweight optical remote sensing image change detection method and system based on multi-scale perceptual learning, a storage medium and electronic equipment, and the method comprises the following steps: firstly, carrying out the preprocessing of a dual-time-phase optical image, marking a change region, and generating a label graph; then, the image and the label are cut into small samples, and the small samples serve as training data of an LCDNet model; according to the model, a lightweight encoder is adopted to extract a multi-scale feature map, a low-pass filtering down-sampling module reduces information loss, and a scale sensing block enhances the sensing ability of the model to different scale changes. And the multi-scale features are subjected to difference fusion through the spatio-temporal change perception module to generate a difference graph, efficient fusion decoding is realized through a learnable multi-scale decoder, and a final change graph is output. Model parameters are stored after training is completed, the preprocessed to-be-detected sample is input into the model, a detection change prediction map is output, accurate detection of a multi-scale change area is achieved while the parameters and the calculation amount are reduced, and the method is more suitable for actual deployment requirements.

The invention relates to the technical field of image recognition, in particular to an intelligent comparison recognition method and system for printed images, and the method comprises the steps: building a spatial position mapping relation according to a multi-dimensional feature set, and comparing various types of feature differences of corresponding positions to obtain a feature difference chart; according to a printing process parameter library, analyzing a characteristic offset rule of each difference area in the characteristic difference chart, and judging whether the difference is caused by printing pressure or ink supply fluctuation; and according to a historical defect case library, matching a difference mode of the feature difference chart, and identifying whether an abnormal difference caused by printing plate abrasion or paper deformation exists or not. According to the invention, through a mechanism of dynamically adjusting the feature matching weight, the feature similarity of the key area can be recalculated according to the cause labeling information detected in real time, so that the optimized feature difference graph is generated, the precision of the detection result is improved, timely feedback can be realized in the production process, and quality problem accumulation is prevented.

The invention provides a nameplate printing defect detection method and system based on image segmentation. The method comprises the following steps: acquiring a nameplate surface image and carrying out distortion correction and brightness homogenization preprocessing; generating a pixel-level segmentation mask by using the semantic segmentation network; template registration is carried out based on the segmentation result, and a deformation field vector and a registration difference chart are calculated; performing defect area extraction and type identification on the difference image; according to the method, deep learning semantic segmentation is adopted to realize pixel-level analysis of a nameplate printing area, deformation field registration is combined to accurately quantify overprinting offset, defect quantitative evaluation is realized through multi-dimensional scoring, the detection rate reaches 99% or above, and the detection time of a single piece is less than 0.5 second.

The invention discloses a cross-sensor remote sensing image unsupervised change detection method based on a structured graph auto-encoder, belongs to the field of computer vision and remote sensing image processing, and particularly relates to a cross-sensor remote sensing image unsupervised change detection method. The objective of the invention is to solve the problem of low cross-sensor remote sensing image change detection accuracy caused by difficulty in explicit modeling of semantic change and insufficient robustness to modal difference in an existing model. The method comprises the steps of 1, obtaining a source domain and a target domain; 2, obtaining a source domain graph structure and a target domain graph structure; 3, constructing a learnable structural difference tensor; 4, constructing a structured graph auto-encoder 1; 5, constructing a structured graph auto-encoder 2; 6, obtaining a trained structured graph auto-encoder 1 and a corresponding weight, offset and structural difference tensor; 7, obtaining a trained structured graph auto-encoder 2 and a corresponding weight, offset and structural difference tensor; and 8, obtaining a final difference chart.

A computer-implemented method can include generating a first knowledge graph from a first version of an application programming interface (API), generating a second knowledge graph from a second version of the API, identifying changes from the second knowledge graph to the first knowledge graph, and generating a difference graph based on the identified changes from the second knowledge graph to the first knowledge graph. The difference graph connects the second knowledge graph to the first knowledge graph via one or more revision edges, which represent the identified changes from the second knowledge graph to the first knowledge graph.

The invention discloses a twin structure change detection method for SAM2-assisted multi-scale feature coding, and the method comprises the steps: constructing a twin network architecture based on a multi-level Hiera encoder, and extracting a multi-level feature map of a dual-time-phase remote sensing image through the architecture; a multi-branch convolution block is used for processing the multi-level feature map, and the weight is reduced and the dual-time-phase feature dimension is unified; generating a change region difference chart based on the multi-level features, enhancing the feature response intensity of a real change region in the change region difference chart of each scale, and obtaining a multi-scale feature difference chart; a target loss function of multi-scale feature difference graph joint prediction is proposed, a model training process is optimized by using a hierarchical constraint relationship among different scale features, and the detection capability of the model to a multi-scale change region is improved. The method can effectively improve the remote sensing image change detection precision, especially shows obvious advantages in the aspects of reducing small change leak detection and improving boundary positioning precision, and has a good practical application prospect.

The invention discloses an SAR (Synthetic Aperture Radar) image change detection method combining convolution and mixed attention. The SAR image change detection method comprises the following implementation steps of: firstly, generating a difference chart for two SAR images by using a composite neighborhood intensity difference method; then, a hierarchical FCM clustering algorithm is used for carrying out pre-classification processing on the difference image, a pseudo label matrix is generated, variable and invariable high-probability sample pixels in pseudo label pixels are selected, spatial positions of the pixels are extracted, and on the pixels of the corresponding spatial positions of the two original SAR images, a pseudo label matrix is generated; pixel blocks with the pixel points as the centers are taken as a training set, and pixel blocks with all the pixel points as the centers are extracted from the two original SAR images to serve as a test set; and then training a neural network combining convolution and mixed attention by using the training sample set, and then carrying out change detection analysis on a test set by using the trained network to generate a final change detection result graph. The method has clear advantages in the aspects of SAR speckle noise suppression and change detection precision.

Aspects of the invention include systems and methods configured to extract enriched target-oriented common sense from grounded graphs to support efficient next step decision making of an autonomous agent. A non-limiting example computer-implemented method includes extracting common sense from a source. The extracted common sense can include a first knowledge graph. An environment state can be extracted from an observation. The extracted environment state can include a second knowledge graph. The second knowledge graph can include an interactive object and a state of the interactive object. A difference graph including the extracted common sense and the extracted environment state can be generated. A next action is selected based on the difference graph and the next action is taken by an autonomous agent.

To provide an inspection device that can more accurately inspect the appearance of an object. [Solution] An inspection apparatus according to one aspect of the present disclosure includes: an acquisition unit that acquires an image of an object; a difference calculation unit that generates a difference map based on a comparison image which is an image of the object stored in advance and the image; a learning unit that generates a learning model that outputs the degree of abnormality of the object shown in the image in response to the input of the image by performing machine learning using a plurality of the comparison images; an abnormality calculation unit that generates an abnormality map for the object using the learning model; a coupling unit that generates a combined abnormality map based on the difference map and the abnormality map; and a determination unit that determines the state of the object based on the feature portion included in the combined abnormality map.

The invention relates to the technical field of software testing, and discloses an AI-based test demand analysis and use case generation method and system.The method comprises the steps that a new and old version demand document set is obtained, a corresponding logic flow graph is constructed, a logic increment difference graph is generated through graph isomorphism matching, and an overall influence area is determined; obtaining a stock test case set and mapping the stock test case set into a stock case path set, and executing path geometric deformation and expansion after an affected path is identified to generate a final evolution path; and finally, calculating the comprehensive energy loss of the evolution path, judging the evolution state of the use case, obtaining a final test use case set and establishing a use case evolution pedigree table. According to the method, activation and evolution of stock test assets are realized, and the maintenance cost of government affair software regression test is reduced.

The application provides a large model-based automatic code generation and optimization method and system. Among them, the application generates an initial logical framework by a large model, forms a symbolic structure through symbolic processing, and generates a code intermediate representation that retains key semantics, and finally outputs source code. An index set is constructed using a vulnerability knowledge graph, a source code instruction sequence is scanned through parallel computing, and risk instruction positions and vulnerability details are marked by combining semantic similarity and path pattern matching. Based on vulnerability information, a historical case database is retrieved, a matching relationship is established, and an optimization strategy containing repair operations and constraints is generated. A semantic analysis model is loaded at the risk position, a difference graph of execution mode is constructed to generate features, a large model parameter is fine-tuned based on the constructed adversarial sample, and the newly generated code is rechecked for vulnerabilities. The application realizes intelligent generation from requirements to code, and completes the loop of vulnerability identification, repair strategy generation, large model dynamic optimization, and post-repair verification.

The present application belongs to the field of structural health monitoring, and particularly relates to a method for detecting cracks in aircraft structures based on convolutional neural networks. Step 1: Construct a crack image dataset; Step 2: Train a YOLOv5 network using the crack image dataset to obtain a YOLOv5 model; Step 3: Construct a difference map dataset; Step 4: Obtain an improved YOLOv5 network, and train the improved YOLOv5 network using the difference map dataset to obtain an improved YOLOv5 model; Step 5: Perform an overall test on the image to be detected based on the YOLOv5 model and the improved YOLOv5 model. The present application can achieve efficient and reliable crack damage detection in fatigue testing of full-scale aircraft structures.

The present application provides a system and method for identifying encroachment and degradation of urban green space based on computer vision recognition, wherein the method includes collecting multi-temporal images of urban green space and sorting them by timestamp to generate a difference map to reflect the dynamic changes of green space; vectorizing the dynamic information, establishing a digital model as a reference benchmark for the green space boundary, and overlaying and comparing it with the latest remote sensing image to obtain the corrected green space boundary; using a target detection network to identify non-vegetation covered areas in multi-temporal images, generating a preliminary green space encroachment location map, and then performing shape regularization and size filtering to optimize the green space encroachment location map; based on the optimized map, analyzing the temporal and spatial distribution characteristics of each factor, combining the changing trend of the non-vegetation covered area to make a prediction, and generating a trend prediction result. Finally, the prediction results are quantitatively evaluated to generate a comprehensive degradation severity report. The present application improves the efficiency and accuracy of identifying encroachment and degradation of urban green space.

The invention relates to a generated image detection method based on texture complexity and difference feature focusing, and aims to detect and distinguish a generated image and a real image. The method mainly comprises the following steps: (1) providing a generated image detection model based on texture complexity and difference feature focusing; (2) providing a mask constraint-based directional feature difference chart generation method; and (3) providing a classifier structure based on image difference feature graph comparison.

Systems and techniques are described herein for processing image data. For instance, a method for processing image data is provided. The method may include predicting, using a machine-learning model, a difference map indicative of differences between a first image and a second image to generate a predicted difference map; determining a confidence map based on the predicted difference map; determining an error based on the confidence map and a comparison of the predicted difference map and a ground-truth difference map; and adjusting one or more parameters of the machine-learning model based on the error.

A SAR image change detection method based on a dual-stream hierarchical fusion engine network includes the following steps: A dual-stream hierarchical fusion encoder is constructed to collaboratively extract global and local features from SAR images at different time phases. The enhanced features are then used in a hierarchically aware U-shaped decoder to achieve hierarchical modeling and refined representation of the difference features. A frequency-domain channel attention mechanism with fused spatial weights is designed to enhance channel selectivity in the frequency domain and strengthen the response to change regions in the spatial dimension. A noise-resistant weighted loss function based on the difference map ablation coefficient is constructed to adaptively adjust the loss weights of each region, achieving noise region suppression and change region enhancement, effectively mitigating gradient bias caused by class imbalance and speckle noise. Training includes a dual-stream hierarchical fusion encoder and a U-shaped decoder network; SAR images from different time phases are input into the trained dual-stream hierarchical fusion engine network, which outputs a change detection binary map.

The application relates to a SAR image change detection method based on fusion difference maps and morphological reconstruction, which comprises the following steps: S1, acquiring two SAR images of the same region at different times; S2, obtaining a logarithmic mean ratio difference map and a logarithmic ratio difference map according to the two SAR images; S3, performing SLIC superpixel segmentation on the logarithmic ratio difference map to obtain a superpixel segmentation difference map; S4, performing wavelet fusion processing on the logarithmic mean ratio difference map and the superpixel segmentation difference map to obtain a wavelet fusion difference map; S5, performing morphological reconstruction on the wavelet fusion difference map to obtain a final difference map; and S6, processing the final difference map by using a fuzzy C-means clustering algorithm to output a detection result map. The method can better utilize the information of neighboring pixels, has excellent detection effects on different types of SAR images, has very good robustness to noise, and can maintain the details of the image.

This invention discloses a high-quality hybridflow image compression method at extremely low bit depths, comprising: processing the input image by a continuous coding branch to obtain continuous bitstream bits, and decoding loopbacks to generate continuous reconstructed latent variables; processing the input image by a discrete branch to generate vector-quantized continuous latent variables; calculating a cross-stream difference map based on the continuous reconstructed latent variables and the vector-quantized continuous latent variables; performing masking decisions based on the cross-stream difference map to generate a final mask and transmission strategy map; organizing and encoding hierarchical syntax based on the final mask and transmission strategy map to obtain mask bits and index-related bits; and packetizing the continuous bitstream bits, mask bits, and index-related bits to output a bitstream. This invention solves the problems of inaccurate masking decisions and low indexing coding efficiency, achieving high fidelity and high perceptual quality at extremely low bit depths.

The application discloses a kind of for low-illumination wide field of view video image significant change detection method and device, method includes: using logarithm ratio operator and subtraction operator respectively obtains global difference map and carries out fusion;Using frequency domain attention mechanism obtains the significant change area of global difference map;Combining morphological filtering, and energy feature and density feature are handled to noise, and the local change area pair where target is located is extracted;Using logarithm ratio operator extracts the difference feature of local change area pair, obtains local change difference map;Weighted fusion is used to carry out feature optimization to local change difference map, obtains local feature fusion map;Local feature fusion map is classified using k-means clustering algorithm to generate corresponding local change map, and all local change map is integrated into global change map, obtains the final change detection result.The device includes: processor and memory.

The invention discloses a multi-dimensional fairness automatic analysis method and system for a large image model, and the method comprises the steps: extracting sensitive attribute tags of an input image sample, and carrying out the classification according to sensitive attributes; carrying out attribute change processing on the input image sample based on the sensitive attribute type; extracting an output representation of the image pair after the image pair passes through the image large model, and calculating an output change index before and after attribute change processing of the input image sample according to the output representation; judging whether the output change index before and after the attribute change processing is higher than a first preset threshold value, if so, further calculating a fairness index quantification score, and comparing the image activation graphs of the input image sample and the attribute change image sample to obtain a difference graph when the score is higher than a second preset threshold value; and positioning a specific semantic region generated by model deviation according to the difference chart. According to the invention, a structured and automatic evaluation process can be provided, and the evaluation efficiency is improved.

The invention provides a luminous character defect detection method, which comprises the following steps of: after obtaining a difference chart of luminous characters, extracting each independent difference area in the difference chart, generating morphological structure elements with corresponding sizes and orientations based on a minimum enclosing rectangle of each independent difference area, and respectively adopting each morphological structure element to detect the defects of the luminous characters. Performing morphological closed operation on the difference image, extracting each independent difference region from the difference image after the morphological closed operation, and judging whether the defect is a pseudo defect or not based on the geometrical characteristics of each independent difference region; in this way, pseudo defects in the difference image are recognized and eliminated, the problem of defect false detection caused by glittering character edge burrs, registration errors and the like is solved, and the detection accuracy is remarkably improved.

The invention provides a digestive endoscopy decontamination quality evaluation method based on image recognition, and the method comprises the steps: constructing a shared and differentiated feature extraction double-flow convolutional neural network through the double-flow input of original and simulated degraded images, and fusing multi-scale features; a LINGAM causal discovery and gradient mask mechanism is introduced, a causal relationship between an image quality factor and residual judgment is dynamically modeled, and implicit causal graph optimization is realized; a causal intervention feature reweighting and anti-fact attribution mechanism is adopted to correct the feature weight of the low-quality region and improve the robustness of the model; according to the method, multi-task joint loss and progressive causal freezing training are carried out, so that the model has precision and interpretability, an image quality factor, residual judgment and a causal difference graph are finally output, dynamic and multi-dimensional quality closed-loop management and auxiliary decision making in a clinical scene are realized, and the analysis reliability of an endoscopic image in a complex environment is effectively improved.

The application relates to a pedestrian anomaly detection method and device and a storage medium, wherein the method comprises the following steps: inputting a first modality image and a second modality image into a target detection model to respectively extract a first modality feature map and a second modality feature map; calculating the difference between the first modality feature map and the second modality feature map via a differential attention mechanism module in the target detection model to generate a feature difference map, and calculating an attention weight based on the feature difference map; fusing the first modality feature map and the second modality feature map based on the attention weight via a feature fusion module in the target detection model to obtain a fused feature map; inputting the fused feature map into a detection head in the target detection model, and outputting a predicted pedestrian position and a predicted pedestrian staying state; and generating a pedestrian anomaly detection result based on the predicted pedestrian position and the predicted pedestrian staying state. Through the application, the problem of low accuracy of pedestrian anomaly detection is solved.

The application discloses a kind of based on optimization's guiding filter image fusion change detection method and device, method includes: by extreme minimum scale difference operator, pixel value difference value is to the picture of same scene different phase, obtains difference map;Using the way of guiding filter, difference map is fused on global scale, to obtain the optimal difference map;Mean filter is used to phase map I1 and I2, and the base layer difference map corresponding to each difference map is obtained, and the optimized brightness saliency map F1 and F2 are obtained by pca fusion as the image of generating saliency map, according to the principle of maximum saliency to determine weight map;Weight map is regularized for double-scale difference map reconstruction, and the final difference map is obtained;In clustering segmentation stage, by introducing soft threshold function, the final difference map is further processed, to suppress existing abnormal noise.The device includes: processor and memory.

The invention relates to the technical field of education management, and discloses a learning cognitive path planning system based on a cognitive map. Comprising a cognitive map construction module for recognizing subject knowledge points, extracting a dependency relationship and constructing a cognitive map; the evaluation portrait construction module is used for constructing a learning evaluation portrait; the difference atlas drawing module is used for identifying a difference atlas in the cognitive atlas; the path calculation and screening module is used for calculating the path value of the feasible path and screening out a learning cognitive path; according to the method, the cognitive dynamic change condition of the learner can be regularly monitored and analyzed, the continuous cognitive updating effect of the learner along the timeline is realized, and the limitation and hysteresis existing when a learning evaluation portrait is constructed by single-dimensional static data are avoided; and the phenomenon that part of associated subject knowledge points are missed and lost due to direct comparison of the real-time cognitive level and the planned cognitive level is avoided, and the planning accuracy of the learning cognitive path is improved.