43 results about "Fuzzy graph" patented technology

The invention provides an image visual identification processing method for foundation pit monitoring, and belongs to the technical field of data processing, and the method comprises the steps: 1, carrying out the multi-modal image and motion information collection of a foundation pit; 2, the background server carries out multi-modal feature fusion and blurred image screening on the foundation pit image; step 3, the background server performs rigid-non-rigid structure classification and feature enhancement based on the multi-modal feature map; 4, the background server performs dynamic fuzzy kernel construction and adaptive deblurring on the rigid structure area and the non-rigid structure area; and 5, based on the complete deblurred image, fine crack identification of the foundation pit is carried out. Through four core steps of multi-modal feature fusion extraction, dynamic fuzzy kernel construction, non-rigid structure enhancement and refined crack identification, efficient deblurring and accurate crack identification of a foundation pit image are realized.

The invention relates to an implicit diffusion super-resolution assisted remote sensing image small target detection and identification method. The method comprises the following steps: acquiring a training data set; obtaining a clear-fuzzy sample pair; and introducing the target classification model and the implicit diffusion model into the target detection and recognition model to obtain a small target detection and recognition model. The recognition method is used for effectively supplementing feature information when small target detection and recognition are carried out, so that the accuracy of small target detection and recognition is improved. According to the invention, aiming at a difficult point with less effective information in small target detection and identification, a low-resolution blurred image is gradually recovered to a high-resolution clear image by means of an implicit diffusion model based on deep learning, super-division of an image area where a small target is located is realized, and the definition of the area image is improved, so that feature information is enriched, and the detection and identification efficiency is improved. The problems of insufficient feature information, unclear target boundary, susceptibility to noise interference and the like in small target detection and recognition are relieved, and the overall effect of remote sensing image target detection and recognition is finally improved.

The invention discloses a dynamic scene deblurring method and system based on physical information adversarial learning, and the method comprises the steps: obtaining original blurred image data, carrying out the preprocessing of the original blurred image data, and obtaining a preliminary deblurred image; inputting the preliminary deblurred image into an initial dynamic scene deblurring model for training to obtain a dynamic scene deblurring model; the initial dynamic scene deblurring model comprises a generator network, an optical flow estimation network, a three-stage progressive training strategy and a multi-scale discriminator network, the generator network is used for mapping an initial deblurred image into a deblurred image, and the optical flow estimation network is used for estimating a motion field and calculating optical flow consistency loss; the three-stage progressive training strategy is used for carrying out three-stage training on the initial dynamic scene fuzzy model in sequence, and the multi-scale discriminator network is used for judging image authenticity on different scales; and inputting a to-be-processed blurred image into the dynamic scene deblurring model to obtain a blurred image. According to the invention, the deblurring effect is improved.

The invention discloses a fuzzy type adaptive image restoration method, which belongs to the field of image processing and computer vision, and comprises the following steps: carrying out frame extraction processing on an input video stream, and preprocessing extracted image frames; based on the preprocessed image, a comprehensive fuzzy score is calculated through a multi-feature fuzzy evaluation mechanism; judging whether the image frame is a fuzzy frame according to the comprehensive fuzzy score; if the image frame is a fuzzy frame, judging the fuzzy type of the image frame through a preset rule according to a plurality of evaluation scores in a multi-feature fuzzy evaluation mechanism; selecting a corresponding restoration strategy from a plurality of preset restoration strategies to restore the image frame, and generating a restored image; performing quality evaluation on the repaired image, and if an evaluation result does not meet a preset condition, adjusting a repairing strategy and repairing again; and re-inserting the repaired image meeting the preset condition into the input video stream, and outputting the repaired video. According to the invention, efficient identification and accurate restoration of the blurred image frame are realized.

According to the high-speed fuzzy license plate character recognition method based on deep learning, a deep convolutional generative adversarial network BLPDCGAN generates a high-quality fuzzy image similar to a real fuzzy license plate, a data set is expanded, and the model generalization ability is improved. The generation module introduces a noise image to enhance the blurring effect, and optimizes the quality of the generated blurred image through MSE loss and adversarial loss. The blurred license plate character recognition module uses a Unet architecture for reference, integrates a CBAM attention mechanism and multi-scale feature fusion, enables the model to pay more attention to character features rather than noise, and improves the feature extraction capability of blurred images. The feature enhancement module further enhances the structure and texture extraction of the fuzzy characters, and increases the robustness and recognition precision of the model to the characters in the fuzzy scene. According to the multi-scale feature fusion strategy, features with different resolutions are aggregated, so that the model can capture key feature details of a fuzzy region and optimize the overall recognition effect.

The application discloses a kind of unmanned aerial vehicle image enhancement method and system.Therein, the method includes: obtaining first fuzzy image and its corresponding semantic prompt text;The structural feature extraction is carried out to first fuzzy image, and the semantic feature extraction is carried out to semantic prompt text, to obtain prior information;Prior information is injected into denoising model using cross attention mechanism;First fuzzy image is input into the denoising model injected with prior information to carry out multiple rounds of training and obtain target denoising model;Second fuzzy image is obtained, which is subjected to frequency domain transformation and input into frequency domain residual error model for multiple rounds of training to obtain target frequency domain residual error model;The fuzzy image to be detected is input into target denoising model for denoising to obtain spatial clear image;The fuzzy image to be detected is input into target frequency domain residual error model for prediction to obtain frequency domain clear image;Spatial clear image and frequency domain clear image are fused to obtain high-resolution image.The method can obtain higher quality clear image.

The invention relates to the technical field of three-dimensional reconstruction, and discloses a three-dimensional scene reconstruction method and device, equipment, a storage medium and a program product, and the method comprises the steps: carrying out the initialization of a three-dimensional Gaussian ellipsoid based on an obtained fuzzy image sequence and an event sequence, and obtaining an initial Gaussian ellipsoid; constructing a loss function based on a clear picture sequence obtained by rendering the fuzzy picture sequence through the initial Gaussian ellipsoid and geometric constraints of an event-ungenerated area corresponding to the fuzzy picture sequence and the event sequence; taking the minimum value of the loss function as an optimization target, and adjusting the parameter information of the initial Gaussian ellipsoid to obtain a target Gaussian ellipsoid; and performing three-dimensional scene reconstruction based on the target Gaussian ellipsoid to obtain a target three-dimensional scene. Therefore, three-dimensional reconstruction is carried out in combination with a fuzzy picture sequence obtained by a traditional camera and an event sequence obtained by an event camera, the reconstruction effect of a three-dimensional scene is improved, geometric constraint is added to guide and adjust a Gaussian ellipsoid, and the reconstruction effect of the three-dimensional scene is further improved.

The application belongs to the technical field of simultaneous localization and mapping, and particularly relates to a SLAM algorithm based on feature reinforcement and motion judgment in a dynamic scene, which applies a feature reinforcement instance segmentation network FENET and comprises the following steps: step S1, collecting image information and realizing feature recovery of a dynamic fuzzy object through a fuzzy feature recovery module; step S2, guiding a model to focus on key features of an object based on a reinforced feature recognition mechanism, and recognizing potential dynamic objects; and step S3, jointly estimating the pose of a camera itself and judging the motion of an object to remove a dynamic object. The application can reconstruct and recover lost feature information from a fuzzy image, greatly improves the recognition accuracy of a system for a dynamic object, greatly improves the recognition accuracy of a dynamic object, and avoids misjudgment of static features.

The invention discloses a small sample hyperspectral image classification method and device based on fuzzy contrast graph convolution. The method comprises the following steps: determining membership information, fuzzy node initial feature vectors and similarity of each initial graph node corresponding to each fuzzy cluster based on a hyperspectral image; generating a fusion weight, a fusion feature and a target feature according to convolution features output by convolution of different layers of fuzzy images; performing linear transformation operation and probability distribution operation on the target features in sequence to obtain the probability that the corresponding initialized graph node belongs to each category; and performing argmax operation on the probability to obtain a category index corresponding to the hyperspectral image. The uncertainty of pixels is effectively processed through fuzzy learning, interference of neighborhood pixels is weakened, the uncertainty between the pixels is better processed, and meanwhile richer and more expressive feature representation is obtained. The method has higher classification effect and robustness, and plays an important role in multiple fields of mineral exploration, environment monitoring, forest management, precision agriculture and the like.

The invention relates to the technical field of image display, in particular to a method and device for generating a fuzzy layer of an auxiliary screen, a display screen and a vehicle. The screen is divided into a main screen and an auxiliary screen, the auxiliary screen is simulated through a virtual screen, and the auxiliary screen covers display content, located in an auxiliary screen display area, of the main screen; under the condition that a first image layer displayed on the auxiliary screen comprises a fuzzy image layer, the method comprises the steps that a second image layer which is located below the fuzzy image layer and has an intersection with the fuzzy image layer is determined, and the second image layer is an image layer on the main screen; under the condition that the second image layer meets a first condition, performing fuzzy processing based on rendering information of an application corresponding to the second image layer to generate a fuzzy image layer; and under the condition that the second image layer meets a second condition, obtaining a snapshot of the main screen, and performing fuzzy processing on the snapshot to generate the fuzzy image layer.

The invention relates to the technical field of power grid safety protection, in particular to a collaborative false data injection attack detection method and device, electronic equipment and a medium. The method comprises the following steps: acquiring a current node feature and a current edge feature of a power system; based on the current node features and the current edge features, a first target feature matrix is generated by using a preset fuzzy graph convolutional network model, and the preset fuzzy graph convolutional network model is obtained by integrating a fuzzy learning module and a graph convolutional network; and inputting the first target feature matrix into a preset multi-agent detection module to obtain a current attack detection result of the power system. Therefore, the problems that limitation exists when an existing detection technology processes a complex non-linear relation, and accuracy and interpretability of a detection result are insufficient are solved, and therefore effectiveness and accuracy of collaborative false data injection attack detection are remarkably improved.

The application provides a fluorescence cell multilayer microscopic imaging method and system based on multi-feature fusion, and relates to the technical field of microscopic imaging. The method comprises the following steps: acquiring a multilayer Z-axis image sequence of a fluorescence cell sample at an X-Y scanning point; dividing an optimal focal plane image into blocks, and identifying the definition state through a convolutional neural network classification model; searching for a replacement block in a neighboring Z layer according to the classification result for a fuzzy block; and outputting a full field of view image after local registration and feathering fusion. The application solves the problems of unstable automatic focusing and insufficient single focal plane coverage under weak fluorescence conditions by fusing multi-feature definition evaluation and deep learning block classification, and realizes full field of view clear imaging of a fluorescence sample with uneven thickness.

The application relates to the technical field of vehicle speed identification methods, and discloses a fuzzy image frame analysis vehicle speed identification method based on field reproduction, wherein, according to the differences of a camera, a field environment and road conditions, a field reproduction positioning measurement method in the fuzzy image vehicle speed identification method is divided into a direct measurement method and an indirect measurement method. Through the use of the method, the limitations and deficiencies of target vehicle calibration distance selection in the fuzzy video image for vehicle driving speed identification are overcome, the problems of identification failure and inaccuracy are solved, most of the video image identification materials which cannot be identified by using the traditional method regain the use value, the problems caused by the defects of the identification materials are solved, the requirements for the identification materials are reduced, the problems of fruitless vehicle speed identification and inaccurate vehicle speed identification results are solved, the scientificity and accuracy of the identification opinions are improved, and strong evidence support is provided for the traffic police department for accident handling.

This invention proposes a dynamic scene deblurring method and system based on 4D Gaussian representation and pseudo-ground value supervision. The method includes: obtaining a sharp image and a rendered depth map from a 4D Gaussian representation; predicting the motion velocity of dynamic pixels using a velocity MLP network based on the rendered depth map; constructing a fuzzy weight network and predicting the contribution weight of each sampling point on the sampling trajectory; fusing the pseudo-sharp image with the original blurred input using a dynamic region mask to obtain a hybrid pseudo-ground value image; synthesizing a physically blurred image through weighted integration; and constructing a dual-domain reconstruction loss and geometry-motion regularization constraints by combining hybrid pseudo-ground value supervision and gradient decoupling strategies to jointly optimize the scene representation, motion parameters, and fuzzy model, thereby obtaining a sharp dynamic scene representation. This invention achieves explicit modeling of the physical blurring process through 4D Gaussian representation and a fuzzy weight network, improving the realism and view consistency of motion blur synthesis.

The application discloses a kind of fuzzy type self-adapting image restoration methods, belong to image processing and computer vision field, comprising: frame extraction processing is carried out to input video stream, and the image frame extracted is preprocessed;Based on preprocessed image, through multiple feature fuzzy evaluation mechanism, integrated fuzzy score is calculated;According to integrated fuzzy score, whether image frame is fuzzy frame is judged;If image frame is fuzzy frame, then according to multiple evaluation scores in multiple feature fuzzy evaluation mechanism, the fuzzy type of image frame is discriminated by preset rule;From multiple preset repair strategies, a corresponding repair strategy is selected to repair image frame, and a repaired image is generated;The quality of the repaired image is evaluated, and if the evaluation result does not meet the preset condition, the repair strategy is adjusted and re-repaired;The repaired image that meets the preset condition is re-inserted into the input video stream, and the repaired video is output.The application realizes the efficient identification and accurate repair of fuzzy image frame.

The invention provides a fuzzy graph neural network method for large-area flight delay prediction. The technical problem of flight delay accurate prediction under the condition of large-area flight delay is solved. According to the technical scheme, firstly, a civil aviation data set is selected, flight feature data are extracted and standardized with key information of flights as node identifiers, and a node feature matrix V is formed; then, constructing a directed graph, and establishing a relationship among edge capture flights through various relationships; thirdly, a sparse constraint function is introduced, the fuzzy similarity between flights is calculated, and a fuzzy adjacency matrix AF is constructed; and finally, based on the fuzzy adjacency matrix AF, constructing a fuzzy graph convolution operator, establishing a fuzzy graph neural network model, and performing flight delay prediction. The flight delay prediction method has the beneficial effects that the accuracy of flight delay prediction is improved, the capability of processing complex dependency relationships and uncertain information is enhanced, and the re-flight decision under large-area flight delay is supported.

The invention discloses an image information anti-counterfeiting encryption method and application thereof in transparent plastic package anti-counterfeiting encryption, and the image information anti-counterfeiting encryption method comprises the steps: extracting a gray scale coding matrix of original image information, and carrying out the fuzzy processing of the gray scale coding matrix of each channel, and obtaining a fuzzy image information matrix; generating a random fuzzy matrix for each channel by using a random function; generating an encryption share matrix based on each channel fuzzy image information matrix and the random fuzzy matrix; performing error diffusion and channel combination on the random fuzzy matrix and the encryption share matrix of each channel to generate a first encryption share and a second encryption share; superposing the first encryption share and the second encryption share to obtain a decrypted image approximate to the target image; the method realizes lossless encryption of gray and color images, ensures that the encrypted image is consistent with the original image in size, is simple and convenient in encryption process and visual in decryption, and is adaptive to a transparent plastic bag printing process.

The application provides a power transmission line defect detection method based on improved YOLOv5 and fuzzy image enhancement, comprising the following steps: constructing a defect detection model based on improved YOLOv5; constructing a fuzzy image enhancement algorithm based on a generative adversarial network; collecting power transmission line image data in multiple scenes and performing pretreatment, and using the pretreated image data to train the generative adversarial network in the fuzzy image enhancement algorithm and the defect detection model; using the trained fuzzy image enhancement algorithm and defect detection model to perform frame-by-frame detection on the collected power transmission line inspection video, and first reconstructing the image through the fuzzy image enhancement algorithm and then performing defect detection through the defect detection model. The application combines the improved YOLOv5 model and the fuzzy image enhancement technology to perform power transmission line defect detection, improves the defect detection precision of the model on small-size components of the power transmission line, and can perform multi-scale target detection and fuzzy target detection in the field of power transmission line defect detection.

A hyperspectral and laser radar data classification method and system based on KAN, comprising: performing convolution feature extraction on a hyperspectral image and laser radar data respectively to obtain shallow hyperspectral features and shallow laser radar features; performing depth separable convolution and gate adaptive fusion, layer normalization and state space duality modeling, depth convolution enhancement and residual connection on the shallow laser radar features, and then using a KAN network to replace a feedforward network to perform nonlinear mapping to obtain deep laser radar features; performing fuzzy graph convolution processing on the shallow hyperspectral features and the deep laser radar features to obtain hyperspectral branch graph features, laser radar branch graph features and fusion branch graph features of the two; performing multi-view canonical correlation analysis processing on the hyperspectral branch graph features, the laser radar branch graph features and the fusion branch graph features to obtain multi-view enhanced features; and performing gate weighted fusion on the multi-view enhanced features, and outputting a final classification result through a fully connected layer.

The invention provides an automatic commodity scene supplementing method and device based on a BAGEL model, equipment and a medium, and the method comprises the steps: carrying out the processing of a set commodity image through employing a BiRefNet model, and obtaining a commodity main body image; constructing a preset cue word template, inputting the commodity main body graph into the multi-modal model, and generating a corresponding scene description cue word according to the preset cue word template; inputting a commodity main body graph and the scene description prompt word into a BAGEL model, wherein the BAGEL model generates a required scene commodity graph; determining Gaussian filtering kernel parameters according to the size of the scene commodity image, converting the scene commodity image into a scene commodity grey-scale image, and performing Gaussian blur on the scene commodity grey-scale image through the Gaussian filtering kernel parameters to obtain a scene commodity fuzzy image; and mixing the commodity graph and the scene commodity fuzzy graph according to a soft light mode to obtain a scene commodity final graph, so that the proportion of the commodity in the scene is balanced, and the composition coordination is improved.

The present application provides a kind of foggy traffic scene image semantic segmentation method and related equipment, comprising: obtaining foggy traffic scene image set;Fuzzy image semantic segmentation network including double-task feature extraction module, mapping module for projecting image from Euclidean space to graph space, confidence pairing enhancement module for capturing the dependency between feature maps, back mapping module for projecting image from graph space to Euclidean space, mutual supervision module is constructed;Foggy traffic scene image set is input fuzzy image semantic segmentation network and is trained, obtains the fuzzy image semantic segmentation network after training;The target traffic scene image to be processed is input fuzzy image semantic segmentation network after training and carries out semantic segmentation, obtains segmentation result;Compared with prior art, it is still possible to improve the accuracy of semantic segmentation according to the guidance of the same task under the condition that the label data is defective, so as to improve the safety of intelligent driving.

The application relates to a spaceborne SAR signal post-facto fine analysis system and belongs to the field of radar signal reconnaissance; can complete time-frequency analysis and PDW parameter measurement functions; can perform pulse rapid retrieval based on a time scale, realizes fine analysis on a single pulse signal, and includes a fuzzy diagram, a spectral peak degree coefficient and the like; has a radiation source signal sorting function, can manually add a radiation source database, can automatically store a radiation source result obtained through detection and collection into a warehouse, and can sort known radiation sources according to the warehouse; GPU is adopted to accelerate processing of an analysis algorithm, time-frequency analysis speed can reach 1.6GS / s, and PDW measurement speed can reach 80MS / s; the whole processing system is completed in a 2U server disk array all-in-one machine, the system has high integration degree and full functions.

The invention provides a power transmission line icing weight estimation method based on image splicing and sag inversion. An icing image of a power transmission line is shot through an unmanned aerial vehicle; an improved total variation regularization algorithm is adopted to carry out enhancement processing on the blurred image, and the detail definition is improved; feature points are extracted by using an SIFT algorithm and dynamic matching is carried out, and image splicing is realized by combining a weighted fusion algorithm; extracting the contour of the power transmission line based on the spliced image and calculating the maximum sag; a dynamic relation model of the icing weight and the sag is established by combining a parabola model and temperature and wind load factors, the icing weight is inversed, and compared with the prior art, through a combined strategy of total variation regularization and SIFT dynamic matching, the feature point matching accuracy under a complex environment reaches 95.8%, the sag measurement error is only 1.337%, and the accuracy of the feature point matching is greatly improved. The precision and robustness of icing weight estimation are remarkably improved, and reliable technical support is provided for power transmission line anti-icing disaster reduction.

The application provides a scanning file fuzzy retrieval self-adaptive splicing method, and relates to the technical field of document digitization and computer vision. The method comprises the following steps: obtaining a plurality of fuzzy image blocks obtained by non-fixed block scanning of a long document by a mobile terminal, wherein the long document comprises a layout structure composed of text lines and lines; performing local fuzzy retrieval on each fuzzy image block to extract local features of each image block, and determining an initial pose relationship and an overlapping area between the image blocks according to the local feature matching; determining a local rigid texture primitive in the overlapping area of each image block; performing preliminary splicing on the plurality of fuzzy image blocks according to the initial pose relationship and the overlapping area to obtain a preliminary splicing image, and determining a global topological constraint manifold feature point in the preliminary splicing image. The application can realize accurate splicing of fuzzy image blocks and layout structure restoration.

The invention discloses a fish ingestion intensity discrimination network method and system, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: recovering a network based on a fuzzy frame, carrying out the dynamic reconstruction of a fuzzy image, and outputting a clear image sequence to a TNT network model; a feature pyramid structure is adopted to adjust the scales of feature maps in different stages, and a shunt attention mechanism is utilized to extract multi-scale semantic features; carrying out step-by-step cutting on the redundant features based on a dynamic Token rarefaction mechanism, completing multi-scale semantic aggregation through a shunt attention mechanism, and constructing a space-time representation structure; according to the method, image reconstruction and group feeding behavior dynamic identification in a complex water body environment are realized, a time sequence optimization feeding strategy is constructed, and intelligent feeding precision and adaptability are improved.

The application discloses a kind of multi-constraint quality of service oriented to software-defined internet of vehicles routing method and device, the method includes: in preset length, for every two adjacent nodes in user node set, the value of each quality of service parameter is measured in real time, and measurement parameter set is obtained;For each quality of service parameter, the membership degree of all adjacent nodes in user node set about quality of service parameter is calculated, and the determination value between all adjacent nodes is calculated according to preset determination function;In topological graph, the edge between adjacent nodes that determination value does not satisfy preset screening requirement is deleted, and basic graph is obtained;The comprehensive membership degree of all adjacent nodes in basic graph is calculated, and fuzzy graph is obtained;Path connecting source node and destination node is searched in fuzzy graph, and multiple routing links are obtained;Optimal routing link is selected from multiple routing links.The method can effectively optimize path calculation efficiency and improve data transmission performance.

The application provides a kind of image visual identification processing method for foundation pit monitoring, belong to data processing technical field, it includes: step 1: to the foundation pit carries out multimodal image and motion information acquisition;Step 2: background server carries out multimodal feature fusion and fuzzy image screening to foundation pit image;Step 3: background server carries out rigid-nonrigid structure classification and feature enhancement based on multimodal feature map;Step 4: background server constructs dynamic fuzzy kernel and self-adapting deblurring for rigid structure area and nonrigid structure area;Step 5: based on complete deblurring image, carry out fine crack identification of foundation pit.Through four core steps of multimodal feature fusion extraction, dynamic fuzzy kernel construction, nonrigid structure enhancement and fine crack identification, the efficient deblurring and crack accurate identification of foundation pit image are realized.

The invention discloses a low light blurring image processing algorithm based on an improved LEDNet model, and belongs to the technical field of image processing, and the algorithm comprises the following steps: S1, constructing a low light blurring-clear image data set fitting a police service scene; s2, constructing an improved low-light blurred image processing network based on the LEDNet; s3, a multi-task loss function is designed, the LEDNet network is improved, and the multi-task loss function comprises pixel-level loss, perception loss and improved structural similarity loss; and S4, performing model training and image processing, and training the improved LEDNet network based on the data set. According to the low-light blurred image processing algorithm based on the improved LEDNet model, the processing effect of the low-light blurred image is improved, a new scheme is provided for landing application of the deep learning technology in public security actual combats, and meanwhile reference is provided for design of a police service adaptive multi-task image processing model.

The application provides a kind of detection method, system, storage medium and computer of vehicle plastic parts, the method comprises: real-time acquisition vehicle plastic parts is detected image, and corresponding separation threshold is constructed, and according to separation threshold, feature separation is carried out to the image to be detected to obtain feature image;Increase regression prediction in convolution neural network model to obtain deep learning model;Get the labeled image of detection completed, and the labeled image is fuzzy processed, the obtained fuzzy image and labeled image are calculated, the obtained fuzzy degree is input into deep learning model for optimization, to obtain deep learning optimization model;Feature image is input into deep learning optimization model for image processing, and the detection result of image to be detected is generated according to image processing result.The application optimizes deep learning model using fuzzy degree, and uses the constructed deep learning model to carry out image processing to feature image, to generate corresponding detection result.