72 results about "Background reconstruction" patented technology

The invention provides a differential Mama-based adaptive background reconstruction hyperspectral anomaly detection method, and solves the problem of poor detection performance caused by insufficient local detail reconstruction precision in the prior art. Comprising the following steps: 1) acquiring an original hyperspectral image, and dividing the original hyperspectral image; 2) constructing an adaptive background reconstruction hyperspectral anomaly detection model which sequentially comprises an encoder, a differential state space DSSM model, a weight-guided center feature reconstruction WCBR module, a space-spectral attention SSA module and a decoder based on a traditional differential Mamba architecture; 3) adopting an L2 norm as a loss function of the detection model, and guiding the model to be trained to converge; and 4) inputting the original hyperspectral image into the trained final detection model to obtain a reconstructed hyperspectral image, and calculating to obtain an anomaly detection result. According to the method, the background reconstruction effect can be improved, abnormal feature expression can be remarkably inhibited, and the hyperspectral anomaly detection performance is effectively improved.

The invention discloses a two-stage hyperspectral image band selection and target detection method, which comprises the following steps of: based on a two-stage band selection and background reconstruction network of a transformer, realizing band selection through a first-stage training transformer and realizing background reconstruction through a second-stage training transformer; a transform position coding module and a multi-head self-attention mechanism are used for learning similar features and difference features among wave bands, a full connection layer network is used as a clustering device, a structural similarity index and an Euclidean distance are added to serve as a loss function training model, wave band clustering is achieved, and finally a local variance is used for estimating the noise level of an image of each wave band. Obtaining a band training subset; the band subset is subjected to constraint energy minimization detection and then sent to a background feature extraction network composed of a transformer and a discriminator, background pixels are arranged through position coding, network learning background features are enhanced through a multi-head self-attention mechanism, an improved loss function is used for training, and therefore reconstruction of a background image is achieved. The invention provides a two-stage wave band selection and background reconstruction network based on transformer so as to realize hyperspectral target detection. The network comprises a first-stage wave band selection work and a second-stage background spectrum learning work. And difference detection is carried out on the final background reconstruction image and the original image, so that a final task is realized, and the detection precision is improved.

The invention provides a self-encoding hyperspectral anomaly detection method based on double-domain feature learning, and mainly solves the problem of non-ideal detection performance caused by insufficient frequency domain feature mining, missing detection and poor fusion in the prior art. Comprising the following steps: 1) acquiring an original hyperspectral image, and dividing the original hyperspectral image; 2) constructing a self-encoding hyperspectral anomaly detection model based on double-domain feature learning, wherein the self-encoding hyperspectral anomaly detection model sequentially comprises an encoder, a mask-based frequency domain interactive attention MFIA module, an attention-aware visual state space AVSS model, a frequency domain spectrum interactive fusion FSIF module and a decoder; 3) using an L1 norm as a loss function of the detection model, and guiding the model to be trained to converge; and 4) inputting the original hyperspectral image into the trained final detection model to obtain a reconstructed hyperspectral image, and calculating to obtain an anomaly detection result. According to the method, the background reconstruction effect can be improved, abnormal feature expression can be remarkably inhibited, and the hyperspectral anomaly detection performance is effectively improved.

The invention relates to a fire point detection method, device and system based on a multiband infrared image and a storage medium, and relates to the technical field of image data processing. The method comprises the following steps: acquiring multiband infrared image data, and preprocessing the multiband infrared image data; performing feature classification extraction and feature fusion on fire point features of a fire point region and background features of a non-fire point region in the preprocessed multiband infrared image data by adopting a preset fire point detection model to obtain a target fire point segmentation probability graph; wherein the preset fire point detection model comprises an encoder, a fire point segmentation decoder, a background reconstruction decoder and a fusion module; and determining a fire point pixel detection result according to the target fire point segmentation probability graph and the segmentation threshold.

The invention relates to the technical field of automatic driving, in particular to a dynamic scene simulation generation method for automatic driving, which comprises the following steps: step 1, scene initialization and static background reconstruction: utilizing an improved 3D Gaussian splash technology, fusing LiDAR point cloud prior information, reconstructing a large-scale static road environment, and performing semantic decoupling on a scene; step 2, dynamic object and behavior injection: endowing each dynamic object with an initial motion track represented by a learnable Bezier curve, outputting an adjustment instruction for behaviors of other traffic participants by using a generative AI model according to the semantic information of the current scene and a self-vehicle planning track, and finely adjusting a control point of the Bezier curve; the interactive authenticity is improved, human driving behaviors are simulated through generative AI, physical reasonability is ensured through explicit tracks, the test efficiency is improved, and the long-tail scene generation efficiency is improved by 50% or above through conditional generation.

The invention discloses a cable sheath microcrack image identification method based on deep learning. The method comprises the following steps: acquiring a cable sheath image and executing image preprocessing operation; inputting to an improved MAE model, and generating a background reconstruction image and a crack reconstruction image; pixel-level residual fusion is carried out to generate a background shielding image; performing pixel-level fusion on the background shielding image and the preprocessed image to generate a background suppression image; micro-crack recognition operation is executed, and a preliminary crack response heat map set is output through image feature extraction and crack region judgment; executing a heat map accumulative analysis operation, and constructing a multi-scale accumulative heat map; judging a pseudo response risk area according to the local response change rate; response value retraction operation is executed based on the corresponding local area, and a crack heat map after pseudo response suppression is generated; and extracting a high-confidence crack region to obtain a cable sheath microcrack identification result. According to the invention, the precision and robustness of microcrack detection are improved, and the background interference and false detection risk are reduced.

The invention discloses a rockfall detection method, device and equipment, a storage medium and a product, and relates to the technical field of machine vision, and the rockfall detection method comprises the steps that a monitoring picture of rockfall of a road side slope is acquired; performing static background removal processing on the monitoring picture to obtain a background reconstruction image, and performing region labeling on a dynamic target in the monitoring picture to obtain a dynamic region of the dynamic target; inputting the background reconstruction image into a preset YOLO algorithm to obtain a first detection result of the dynamic target, and inputting the dynamic region into a preset classification network to obtain a second detection result of the dynamic target; and the falling point of the rockfall is determined based on the first detection result and the second detection result, that is, the rockfall detection efficiency is improved by improving the rockfall detection rate and the rockfall point detection accuracy.

According to the chemical material detection method and system based on deep learning provided by the invention, the online adaptive training generative adversarial network BR-GAN is introduced, and the generator can dynamically learn background optical feature mapping under the fine tuning condition of production process parameters; meanwhile, a multi-layer convolution discriminator is used for recognizing time correlation of background optical features, so that a defect-free background reconstruction image matched with the current production condition can be generated, and a difference image is generated by calculating pixel-level residual errors and feature-level attention differences of a real-time image and the reconstruction background; microscopic bubble signals are effectively separated from complex backgrounds changing along with technological parameters, and the effect of accurately detecting microscopic bubbles is achieved.

The invention provides a robot multi-modal data enhancement system for an industrial close-range grabbing scene, and relates to the technical field of industrial robots. The system comprises a data preprocessing module which obtains an RGB image composed of a robot execution track and visual information, a depth image and track data; the background reconstruction module is used for carrying out background reconstruction on the RGB image; the adaptive illumination enhancement module generates illumination disturbance images of multiple versions for the RGB image after background reconstruction, and performs fusion processing to obtain an enhanced RGB image; the depth information reconstruction module is used for reconstructing and complementing missing or invalid areas existing in the original depth image; the multi-modal data synchronization verification module is used for carrying out consistency verification; and the enhanced data output module is used for re-packaging and outputting the enhanced RGB image, the depth image, the track data and the corresponding check code. According to the system, on the premise that existing hardware does not need to be replaced, the quality and robustness of multi-modal data are remarkably improved.

The application discloses a three-dimensional static background reconstruction method, device and equipment and a storage medium. Real images and camera parameters of a scene are acquired; initialized 3D Gaussian parameters and the camera parameters are input into a 3D GS renderer; a rendered image output by the 3D GS renderer and the real images are input into a mask predictor; the 3D Gaussian parameters and the mask predictor are trained; the trained 3D Gaussian parameters and the camera parameters are input into the 3D GS renderer, and a rendered image of a reconstructed static background is output. The application is based on the 3D GS renderer and the mask predictor, uses 3D GS to reconstruct a static background from scene data containing dynamic objects, and does not depend on artificial annotation information.

The application relates to an abnormality detection method based on a hyperspectral image. The main body is based on a space-spectrum combined feature extraction method of filtering and low-rank decomposition to perform abnormality detection on the hyperspectral image. The specific method comprises the following steps: firstly, in the spatial dimension, a reduced dimension image is obtained through a data dimension reduction and eigenvalue weighted fusion method, and then an improved spatial filtering method is used to extract the spatial features of the image to obtain an initial spatial feature image. In the spectral dimension, a background reconstruction image of the approximate background is obtained by using a Tucker decomposition method on the original hyperspectral image, and a background dictionary of the image is obtained by using an improved k-means clustering method, then the background dictionary is input into a low-rank decomposition model to obtain a sparse matrix, and an initial spectral feature image is obtained, finally, the initial spectral feature image is fused with the spatial feature image to realize abnormality detection.

The invention provides a model training method and device of a generative model, a generation method and device of the generative model, and equipment. The method comprises the steps of obtaining a target training sample; the target training sample comprises an initial two-dimensional image and mask data used for representing each initial object in the initial two-dimensional image; inputting the initial two-dimensional image and the mask data of each initial object into a to-be-trained generative model to obtain model features of a 3D mesh model representing the initial object in the initial two-dimensional image by using a mid-scene reconstruction branch of the to-be-trained generative model, and reconstructing a background reconstruction branch of the to-be-trained generative model to obtain a model feature of a 3D mesh model representing the initial object in the initial two-dimensional image; background features capable of describing a background area in the initial two-dimensional image are obtained; and performing model training on the to-be-trained generative model based on model features of a 3D mesh model representing an initial object in the initial two-dimensional image and background features describing a background region in the initial two-dimensional image to obtain a target generative model.

The application discloses a hyperspectral remote sensing image target detection method based on target-background reconstruction deviation, comprising the following steps: acquiring a to-be-detected hyperspectral remote sensing image, inputting the pre-trained asymmetric auto-encoding network, and obtaining the first output and the second output of each pixel in the image; the pre-trained asymmetric auto-encoding network comprises a feature extraction subnetwork, a feature fusion subnetwork and a feature reconstruction subnetwork; the first output is the output of the feature extraction subnetwork, and the second output is the output of the feature reconstruction subnetwork; the pre-trained asymmetric auto-encoding network is obtained by training a mixed target spectrum and a mixed background spectrum; the mixed target spectrum and the mixed background spectrum are generated based on a first hyperspectral remote sensing sample image, a prior spectrum of a preset target and a bilinear spectrum mixing model; according to the first output and the second output, the spectral angle distance of the pixel is determined; and the spectral angle distance is smoothed to obtain the detection result of the to-be-detected hyperspectral remote sensing image.

The invention belongs to the technical field of underwater sonar imaging and intelligent identification, and relates to a virtual sonar image generation method and system. The method comprises the following steps: mapping a real optical target image into a virtual sonar target image by adopting a vision-text fusion network; embedding the virtual sonar target image into the real sonar background image to generate a combined image; and background compensation and texture consistency adjustment are carried out on the combined image by using a virtual sonar image background reconstruction network to obtain a virtual sonar image with a background. According to the invention, the bottleneck that real sonar data acquisition is limited by environment, cost and equipment conditions is broken through; meanwhile, the target type and number can be flexibly expanded, and the problems that sonar samples are insufficient in category and unbalanced in distribution are effectively solved.

The invention provides a method and a system for detecting non-metallic inclusions in steel based on background reconstruction. The method comprises the following steps: acquiring a to-be-detected input image; inputting the input image to a background reconstruction module to generate a reconstructed image after inclusion removal; calculating a difference value between the input image and the reconstructed image to obtain a detection result image containing inclusions; the detection result image is processed through a post-processing module to output an inclusion detection result, and the method is low in data labeling requirement, is not influenced by unbalanced inclusion sample types, is higher in algorithm adaptability and robustness, and is higher in accuracy. The problem that a method for detecting non-metallic inclusions in steel is difficult to meet high-precision and high-efficiency quality inspection requirements is solved.

The invention discloses an intelligent switching method and system for face component structured mask learning, and the method comprises the steps: extracting a portrait mask with an edge perception capability, and reconstructing a high-fidelity background region with spatial continuity through combining a background reconstruction network; generating a standardized face image with geometric consistency by adopting a face detection and posture correction network; generating an optical head portrait with vision and texture consistency; performing structured mask learning on the standardized face image and the optical head image by using a semantic segmentation network to realize pixel-level face analysis; obtaining a multi-component region mask through mask calculation based on morphological operation; generating an optical head portrait with a natural transition effect by using an optical head portrait synthesis module based on Poisson fusion; and generating a hair changing portrait with physical authenticity by using a hair changing portrait synthesis module. According to the method, the visual fidelity, the detail consistency and the identity retentivity of the face image after hair changing are remarkably improved.

The invention provides a hyperspectral anomaly detection method based on a multi-scale memory network, and mainly solves the problem of limited model detection performance caused by poor background and anomaly separation in the prior art. According to the scheme, the method comprises the following steps: 1) acquiring an original hyperspectral image, and preprocessing the original hyperspectral image; 2) designing a coarse-to-fine pseudo label generation module, a multi-scale memory module based on separation training, and a consistency and discriminative feature learning CDFL module; 3) constructing a hyperspectral anomaly detection model by an encoder, an intermediate block, a first decoder, an up-sampling block, a second decoder and the designed modules; 4) adopting an L2 norm and a CDFL loss function as a joint loss function to guide model training; and 5) obtaining a detection result by using the trained model. According to the method, the background reconstruction capability of the model can be remarkably improved, the separation degree of the anomaly and the background is improved, and the hyperspectral anomaly detection performance is effectively improved.

The invention discloses an infrared weak and small target detection method and device based on information fusion, and relates to the field of image processing. The method comprises the following steps: constructing continuous image frames of a thermal infrared image sequence to be detected into space-time tensors; designing a spatio-temporal feature extraction network based on a multi-head self-attention mechanism for extracting spatio-temporal information of the spatio-temporal tensor; designing a layered background reconstruction network based on a decoder for reconstructing a background tensor; designing multi-scale structure similarity loss, and carrying out unsupervised learning training; according to the method, the residual error between the space-time tensor and the background tensor is calculated, a sparsity filter is designed to filter the residual error, the target tensor is obtained and reconstructed into an infrared weak and small target detection result sequence, infrared weak and small target detection based on space-time background reconstruction is achieved, and experimental tests prove that the method can effectively improve the comprehensive detection performance of the infrared small target.

The invention discloses an event camera video reconstruction method and system based on active aperture modulation, and the method comprises the steps: introducing an aperture modulation strategy for the first time, and reconstructing an initial frame with good quality by periodically adjusting the opening and closing of an aperture and actively triggering a dense global event signal; the problem of low background reconstruction quality caused by sparse events in a static region in the prior art is solved, and good prior information is provided for subsequent dynamic scene reconstruction. By constructing a forward-reverse bidirectional network, rich intensity information is provided for a static scene, and the problems of background disappearance and error accumulation in long-time operation in a traditional method are effectively solved. And meanwhile, high-time-resolution capture of a dynamic region is kept, and high-fidelity and high-dynamic-range video reconstruction is realized.

The invention discloses a foreign matter segmentation method based on dynamic background suppression and weak supervised learning, and the method comprises the steps: 1) training a static background reconstruction network, screening a foreign matter-free frame from a video as the input of a full-connection automatic encoder, and learning a static background of a scene through minimizing reconstruction loss; 2) inputting a video frame to reconstruct a static background, performing adaptive background modeling by adopting a Gaussian model so as to perform foreign matter judgment, and applying a neighborhood consistency constraint to improve robustness; 3) identifying an area containing foreign matters and a dynamic background, and screening a sequence only containing the dynamic background to train a U-Net network, so that the U-Net network can predict a dynamic background mode; 4) performing pixel-by-pixel operation on the foreground binary image and the dynamic background probability predicted by the U-Net network to generate a foreign matter probability graph; and 5) performing morphological post-processing on the foreign matter probability graph to optimize the integrity of the foreign matter contour. According to the method, static and dynamic background features are learned through dual-network collaborative learning, and the foreign matter segmentation performance in a complex environment is remarkably improved.

The present invention discloses a hyperspectral anomaly detection method based on a dual-line iterative convolutional neural network, which includes the following steps: Step S1, input and parameter setting; Step S2, anomaly enhancement line; Step S3, background reconstruction line; Step S4, anomaly point determination; Step S5, hyperspectral data update; Step S6, detection map generation. In the present invention, the background reconstruction line focuses on background reconstruction, and the anomaly enhancement line is responsible for anomaly enhancement. The two complement each other and promote each other, realizing more accurate anomaly detection and overcoming the problem that it is difficult to effectively combine background reconstruction and anomaly detection in traditional methods. In the present invention, combined with the superpixel technology, an anomaly detection strategy based on comparison with surrounding pixel points is used, which improves the sensitivity to local areas, makes the selection of anomaly targets simpler and more efficient, improves the detection efficiency, and avoids possible computational redundancy and efficiency bottlenecks.

The application discloses a kind of cable sheath micro crack image recognition methods based on deep learning, including the following steps: cable sheath image is collected and image pre-processing operation is executed;Input to improved MAE model, generate background reconstruction map and crack reconstruction map;Pixel-level residual fusion is carried out, and background mask map is generated;Pixel-level fusion is carried out to background mask map and pre-processing image, and background suppression map is generated;Micro crack identification operation is executed, and preliminary crack response heat map set is output by image feature extraction and crack area discrimination;Heat map cumulative analysis operation is executed, and multi-scale cumulative heat map is constructed;According to local response change rate, judge false response risk area;Response value retraction operation is carried out based on corresponding local area, and crack heat map after false response suppression is generated;High confidence crack area is extracted, and cable sheath micro crack recognition result is obtained.The application improves the precision and robustness of micro crack detection, reduces background interference and false detection risk.

This invention relates to a texture primitive extraction method for tire X-ray images, addressing the challenge of accurately obtaining pixel-by-pixel texture information at the individual cord level while suppressing background interference. It falls under the field of computer vision and image processing technology. The method combines frequency domain analysis to obtain texture direction and spacing, utilizes this information to construct a mesh mask, and further combines background point extraction with real background reconstruction to filter out specific background regions in the original image. Under directional constraints, the remaining texture mesh is continuously tracked to obtain pixel-by-pixel texture information, thus achieving texture primitive extraction. This provides a more reliable foundation for subsequent pathological detection, structural analysis, and cord-level texture modeling.

The invention provides a ground coffee foreign matter detection method and device based on background reconstruction, a medium and a product, and the method comprises the following steps: obtaining an original transmission image of to-be-detected ground coffee, and carrying out gray normalization to obtain a standard input image; respectively inputting a standard input image into a background reconstruction branch and a target detection branch; in the background reconstruction branch, generating a pure background prediction image by using a self-encoding model, and calculating a gray difference between the pure background prediction image and the standard input image to obtain a foreign matter residual image; convolutional features are extracted from the foreign matter residual image, and a spatial residual attention weight map is generated; in the target detection branch, performing multi-layer convolution on the standard input image, and performing weighted fusion in combination with the attention weight map to obtain a foreign matter sensitive feature map; and outputting a foreign matter detection result through prediction head network classification and regression decoding. By implementing the technical scheme provided by the invention, the detection sensitivity of low-contrast and low-density foreign matters is improved, and meanwhile, the false detection rate caused by complex powder textures is reduced.

The invention provides a hyperspectral anomaly detection method based on prototype guidance and spatial spectrum interaction, and mainly solves the problem of limited detection performance caused by lack of information complementarity and insufficient background characterization in the prior art. Comprising the following steps: 1) preprocessing data to obtain a mask result and a mass center of a local homogeneous region; 2) constructing a background guidance self-compiler model containing a background guidance prototype and a spatial spectrum interaction module, and performing multivariate background characterization from a global-local angle; 3) designing a joint loss function optimization multivariate background representation based on reconstruction loss and background prototype module loss; 4) inputting an original hyperspectral image and a preprocessing result thereof into the model, and training through a joint loss function; and 5) introducing a mahalanobis distance to evaluate a reconstruction error so as to realize hyperspectral anomaly detection. According to the method, prototype learning and a space-spectrum normal form are combined, multi-element background reconstruction is carried out, effective separation of the background and anomaly is realized, and the detection performance is remarkably improved.

The invention provides a dazzle light removing method. The method comprises the steps that a night light emitting model containing a dominant light source and a non-dominant light source is established through radiation transmission analysis; the method comprises the following steps: acquiring HDR night scene dazzle light damaged image data, and preprocessing and normalizing the data to obtain standardized data; aiming at input data, an unsupervised exposure guide label generation method is adopted, and a dazzle light area around a dominant light source and a non-dominant light source is positioned based on modulus imaging constraint; introducing a Uform network guided by an exposure label, combining with an exposure guide mask attention mechanism, and jointly optimizing a dazzle light and background decomposition and reconstruction process through exposure guide loss; based on a pseudo-label guided light source recovery method, decoupling a dominant overlapped light source under the constraint of exposure and connectivity, and retaining a non-dominant light source; and fusing the non-glare background reconstruction result and the light source reconstruction result to obtain output image data which is free of glare and contains an ideal light source. The method has the advantages of breaking through light source and dazzle light marking dependence, improving the reconstruction precision of the overlapped light source and the weak light source, enhancing the supervised learning ability and effectively removing night dazzle light artifacts.

The application relates to the technical field of computer vision, in particular to a NeRF map construction method and device based on visual SLAM, wherein the method comprises the following steps: in a dynamic scene, acquiring a static target feature point of a current frame image of a camera; tracking the static target feature point, solving a current frame camera pose of the camera, and selecting a key frame according to the number of projection inliers in a tracking process and tracking time; sampling pixel points in the key frame according to ORB features, inputting a key frame sampling result into a pre-established NeRF map model for training, obtaining NeRF map model parameters, and obtaining a NeRF map construction result. The application can generate a dense NeRF map in real time for a dynamic scene, overcome the problem of false matching in the dynamic scene, maintain simplicity with high efficiency, eliminate the influence of dynamic targets, complete high-quality background reconstruction, and effectively meet the positioning and mapping application requirements in different scenes.

Embodiments of the present disclosure disclose a video encoding method, apparatus, electronic device, and computer-readable medium. A specific implementation of the method includes: obtaining a target image sequence; for each target image, performing the following first encoding steps: determining structured description information; generating a background frame image according to at least one historical target image; generating a background reconstructed frame image; generating a structured reconstructed frame image according to the background reconstructed frame image, at least one historical reconstructed frame image, and the structured description information; for each target macroblock, performing the following second encoding steps: determining a first macroblock; generating a first macroblock bitstream; determining a second macroblock; generating a second macroblock bitstream; determining the first macroblock as a prediction macroblock; encoding the target image according to the prediction macroblock set to obtain a first target image bitstream; generating a video bitstream according to the first target image bitstream set. This implementation can improve the video compression encoding efficiency in complex scenarios.

This invention provides a multimodal data augmentation system for robots in industrial near-field grasping scenarios, relating to the field of industrial robot technology. The system includes: a data preprocessing module, which acquires an RGB image, a depth image, and trajectory data composed of robot execution trajectory and visual information; a background reconstruction module, which reconstructs the background of the RGB image; an adaptive lighting enhancement module, which generates multiple versions of lighting perturbation images from the reconstructed RGB image and fuses them to obtain an enhanced RGB image; a depth information reconstruction module, which reconstructs and completes missing or invalid regions in the original depth image; a multimodal data synchronization verification module, which performs consistency verification; and an enhanced data output module, which repackages and outputs the enhanced RGB image, depth image, trajectory data, and corresponding checksums. This system significantly improves the quality and robustness of multimodal data without requiring replacement of existing hardware.

The present application relates to a kind of hyperspectral image anomaly detection method, the present application is based on the background reconstruction subtraction of multiple feature joint to the hyperspectral image is carried out anomaly detection.Specific method includes: first using spatial feature and spectral feature joint improved double window three-edge filtering method to reconstruct background to image;Second, for the distribution characteristics of the abnormal target in the analysis image, on the basis of traditional saliency detection method, propose the saliency feature extraction method based on global context perception to extract the saliency feature map of image;Then the saliency feature map of image and the square difference of three-edge filtering after reconstructing background map are obtained Abnormal target initial detection map;Finally, using spectral correlation coefficient to obtain the spectral weight map of image, and it is fused with initial anomaly detection map to obtain the final anomaly detection result.