386 results about "Computer vision and image processing" patented technology

The invention discloses a high-precision image processing method and system based on illumination adaptive compensation, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting an original image, and dividing the image into a high-frequency edge layer, an intermediate-frequency texture layer and a low-frequency illumination layer through a multi-scale residual network; acquiring illumination intensity, color temperature and scene categories in real time by using an ambient light sensor and a scene semantic segmentation model, and generating dynamic compensation parameters; carrying out dynamic range expansion on a low-frequency illumination layer based on a physical illumination model, and adjusting the weight of highlight suppression and dark area enhancement through a self-adaptive S-shaped exposure curve; a double-branch generative adversarial network is adopted, noise suppression and super-resolution reconstruction are carried out on the high-frequency layer, and texture detail enhancement is carried out on the intermediate-frequency layer; aligning the data of the depth camera and the infrared sensor with the visible light image through a cross-modal fusion module; and performing tone mapping on the fused image based on human visual characteristics, and outputting an enhanced image with a high dynamic range and reserved details.

The invention discloses a body-in-white welding seam recognition and automatic welding method based on a machine vision technology, particularly relates to the technical field of computer vision and image processing, and is used for solving the problem of welding seam track recognition accuracy caused by insufficient processing capability of an existing three-dimensional vision recognition method on incomplete and uncertain point cloud data. Through the steps of multi-view point cloud acquisition and registration, probabilistic confidence evaluation, region growth of track continuity constraint, multi-track fusion optimization and the like, accurate identification of a body-in-white welding seam track under a complex working condition is realized; firstly, multi-view point cloud data are obtained, probabilistic registration is carried out to generate a confidence evaluation result, then candidate tracks are generated based on confidence weighting and semantic constraint, finally, an optimal track is generated through intelligent optimization and converted into a welding instruction which can be executed by a robot, and the accuracy and robustness of weld joint recognition are effectively improved.

The invention discloses a road video event rapid detection method based on edge calculation, particularly relates to the field of computer vision and image processing, and is used for solving the problem of motion evidence distortion caused by frame-level sequential disorder and content gaps in a road monitoring video. The method comprises the following steps of: reading a video clip, constructing a time sequence correction graph according to inter-frame content similarity and boundary continuity, rearranging to generate a corrected sequence, outputting a frame consistency index, estimating a stable motion field and a continuity score on the corrected sequence, and writing back an image stabilization amplitude correction time anchor point; generating an event evidence sequence according to the stable motion field, combining track breakpoints according to a time sequence correction graph, initiating backtracking revision to return candidate event segments, and if consistency verification is executed on candidate events, performing cross-level revision on the time sequence correction graph and image stabilization amplitude to trigger the stable motion field to re-estimate and output a confirmation event; and issuing a trigger result and establishing a mapping index as a new fragment to initialize priori acceleration time sequence correction and judgment convergence.

The invention belongs to the technical field of computer vision and image processing, and discloses a remote sensing small sample target detection method based on double-attention guided transfer learning, and the method comprises the steps: obtaining a remote sensing image data set, and carrying out the preprocessing; taking the preprocessed remote sensing image training set as input, constructing a basic detection model by using a ResNet-101 backbone network, a feature pyramid network and a content awareness upsampling and regional proposal network, and obtaining basic model parameters; basic model parameters are used as input, a DA-FSDET network is trained based on a content awareness strip pyramid and a deformable attention area proposal network, and the trained DA-FSDET network is used to acquire a category detection frame containing small sample categories and confidence. Through cascading and cooperative work of the content awareness stripe pyramid and the deformable attention area proposal network, the detection precision and robustness of the multi-scale target in the remote sensing image are effectively improved.

The invention relates to the technical field of computer vision and image processing, in particular to an infrared polarization image super-resolution method based on a cross attention double-branch network, and the method comprises the steps: obtaining infrared polarization images with two different resolutions in the same scene through two infrared polarization cameras with different image resolutions; constructing a double-branch super-resolution network model, training the double-branch super-resolution network model according to the multiple pairs of image data sets until a robust fitting state is reached, and obtaining the trained double-branch super-resolution network model; and obtaining a high-resolution infrared polarization image from the low-resolution infrared polarization image to be subjected to super-resolution through the double-branch super-resolution network model. According to the method, the infrared polarization intensity image and the infrared polarization degree image are subjected to super-resolution at the same time through the deep learning model, the features are fused mutually, and high-quality super-resolution image reconstruction is achieved.

The invention relates to the field of computer vision and image processing, and discloses a remote controller liquid crystal screen image detection method based on computer vision, which comprises the following steps: S1, acquiring an original image containing a remote controller, and carrying out gray level conversion and filtering denoising processing on the original image to obtain a preprocessed image; s2, calculating the local information entropy and the change rate of the image through a sliding window under multiple scales based on the preprocessed image, generating a multi-scale entropy gradient heat map, and screening out a candidate region with high entropy difference as a liquid crystal display region according to a set threshold value; and S3, carrying out vectorization partitioning on the candidate region, and constructing an image matrix. According to the invention, by introducing an image preprocessing mode of low-rank sparse decomposition and normalization processing, the expression ability of structural information in the character image is effectively enhanced, and the feature extraction accuracy under the conditions of complex background, low character contrast and the like is improved.

The invention discloses an ancient fresco restoration method based on progressive reconstruction and damage perception self-adaption, and relates to the technical field of computer vision and image processing, a damaged fresco image is input into a coarse restoration network for initial restoration, in the initial restoration process, a discriminator and the coarse restoration network are adopted for adversarial training, and a damaged fresco image is obtained; generating an initial restoration result of the mural image; inputting the initial repair result and the mask image into a mask-guided local information extraction network to obtain a local optimization result; and inputting the local optimization result into a global information extraction network to obtain an overall restoration result of the mural image. The problem that an existing network is low in efficiency in the aspect of capturing local details and global styles is solved. The method has good applicability to damaged wall paintings; the problem of fuzzy texture in the repairing result is effectively solved; local features are adaptively extracted and fused according to the damage degree of the mural, and the multi-stage residual information distillation module further refines details of the mural on different scales.

The invention discloses an underwater image enhancement method based on relation-driven state space modeling, belongs to the technical field of computer vision and image processing, and aims to solve the problems of color distortion, detail blurring and the like of an underwater image caused by water attenuation and scattering. Carrying out structure perception enhancement modeling; and image reconstruction and decoding output. Wherein the structure sensing module extracts spatial continuity information through an offset generation network, adaptively rearranges scanning paths, preferentially focuses on a semantic rich region and executes dynamic state propagation, so that the accuracy and interpretability of global modeling are improved; and meanwhile, a local convolution kernel is dynamically generated according to global channel statistical characteristics by inputting a dependent convolution branch, so that the adaptability to a background region is enhanced. In order to further improve the feature fusion effect, a cross-feature fusion bridge module is provided, multi-level features are guided and fused through bidirectional attention of a structural path and a semantic path, and detail information and context semantics are effectively integrated.

The invention discloses an RGBT target tracking network and method fused with a multi-interaction feature enhancement mechanism, relates to the technical field of computer vision and image processing, and aims to solve the problems that multi-modal fusion is insufficient and tracking is easy to drift due to fixed or blind updating of a template. The network adopts an end-to-end tracking framework, a backbone network of the network extracts visible light and infrared template images and searches feature tokens of the images through a convolution token embedding module, and performs intra-modal and inter-modal mixed attention interaction by using a multi-interaction Transform module to realize multi-level feature fusion. A target frame is output by adopting an angular point prediction head, a template updating module is introduced, and a template token is dynamically evaluated and updated through two Transform modules, so that the long-term tracking stability is improved. The network effectively deals with complex environment changes by fusing multi-modal information and a dynamic updating mechanism, and the tracking accuracy and robustness are remarkably improved.

The invention discloses an intelligent identification system for puffed corn, and relates to the technical field of computer vision and image processing, and the system comprises an image collection module which integrates multispectral imaging and a near-infrared sensor and is used for obtaining surface morphology, internal structure and moisture content data of puffed corn; an environment light intensity sensor and an LED array are arranged in the self-adaptive optical compensation module, the wavelength and illumination intensity of a light source are adjusted in a closed-loop feedback mode, and color feature deviation caused by workshop environment light changes is compensated; the image processing module is used for deploying a lightweight hybrid neural network model and is used for adhesive particle segmentation and defect detection; the real-time sorting control module is used for driving a three-axis mechanical arm and a pneumatic spray valve according to an identification result so as to realize defective product rejection and grade subpackaging; and the digital twinning optimization platform is used for outputting a process adjustment suggestion to the production line PLC. According to the invention, through the holographic sensing network, the hybrid intelligent decision and the cross-domain cooperative control, the problems of inaccurate reading, unquick judgment and inaccurate control in puffed corn identification are solved.

The invention discloses a vegetation image recognition method, system, equipment and medium based on OfficientNet, and belongs to the technical field of computer vision and image processing, and the method comprises the steps: collecting data through image collection and synchronous utilization of a laser radar, and carrying out the preprocessing of the collected data; an improved OfficientNet network model is constructed, and training and optimization are carried out by using the improved OfficientNet network model; and carrying out vegetation classification and hidden danger detection on the input image, inputting the collected real-time data into a comprehensive risk scoring model for hidden danger detection for calculation, and carrying out risk early warning positioning. The hidden danger trees are accurately positioned based on the GIS technology, the omission ratio is reduced through a dynamic early warning system, and the manual inspection risk is reduced through automatic reporting; different climate monitoring requirements are adapted based on the expansibility of the model, the tree barrier hidden danger can be blocked and the maintenance efficiency can be improved in practical application, and an integrated solution integrating accurate identification and real-time positioning is formed.

The invention discloses a remote sensing image change detection method, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting a preprocessed dual-time image of a target region into a remote sensing image change detection model, and outputting a change probability graph of the target region; the remote sensing image change detection model is obtained by training a global network model by adopting a training set, and the global network model comprises an initial network model and a classifier; the initial network model is constructed on the basis of a transform model; performing thresholding processing on the change probability graph of the target area to obtain a binary change detection graph of the target area; the initial network model comprises a pyramid segmentation attention feature enhancement module, a cross-spatio-temporal feature interaction module, a cross-scale attention transformer module and a cross-level pyramid transformer module. According to the invention, the accuracy and reliability of remote sensing image change detection are improved.

The invention discloses a construction scene dynamic obstacle avoidance method and system based on multi-source image fusion, and belongs to the technical field of computer vision and image processing, and the method comprises the steps: mapping multi-source image data to a symmetric positive definite matrix manifold space, carrying out the high-precision registration based on Riemannian geometric measurement, achieving the self-adaptive feature fusion through geometric flow optimization, and achieving the dynamic obstacle avoidance of a construction scene. According to the method, spatial topological features of obstacles are extracted through topological data analysis, and probability trajectory prediction is carried out through a variational inference method. Compared with the prior art, the method has the advantages that the obstacle avoidance success rate is increased by 35%-50%, the false alarm rate is reduced by 40%-60%, the similarity of the technical scheme is lower than 20%, and the method has the advantages that the method is suitable for large-scale popularization and application. And the accuracy, the reliability and the self-adaptive capability of dynamic obstacle avoidance in the construction scene are remarkably improved.

The invention belongs to the technical field of computer vision and image processing, and particularly relates to a remote sensing and unmanned aerial vehicle image defogging method, a defogging network DWTMA-Net is adopted, and the DWTMA-Net is constructed based on a U-shaped architecture and comprises a discrete wavelet block DWB, a multi-dimensional attention module MAB and a wavelet down-sampling module WDM; downsampling of the encoder part is carried out by using Haar discrete wavelet transform through a WDM expansion downsampling method, and frequency information of wavelet transform is combined with spatial information of convolution downsampling; the DWB and the MAB are sequentially arranged between the encoder part and the decoder part, the DWB decomposes features into four frequency components by using Haar discrete wavelet transform (DWT), the low-frequency features are processed by a small AOD network to extract the features, and the high-frequency features are refined by using an expansion residual block; and then spatial information is reconstructed by applying inverse wavelet transform. According to the method, feature representation is improved, and the defogging performance of the network is effectively enhanced.

The invention discloses an image generation method and device and electronic equipment, and belongs to the technical field of computer vision and image processing. The method comprises the steps of inputting feature information of a target building into a multi-modal big language model, and performing cross-modal reasoning based on the feature information through the multi-modal big language model to obtain key environment semantic information of the target building; constructing a space block model of the target building by utilizing the key environment semantic information; generating a target space condition control chart according to the space body block model and target observation configuration selected by a user; wherein the target space condition control chart is a visual angle screenshot, corresponding to the target observation configuration, of the space block model; inputting the key environment semantic information and the target space condition control chart into a condition diffusion model to obtain an initial view angle image, corresponding to the target observation configuration, of the target building; and determining a target view angle image of the target building according to the initial view angle image. According to the invention, the image generation effect can be improved.

The invention provides an object motion trail prediction method and device, and belongs to the technical field of computer vision and image processing, and the method comprises the steps: reconstructing an obtained multi-view time sequence two-dimensional image sequence of a target object, obtaining an original particle point cloud sequence, and generating a sparse key point cloud sequence after sampling; constructing a particle map sequence based on the spatial position of the key point at each moment in the sequence, and performing spatial semantic completion; performing object-level dynamic space-time aggregation on the complemented key point feature sequence to obtain aggregated key point features, inputting the aggregated key point features into a particle diagram converter, and outputting key point updating features after long-range force propagation modeling through the converter; and decoding and predicting key point displacement at a future moment based on the updated feature, and generating a future movement track of the target object according to the key point displacement. Based on the method, the invention further provides a device for predicting the object motion trail. According to the method, the fine appearance and the stable and accurate motion trail of the object can be predicted at the same time.

The invention discloses a ship bollard identification method and system based on adaptive multi-scale multi-grid division, and belongs to the technical field of computer vision and image processing. The system divides an image foreground region and a background region through a visual saliency calculation module, carries out dense sampling in the foreground region and sparse sampling in the background region by adopting a non-uniform grid generation module, screens candidate grids in combination with gradient direction consistency and texture features, fuses candidate frames of different scales through a multi-scale image pyramid, and finally obtains a multi-scale image. And accurate positioning of the bollard is realized through the fine grid accurate positioning module. The method solves the problem that the prior art is insufficient in adaptability to ship size, shooting distance and resolution change, improves the precision and generalization ability of bollard positioning in a complex scene, and is suitable for bollard detection scenes of various ship images.

The invention relates to the technical field of computer vision and image processing, in particular to a cross-day-and-night boundary target thermo-optic joint detection method, which comprises the following steps of: firstly, acquiring a visible light image and an infrared image and unifying the visible light image and the infrared image to an image plane reference system; extracting topological features and multi-scale energy features from the input, fusing the topological features and the multi-scale energy features to generate a feature map, and outputting a central heat map, target size regression and sub-pixel offset by adopting anchor-frame-free detection; implementing optimal transmission correction according to an evidence field obtained by normalization of a cross reconstruction residual field to obtain a correction heat map and an initial candidate; triggering a fixation area by using the shape correction heat map and the initial candidate, executing super-resolution and secondary detection in the area, and performing affine reprojection and primary detection fusion to form an updated heat map and a fusion candidate; and in combination with uncertainty and topological consistency, a final detection set is output by using a conditional random field and non-maximum suppression. The method is stable in low-contrast, small-target and strong-interference scenes.

The invention discloses a dynamic scene-oriented multi-scale spatial-temporal feature fusion video super-resolution method, and belongs to the technical field of computer vision and image processing. The method comprises the following steps of: inputting an original video image into a trained video super-resolution network based on spatio-temporal feature fusion to obtain an optimized video image, and performing multi-scale extraction and fusion on video features by the video super-resolution network based on spatio-temporal feature fusion to obtain an optimized video image; according to the method, the problems of ghost shadow and optical flow caused by rapid change of an object are solved by utilizing a context residual learning network, and efficient feature extraction and fusion are realized by fully utilizing intra-frame space information and an inter-frame time relationship, so that the visual perception quality and the accuracy and robustness of super-resolution reconstruction are improved.

The invention discloses an intrinsic image decomposition method based on interactive image semantic information constraint, and particularly relates to the field of computer vision and image processing. Comprising the steps of performing semantic segmentation and semantic feature extraction on an original image to obtain semantic segmentation features; performing feature extraction on the original image to obtain original image features; performing feature enhancement on the original image features to obtain enhanced original image features; obtaining reflection features by using common features of the original image features and the semantic segmentation features; obtaining illumination features by using difference features of the original image features and the semantic segmentation features; reconstructing the enhanced original image features and the reflection features to obtain a reflection image; and reconstructing the enhanced original image features and the illumination features to obtain an illumination image. Based on the method, the accuracy of reconstructing the illumination image and the reflection image can be further improved.

This invention provides a method for 3D reconstruction of panoramic images based on 3DGS, which relates to the fields of computer vision and image processing. The method comprises: Step 1: Acquiring a sequence of panoramic images of the scene to be reconstructed and uniformly encoding them via equirectangular projection to obtain a coded dataset; Step 2: Performing panoramic structure-from-motion processing on the coded dataset to generate six-degree-of-freedom camera pose parameters for each image and a corresponding sparse 3D point cloud. This method achieves high-precision and high-efficiency 3D reconstruction of panoramic scenes, capable of outputting high-quality panoramic images or dense 3D models, improving both the quality and efficiency of 3D reconstruction.

The invention discloses an infrared small target detection method and system based on a visual autoregression teacher model, and relates to the technical field of computer vision and image processing. The method mainly comprises the following steps: constructing and training a VAR teacher model, extracting multi-scale features by using a frozen pre-trained visual autoregression model, and adapting general features to an infrared small target detection task through a lightweight adapter module; using the trained teacher model to generate a high-quality pseudo label for the target domain unlabeled infrared image, and carrying out confidence threshold screening and non-maximum suppression; training a lightweight student detector by using a joint loss function based on a self-training framework in combination with the source domain labeling data and the target domain pseudo-tag; and updating the average teacher model through an index moving average mechanism for final reasoning and evaluation. According to the method, the problems of low precision, low robustness and weak generalization ability in cross-domain infrared small target detection are solved.

The invention belongs to the technical field of computer vision and image processing, and discloses a low-light image enhancement method and system based on space-frequency domain feature resolution self-adjustment. According to the enhancement method, a feature splitting and splicing process, a space-frequency domain feature resolution self-adjusting network architecture based on an encoder-decoder framework, and a complete processing process from low-light image input to normal illumination image output are provided. According to the method, a multi-scale feature map is further flattened into a two-dimensional matrix, image restoration is carried out from features of different scales through a linear layer and a feedforward neural network, and the integrity of a receptive field is further kept; besides, after the important spatial features are extracted, frequency restoration is carried out on the important area, the common problem that the dark area is excessively enhanced and details of the bright area are lost is effectively solved, and the color fidelity and the structural integrity in the low-illumination scene are better.

The invention relates to the field of computer vision and image processing, and discloses an image target recognition system based on a convolutional neural network and a feature fusion technology. Comprising a quality evaluation and alignment unit, a reversible decoupling and gating recharge unit, a multi-branch feature extraction unit, an evidence fusion unit, a marginal contribution gating unit, a topology consistency and boundary refinement unit, a detection and positioning unit and a linkage control unit. Reversible decoupling of contents and degradation components is realized in a feature domain, and bitwise gating recharge is implemented in a candidate region according to a quality map, so that small target and weak texture features are enhanced. The system fuses multi-branch output based on an evidence theory, determines weight distribution by combining marginal contribution calculation, triggers gating enhancement and local refinement when a conflict or deviation exceeds a threshold value, and realizes cooperative control of feature suppression and structure correction. According to the method, the recognition stability can be kept in complex scenes such as weak light, blurring and shielding, and the target recognition precision and the system interpretability are improved.

The invention discloses a method for automatically correcting radial distortion of a wide-angle lens, and relates to the technical field of computer vision and image processing, and the method comprises the steps: collecting an original image, calculating the distance from a pixel to an imaging principal point, and extracting a multi-scale image and edge features; analyzing the edge direction and radial change and linear extension of the structure, extracting a linear structure candidate region, obtaining a distortion compensation parameter, generating a multi-scale candidate linear parameter set, and calculating a multi-scale linear consistency index for adaptive adjustment; extracting a point set from the candidate straight line parameter set, executing forward and reverse mapping to calculate a dual-space consistency index, and optimizing local parameters and the point set; calculating a minimum linear complexity index based on the line segment curvature, the curvature change rate and the length, and implementing local optimization; and performing weighted fusion on each index to construct a comprehensive objective function, performing step-by-step shrinkage optimization to obtain an optimal compensation parameter, performing global geometric correction on an original image, generating a distortion correction image, and realizing improvement of the structure recognition precision and the geometric correction effect.

The invention relates to the technical field of computer vision and image processing, and particularly discloses an unmanned aerial vehicle identification and detection method under a target part feature missing condition. The method comprises the following steps: (1) making an initial data set by adopting aerial pictures of an unmanned aerial vehicle; (2) performing labeling and data enhancement processing on the initial data set to obtain a training set and a verification set; (3) a YOLOv8 target detection model is improved, a standard convolution module (Conv) of a backbone network (Backbone) is replaced by dynamic deformable convolution (DEConv), a SimAM attention mechanism is introduced behind a last C2f module of the backbone network and in front of a spatial pyramid pooling layer (SPPF), and in a neck network (Neck), the C2f module is replaced by a C2f-SimAM module, and the standard convolution module is replaced by the dynamic deformable convolution; an additional branch for capturing key features is added in an output branch of a detection head (Head). According to the method, the recognition and detection capability of a target with partial feature missing can be remarkably improved.

The invention relates to the technical field of computer vision and image processing, in particular to a forest wetland environment transition zone identification method and system for an unmanned aerial vehicle. The method comprises the steps of obtaining various prior data layers of a detection area and fluctuation scales corresponding to the prior data layers, taking the fluctuation scales as weights, obtaining similarity degrees between area boundaries of different prior data layers, and identifying similar boundary groups based on the similarity degrees; obtaining the environment fusion degree of the similar boundary groups; and based on the environment fusion degree, identifying an environment transition zone and an unmanned aerial vehicle flight focus region. By fusing prior data layers of DEM, hydrology, vegetation and the like, analyzing boundary similarity according to a fluctuation scale and identifying an environment fusion area, an environment transition zone and an unmanned aerial vehicle key monitoring area are further determined, targeted monitoring and identification of a key interface of a complex ecological crisscross zone are realized, the precision and efficiency of complex environment monitoring are effectively improved, and the method is suitable for popularization and application. And meanwhile, terrain and water area flight risks are avoided.

The invention belongs to the technical field of computer vision and image processing, and discloses a cross-modal target tracking method based on a collaborative strategy and a related device. The cross-modal target tracking method comprises the following steps: acquiring a template image and a current frame search image; based on the obtained template image and the current frame search image, performing cross-modal target tracking by using the trained cross-modal target tracking model to obtain a cross-modal target tracking result; the cross-modal target tracking model comprises four branch networks and a multi-modal region suggestion network; each of the four branch networks comprises a feature extraction module, a multi-scale channel adaptive enhancement module and a hierarchical progressive attention fusion module. According to the technical scheme, challenges such as scale change, deformation and texture blurring can be effectively handled, and the tracking stability and robustness under the conditions of shielding, modal interference and low illumination are improved.

The invention belongs to the field of computer vision and image processing, and particularly relates to an unsupervised test case generation method based on content-style constraint. The method comprises the following steps: acquiring a source domain image and a target domain image, and constructing a training set based on the source domain image and the target domain image; constructing an image style conversion model, training the image style conversion model through the training set, obtaining the trained image style conversion model, inputting the target scene image into the trained image style conversion model, and outputting a corresponding style conversion image; according to the method, the problem that existing paired image samples are difficult to obtain is solved, the image conforming to the target domain style is generated, and the image sample expansion quality is improved.

The invention relates to the technical field of computer vision and image processing, in particular to a foreground detection method fusing wavelet transform and a prompt mechanism, which is characterized in that in a dynamic background, high-speed motion and small target scene, F-measure is averagely improved by more than or equal to 0.30, and the detection precision is obviously superior to that of FFNet and ASCNet. The wavelet transform attention module suppresses background noise, highlights target edges and textures, and improves input feature purity. The CFF, MFR and PB of the decoder collaboratively fuse multi-scale features, redundant information is reduced, and target details and contour integrity are reserved. According to integral end-to-end training, single-frame reasoning only needs 28 ms, and real-time performance and accuracy are both considered. Therefore, the problems that in an existing neural network foreground target detection algorithm, input data are often influenced by noise and background interference, redundant information is easily introduced into a decoder in the multi-level feature fusion and transmission process, and consequently the detection result is inaccurate are solved.