1313 results about "Single image" patented technology

A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

A data processing system comprising: inputting a tiled image of a vehicle including four different angle views of the vehicle combined into a single image to a first machine learning model (e.g. CNN), the model trained based on historical image data to predict a first likelihood of total loss vehicle; inputting a multi-fusion of images each into a second set of machine learning models; the multi-fusion of images including a set of separate and distinct images for each of the views input separately into the second set of machine learning models, and extracting features to predict a second likelihood of total loss vehicle; inputting tabular data relating to the vehicle into a third machine learning model to predict a third likelihood of total loss vehicle for the vehicle; and aggregating the first, second and third likelihood of total loss vehicle to determine the overall likelihood of total loss.

The invention discloses a three-dimensional human body reconstruction method based on an implicit neural network and a diffusion model, and belongs to the field of three-dimensional reconstruction. According to the invention, a diffusible density volume representation is constructed, and identity-based rendering is realized by using a diffusion model. By adopting parameterized three-dimensional distribution representation, Pareto optimum, balanced realistic rendering and feasible prediction are realized in an unbalanced optimization framework. In order to increase the rendering training speed without sacrificing the quality, mixed multi-resolution hash grid features are adopted to enhance the directional feature representation capability. Meanwhile, a multi-scale structural similarity constraint (MSSC) is provided to maintain identity distinguishability and ensure geometric consistency, so that synthesis with high calculation efficiency and satisfactory visual effect is realized. According to the method, the three-dimensional human body model can be reconstructed from a single image based on limited geometric and appearance information, the method is excellent in topology credibility and visual fidelity, and a more flexible and efficient solution is provided for an actual application scene.

Systems and methods for generating a 3D model from a single input image are described. Embodiments are configured to obtain an input image and camera view information corresponding to the input image; encode the input image to obtain 2D features comprising a plurality of 2D tokens corresponding to patches of the input image; decode the 2D features based on the camera view information to obtain 3D features comprising a plurality of 3D tokens corresponding to regions of a 3D representation; and generate a 3D model of the input image based on the 3D features.

The invention discloses a pig single-view three-dimensional reconstruction method based on deep learning, and the method is called Magfup. In order to solve the problems that single-view three-dimensional reconstruction viewpoint learning is unstable and training is difficult due to lack of real three-dimensional data, an implicit-explicit combined three-dimensional representation method is adopted, high-dimensional data features subjected to super-resolution optimization are combined, a high-density pig three-dimensional grid can be reconstructed only through a single image, and the problem that training is difficult is solved. And data acquisition cost and equipment requirements are reduced. According to the method, the three-dimensional grid surface is optimized through the Laplacian smoothing method, and the reconstruction quality is improved. According to the invention, the model can learn the general shape and posture characteristics of the pig, predicts the three-dimensional posture, shape and texture information of the specific pig, and more accurately and comprehensively reflects the behavior characteristics of the pig. Therefore, the method can adapt to complex and changeable environments and pig behaviors in a pig farm, and a more comprehensive and accurate data basis is provided for pig behavior analysis.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

The invention provides a surface roughness measuring method and device based on a multispectral laser speckle image, and belongs to the field of surface quality visual inspection. Comprising the following steps: respectively acquiring speckle images formed by irradiating the surface of a workpiece with lasers of N wavelengths at different angles, and constructing a multispectral laser speckle data set; the speckle images of N wavelengths at a single angle are spliced according to channels and subjected to feature fusion, and then a multispectral laser speckle fusion image is generated; the fused images of the multiple angles form an image sequence, and single images in the sequence are subjected to multi-scale feature extraction and fusion to generate single-angle features; the multi-angle features generate global illumination information through a multi-angle feature aggregation module, and finally a roughness value is calculated through a multi-layer perceptron. Multi-spectral laser speckle coding surface features are adopted, multi-angle information is fused through the multi-angle feature aggregation neural network, and the relation between the light source wavelength, the incident angle and the surface three-dimensional shape and roughness is comprehensively considered, so that high-precision measurement is achieved.

The invention discloses an offshore wind power blade defect detection method, and belongs to the technical field of wind power blade detection. The detection method comprises the following steps: S01, offshore wind power blade defect detection: identifying the position of a blade defect, outputting a positioning mark frame of the defect on an original image, and providing the coordinate of the center position of the mark frame and the corresponding length and width; s02, leaf image splicing: carrying out step-by-step shooting; splicing each detected image by means of an image splicing technology to form a complete fan blade image; and S03, determining the actual physical position of the blade defect in the single image: in the spliced total image, determining the actual physical coordinate of the blade defect in the original single image corresponding to the fan blade. According to the detection method, multiple detection means such as computer vision, feature extraction, image splicing and edge detection technologies are fused, blade surface defect information can be comprehensively obtained, and the detection precision and accuracy are greatly improved.

The invention relates to the technical field of building structure health monitoring, in particular to a building defect detection method based on infrared thermal imaging and visible light bimodal fusion and a matched intelligent imaging device. According to the method, infrared thermal imaging and visible light bimodal image fusion are combined, and an improved YOLOv8m-seg model is combined, so that accurate detection of the defects of the outer wall of the building is realized. Firstly, a bimodal data set containing multi-material temperature difference data is constructed, ORB feature matching is combined with a feature alignment module (FAM) to achieve accurate image alignment, the defect detection robustness is improved through an improved bimodal feature fusion network, and finally automatic recognition and quantitative analysis of cracks, fractures and other defects are achieved. The problems that single-mode detection is interfered by dirt and is poor in multi-material adaptability are solved, the detection accuracy rate reaches 96% or above, the recall rate exceeds 93%, the single image detection time is shorter than or equal to 0.3 s, and the method is suitable for efficient detection of various building outer wall defects.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

There is provided an information processing device and method that can mitigate a reduction in the quality of 3D data due to encoding and decoding. A bitstream is decoded, the bitstream having been obtained by dividing a mesh that represents an object with a three-dimensional structure into at least a first patch and a second patch, placing the first patch and the second patch within a single image, and encoding the single image. A boundary correction process is executed to correct a geometry for a boundary vertex located at a boundary of the patches decoded. The boundary vertex subjected to the boundary correction process is set as a reference point, and a smoothing process is executed to smooth a geometry for a vertex within a partial area based on the reference point. The present disclosure may be applied to, for example, an information processing device, an electronic device, an information processing method, a program, or the like.

The invention relates to the technical field of chemical production process monitoring, and provides a chemical storage tank liquid level real-time monitoring method and system based on image recognition, which can automatically generate an accurate illumination threshold value by applying big data analysis and machine learning technologies, can adapt to illumination changes of different chemical scenes, and can realize real-time monitoring of the liquid level of a chemical storage tank. The accuracy of liquid level monitoring under various illumination conditions is greatly improved; for image enhancement and correction, the system adopts a local region adaptive adjustment histogram equalization and optimization CLAHE technology and constructs a feature database assisted threshold segmentation and environmental parameter dynamic adjustment SFS model, so that dark environment images can be enhanced more excellently, strong light reflection can be removed, and reduction liquid level details can be cleared; according to the system, a dynamic weight updating mechanism is created for the first time, the weight is adjusted in real time according to the liquid level change and the image quality, interference of a single image is effectively avoided, and the monitoring reliability is remarkably improved.

The invention discloses a method and a device for detecting and tracking an unmanned aerial vehicle under a complex background, which can be applied to the technical field of unmanned aerial vehicle countering. According to the invention, the performance of the unmanned aerial vehicle in the image is analyzed by adopting an image processing technology, so that the detection precision is effectively improved; meanwhile, unmanned aerial vehicle target detection is carried out through an image sensor, and target category and position information of the unmanned aerial vehicle are further confirmed; millimeter-wave radar data are fused into state estimation, estimation of a tracker on a target position is corrected based on the target position and speed information acquired by the radar so as to further enhance prediction precision of the trajectory of the unmanned aerial vehicle, and uncertainty of single image detection data during shielding or view angle change can be reduced by introduction of radar data. According to the invention, an image recognition technology and a radar detection technology are used, and existence and dynamic change of the unmanned aerial vehicle can be sensed more comprehensively and accurately. The method is excellent in performance in a complex environment, the false alarm rate is effectively reduced, and the detection tracking precision is improved.

The invention discloses a surface defect detection method and system based on multi-light source fusion and a related product. The method comprises the following steps: collecting multiple groups of images of a sample to be detected under different light source conditions; marking image defects to obtain a marked data set, and combining the same group of multi-light-source image masks; establishing a multi-light-source fusion segmentation model with multi-image input, multi-feature fusion and single-image output; training the fusion segmentation model based on a multi-light-source data set, and optimizing a training process; the trained model is converted into an acceleration model for production line deployment, and nondestructive, real-time and automatic detection of product surface defects is achieved. According to the method, key information of various defects can be comprehensively captured through the changing light source, and the problems that a traditional industrial quality inspection method is low in efficiency and insufficient in precision are solved.

An image enhancement and object detection system is provided to enhance the degraded images and bring out the inherent details of the image received via an underwater surveillance system. The said system includes a novel deep learning architecture i.e., a Zero-DCE-U that enhances the degraded underwater images using modified UIQM. Further, a new training method using a single image based on correlation is employed. Furthermore, a G-UNet architecture is configured to detect moving objects while preserving the spatial-contextual relationships.

The invention discloses a multi-dimensional feature and three-plane representation enhanced three-dimensional grid reconstruction method and device, and belongs to the technical field of three-dimensional reconstruction, and the method comprises the steps: obtaining three-dimensional priori knowledge: after a multi-view image sequence is generated based on a single image, extracting the multi-dimensional features of the image sequence through a multi-dimensional feature extraction and fusion module, and obtaining the three-dimensional priori knowledge; performing feature fusion on the multi-dimensional features and the initial three-plane features by using a high-resolution three-plane representation construction module containing a double-flow Transform, a convolutional layer and a pixel rearrangement operation so as to organize the initial three-plane features into high-resolution three-plane representation; a three-dimensional grid construction module is used for generating a three-dimensional grid based on the three-plane representation, a color decoder is used for decoding and generating color textures of space points based on the three-plane representation, and the three-dimensional grid and the color textures are combined to obtain a high-precision three-dimensional grid with textures. The model effectively reduces the fuzzy and artifact phenomena of the grid surface and improves the accuracy of the geometric structure. The method can be widely applied to modeling processes of game and movie and television industries.

An imaging system includes a light source, an imaging apparatus that captures an image of a subject illuminated by light from the light source to generate image data, which includes image information regarding each of four or more bands or information regarding a compressed image in which the image information regarding the four or more bands is compressed as a single image, and a processing apparatus. The processing apparatus determines whether or not pixel values of pixels in the image data satisfy a predetermined condition, and causes, in a case where the predetermined condition is not satisfied, a lighting condition caused by the light source to be changed under a condition where a spectral shape of light from the light source does not change at the subject's location.

Embodiments of the present application provide a three-dimensional model reconstruction and image generation method, a device, a storage medium, and a program product. In the method, multi-stage three-dimensional reconstruction is performed on the basis of a single image of a target object; in a first stage, a plurality of view images are generated on the basis of an image generation model, and an initial three-dimensional model is reconstructed on the basis of the plurality of view images; and in a second stage, on the basis of the plurality of view images and an initial prompt containing set marker information, a text-to-image model is used to learn an association relationship between the target object and the set marker information, and a plurality of scene images are generated on this basis. Compared with the plurality of view images, the scene images generated in the second stage have higher resolution and richer image details, and then the initial three-dimensional model is optimized on the basis of the plurality of scene images, so that a target three-dimensional model having higher resolution and clearer model details can be obtained, thereby paving the way for practical application of a three-dimensional reconstruction solution based on a single image.

The invention provides a blind image super-resolution reconstruction method and system, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining a damaged low-resolution image; processing the obtained damaged low-resolution image by using a pre-trained diffusion super-resolution model to obtain a super-resolution image; wherein the diffusion super-resolution model constructs a blind super-resolution problem under a maximum posterior probability framework, and is expanded along with the inverse process of the de-noising probability diffusion model. According to the method, the blind degradation setting problem in single image super-resolution is solved, optimization based on the maximum posterior probability is integrated into DM, joint distribution of low-resolution observation, high-resolution data and a degradation kernel is constructed to provide prior information of the data and the kernel, and the blind SR problem is solved by expanding the maximum posterior probability method and a reverse process.

The invention discloses a single image super-resolution reconstruction method and system based on wavelet transform and cross-domain feature fusion. According to the method, firstly, a low-resolution RGB image is mapped to a high-dimensional feature space through a shallow feature extraction module; performing up-sampling and discrete wavelet decomposition on the features by using a wavelet feature mixing module to obtain multi-band features; low-frequency and high-frequency depth features are respectively extracted through a double-branch structure, cross-domain fusion is realized by means of a deformable cross attention mechanism, and the feature expression ability is enhanced in combination with residual connection; and finally, reconstructing a high-resolution image through convolution, up-sampling and regularization processing. In the training process, a pixel-level loss function is adopted to optimize network parameters, the multi-frequency-domain feature sensitivity is effectively improved, texture and structure information is balanced, the image contrast, definition and structural integrity are improved, and high-quality real-time super-resolution reconstruction can be achieved.

The invention belongs to the technical field of Internet of Things information security, and particularly relates to a radio frequency fingerprint identification method based on multi-image feature fusion. According to the method, multiple images are extracted from original IQ data, the limitation of single image features is made up, different basic networks are selected according to different types of image data, the basic networks are optimized, SA-D-ResNet18 and SA-ConvNeXt-T networks are provided, a cross attention mechanism module is introduced between the networks, and image feature information is exchanged. In the final stage of feature extraction, a Concat mode is used to splice and fuse the features of a plurality of images. Therefore, the method can still maintain high recognition accuracy in a more complex environment. According to the invention, a common cross attention mechanism in multiple modes is applied to radio frequency image feature fusion, an image-based classification technology is also applied to the field of radio frequency fingerprint identification, and the radio frequency fingerprint identification method based on multi-image feature fusion is researched.

The invention discloses a bird abnormal behavior recognition method based on radar monitoring data, belongs to the technical field of target detection, and solves the problem that target recognition precision is affected due to incomplete target single image data information acquired by a data acquisition module of an existing system. A bird sensing model performs correlation analysis on the normalized data set, whether a target object is a bird target object is judged, a target object path track is generated based on a target object recognition result and a path optimization algorithm, and the path track of the target object is analyzed and calculated in combination with a weak supervision bird detection algorithm to generate a path anomaly degree; the bird sensing model can analyze and recognize the bird flight path based on the target object recognition result, and judges and recognizes the bird abnormal behavior in combination with the weak supervision bird detection algorithm, so that the recognition precision of the target object is ensured, and the early warning capability of the bird abnormal behavior is improved.

The invention discloses a PCB intelligent online defect detection device based on multi-modal sensor fusion and edge calculation, the PCB intelligent online defect detection device comprises a multi-modal module and an edge calculation module, the multi-modal module is used for collecting data of a PCB, and the edge calculation module is used for processing and analyzing the data. The multi-modal sensor array is fused with multiple detection modes such as vision, 3D laser and ultrasonic, data are comprehensively collected from surface details to an internal structure, a high-precision sensor and an advanced algorithm are matched, tiny defects such as 0.05 mm-level microcracks and various defects such as short circuits, open circuits and cavities can be accurately detected, the detection accuracy is high, and the detection precision is high. Through an edge calculation module and a hardware acceleration and optimization algorithm, rapid image preprocessing, traditional image processing acceleration and deep learning efficient operation are realized, the processing time of a single image is greatly shortened, the real-time detection requirement of a high-speed production line is met, and the production efficiency is improved.

The invention discloses a visual point array laser hair growth treatment hand tool, which comprises a hand tool main body, an optical waveguide array treatment head, an illumination module, an image capture module and an image processing module, and is characterized in that the hand tool main body is internally provided with an image identification module, the image processing module, an image display module, an early warning module and a main control board; the image processing module identifies scalp surface features under the treatment head position; the image processing module performs single-point segmentation on the scalp feature picture under the treatment position of the treatment head; the segmented single picture is compared with the feature image in the storage module, image feature information is recognized, features are judged according to target information, and whether treatment is needed or not is marked in the treatment selection display module. According to the treatment hand tool, the specific condition of a treatment area is monitored in real time in the use process; equipment operators are reminded to avoid inapplicable treatment working points; and for the working point which is misjudged by the operator, the equipment gives an early warning to remind the operator to re-assess the treatment area.

A vehicle comprises a sensor configured to capture images and one or more processors. The one or more processors can be configured to receive a single image from the sensor, the single image captured by the sensor as the autonomous vehicle was moving; execute a machine learning model using the single image as input to generate a change in pose of the autonomous vehicle, the machine learning model trained to output changes in pose of autonomous vehicles based on blurring in individual images; determine a global position of the autonomous vehicle based on the generated change in pose of the autonomous vehicle; and transmit the global position to an autonomous vehicle controller configured to control the autonomous vehicle.

The invention discloses a road obstacle recognition method based on machine vision, and the method comprises the steps: carrying out the precise positioning of the spatial position of an obstacle through a multi-sensor information fusion algorithm according to the azimuth and distance information of the obstacle estimated through a sound source positioning algorithm and the position information outputted by an obstacle detection model, and improving the positioning precision; the method comprises the following steps: acquiring guide mark information, such as road signs and lane lines, extracting guide mark features through a semantic segmentation algorithm, carrying out dynamic fusion on the guide mark features and an obstacle recognition result, and improving the accuracy and robustness of obstacle recognition by using prior information; in order to solve the problem of insufficient parallax analysis precision in long-distance, high-speed motion and other scenes, a monocular depth estimation algorithm based on deep learning is introduced, pixel-level depth information is predicted from a single image through a convolutional neural network, and the precision of obstacle distance estimation is improved.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for encoding input data comprising input data values corresponding to respective input data grid points of an input data grid, such as image, video or audio data.

The invention relates to a single-image dressed human body reconstruction method based on posture guide diffusion and view angle consistency constraint, and belongs to the field of computer vision and graphic images. The method comprises the following steps: firstly, carrying out multi-view image generation on a single image in an input dressed human body data set by adopting posture guide diffusion to obtain a two-dimensional diffusion image of an invisible view angle; secondly, establishing a visual angle consistency constraint module, taking the input image and the diffusion image as bidirectional visual angle features, performing implicit geometric guidance in the normal direction of the SMPLX human body template, and respectively extracting three-dimensional space features corresponding to cross visual angles; and finally, fusing voxelization features and three-dimensional space features in the SMPLX human body template to carry out human body implicit reconstruction to obtain a dressed human body model. According to the method, the problems of low reconstruction quality, clothing detail missing and the like caused by insufficient input information of a single image can be effectively solved, and high-quality three-dimensional reconstruction of a dressed human body is realized.

The invention discloses an industrial defect detection method based on reverse distillation learning, which relates to the technical field of industrial defect detection, and comprises the steps of generating and filtering a noise picture, and fusing reverse distillation learning with an attention mechanism. According to the method, the fine defect detection and positioning capability under the complex background is improved, and particularly, the defects of small defect area and low background contrast ratio under the condition of complex background texture or relatively high noise level are overcome; according to the performance, the image level AUROC is 99.0%, the pixel level AUROC is 98.23%, the AUPRO can reach 95.02%, and the AUROC and the AUPRO are far higher than other indexes based on distillation learning. In the aspect of real-time performance, the detection speed of a single picture is high and can reach 25ms, the real-time performance is in the front, and the defect detection performance and the defect positioning performance are higher; noise synthesis and filtering are used for improving the small defect detection rate and reducing the false detection rate; the MFF + attention mechanism is used for improving the logic defect detection rate.

The present invention provides an article visual inspection method and a rationale-generative estimation method using a large vision–language model.　The article visual inspection method comprises: a pretraining step of performing context-sensitive pretraining by inputting information containing images and sentences into a large vision–language model; an additional training step of performing additional training by inputting training information on a good-quality group and training information on a poor-quality group of a plurality of articles into the large vision–language model; and an inspection step of inputting a visual appearance image for inspection into the large vision–language model and causing the large vision–language model to output an inspection result. The article visual inspection method is characterized in that the training information on a good-quality group includes visual appearance images of good-quality articles and text information about good-quality articles, the training information on a poor-quality group includes visual appearance images of poor-quality articles and text information about poor-quality articles, and the information to be inspected includes an image to be inspected and text information requesting an inspection result. The estimation method comprises: a pretraining step of performing pretraining by inputting a plurality of datasets into a large vision–language model, each dataset containing a single image paired with a sentence containing a specified subject's preference information regarding the image; and a result outputting step of inputting an image of an object to be estimated into the large vision–language model and causing the large vision–language model to output an estimation result regarding individual preference and a rationale. The specified subject's preference information includes a judgment of whether or not the subject prefers the single image and text information indicating a rationale for the judgment.