1001 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.

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.

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.

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.

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 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.

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.

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 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.

The invention provides a steel rail seam width detection method and system based on line-line distance, and the method comprises the steps: shooting an image of a rail connection part, obtaining a near-end rail and a far-end rail through an image recognition strategy, defining the boundary points of the top edge of the near-end rail as A and B, and defining the boundary points of the bottom edge of the near-end rail as C and D, the B and the C are located on the same track connecting line, the far-end track is defined in the same mode, and the A point and the B point are obtained; according to the C point determination strategy, determining the C point based on multi-factor weighting, and obtaining a near-end key point set; acquiring a far-end key point set according to the image acquisition processing step; two sets of key lines of the near-end track and the far-end track are obtained respectively, and the distance between the two sets of key lines is calculated to serve as the width of the steel rail seam; through multi-level constraints of image gradient features and track theoretical width, the problem that a single image feature is susceptible to noise interference is solved, and the positioning precision of A and B points is improved.

The invention discloses a multi-source digital rock image fusion method based on ensemble learning, and relates to the technical field of oil and gas resource geological exploration and development evaluation, and the method comprises the steps: S1, scanning and preprocessing a multi-source rock image; s2, generator optimization of the cyclic consistent generative adversarial network; s3, optimizing three types of weak discriminators; and S4, constructing a strong discriminator based on ensemble learning and reconstructing the multi-source digital rock. According to the method, multi-source digital rock image fusion is realized, the advantages of a large-scale image and a high-resolution image are effectively fused, the problem that a multi-scale three-dimensional pore structure cannot be reconstructed by using a single image scanning mode is solved, and compared with a traditional cyclic consistent generative adversarial network method, the method has the advantages that the method is simple and convenient to implement. The relative error of the constructed digital rock physical parameters is remarkably reduced, the accuracy of rock physical numerical simulation is improved, the method is easy to apply and popularize in different oil and gas fields, and a foundation is laid for fine evaluation of the oil and gas reservoir rock physical parameters.

A method may include receiving, using an application server, formatting information that is associated with sensitive information, from a computing device. The method may include generating, using the application server and based on the formatting information, a video including a single image frame. The single image frame may represent the sensitive information, and the video may be associated with a digital rights management technology. The method may further include transmitting, using the application server, the video to the computing device.

The present invention discloses a method and system for detecting foreign objects in food based on deep learning, including an image acquisition module and a foreign object analysis module connected thereto; the image acquisition module: for acquiring RGB images and penetration images of food; the foreign object analysis module: inputting the food images corresponding to the RGB images, the food images corresponding to the penetration images, color features and shape features into a foreign object detection model, relating to the technical field of food detection, and solving the technical problem that when using X-ray images of food and analyzing the foreign objects therein, the sensitivity for detecting low-density foreign objects in food X-ray images is too low, resulting in difficulty in meeting the current high-quality requirements for food; taking the color situation in food as one of the judgment bases and taking the shape features of food as one of the judgment bases, and judging through the trained foreign object detection model can avoid a large missed detection rate when detecting foreign objects in food by a single image.

The embodiment of the invention discloses a prohibited article risk detection method based on an association relationship. The method comprises the following steps: acquiring a plurality of images of to-be-detected luggage or parcel; target detection is carried out on each image, the category and position of each entity in each image are obtained, and the entity comprises an article and an article assembly; according to the detection result of the single image, constructing a real example sub-graph of each image; according to the similarity between each real example sub-graph and a pre-constructed prohibited article knowledge graph, identifying a potential combined prohibited article risk; according to the detection results of the plurality of images, constructing a user sub-graph of each recipient; and according to the similarity between each user sub-graph and each node pair in the knowledge graph, identifying a potential continuous forbidden article risk. According to the embodiment, the combination risk of forbidden articles can be identified.

The invention relates to the technical field of intelligent inspection of power equipment, and particularly discloses a power transmission line inspection system based on an autonomous correction long-endurance unmanned airship. The system aims to solve the problems of short endurance, small operation radius, single image angle, intermittent operation, leak detection, low precision, poor environmental adaptability and the like caused by dependence on manpower or GPS navigation in the conventional unmanned aerial vehicle inspection. The system comprises an energy supply module, a data acquisition module, an intelligent control module and a communication and data return module. Wherein the energy supply module is integrated with a solar energy collection, storage and intelligent scheduling unit to realize day-and-night continuous energy supply; the intelligent control module adopts an RT-DETR visual algorithm to identify a tower in real time, performs automatic deviation correction by fusing pose information, and accurately tracks a preset track; the data acquisition module supports multi-angle high-definition imaging, and the communication module guarantees real-time data return. And high-precision unmanned inspection with ultra-long endurance, full-automatic and wide-area continuous coverage is integrally realized.

The invention relates to the technical field of image processing, and discloses a fluorescent lifetime microscopic image large-view-field splicing method and system and electronic equipment, and the method comprises the steps: constructing a tissue region mask for a to-be-spliced image, and constructing a vignetting model in a tissue region to complete vignetting correction; counting brightness indexes in an organization area to determine a global brightness reference, and realizing image group brightness unification through global zooming and single image brightness adaptive correction; further realizing alignment of adjacent images through feature point matching, and finishing image block splicing in combination with a minimum color difference suture fusion method to obtain an image band; an overlapping area is extracted from an image belt to generate an effective content mask, relative displacement is estimated by using a phase correlation method after low-frequency suppression, image belt splicing is completed by multiplexing a minimum color difference suture fusion method, a large-view-field spliced image is output, the automation degree and robustness of splicing are improved, the spliced image is geometrically consistent and visually seamless, and the splicing efficiency is improved. And the requirements of medical research on high-resolution and large-field-of-view fluorescence lifetime microscopic images are met.

The invention discloses a target detection method and system based on dynamic memory enhancement, relates to the technical field of computer vision and target detection, and aims to solve the problems that a traditional method depends on local features of a single image, is difficult to detect a target, is low in precision and is weak in generalization ability. The method comprises the steps that after a to-be-detected image is preprocessed, multi-stage multi-scale features are extracted through a backbone network; an encoder outputs single image global features for feature interaction fusion, and a global distribution extraction module constructs and updates a data set level global feature distribution library; the decoder adopts a hierarchical structure, and optimizes the target query token layer by layer in combination with self-attention, cross attention and global distribution fusion sub-modules; and finally outputting a target category and bounding box coordinates. Through global context injection, small target and shielding target detection precision is improved, model training stability and generalization ability are enhanced, and the method is suitable for diversified detection scenes.

The invention discloses a food foreign matter detection method and system based on deep learning. The system comprises an image acquisition module and a foreign matter analysis module connected with the image acquisition module. The image acquisition module is used for acquiring an RGB image and a penetration image of food; and the foreign matter analysis module is used for inputting a food image corresponding to the RGB image, a food image corresponding to the penetration image, color features and shape features into a foreign matter detection model. As the sensitivity of the food X-ray image for detecting low-density foreign matters is too low, the current high-quality requirement on the food is difficult to identify and conform. According to the method, the color condition in the food is used as one of judgment bases, the shape feature of the food is used as one of judgment bases, and the situation that when the foreign matter in the food is detected through a single image, the large omission ratio occurs can be avoided by judging the food through the trained foreign matter detection model.

The invention discloses a sports equipment management personnel identification method, system and equipment, and the method comprises the steps: starting and initializing a camera through detecting a sports equipment use trigger signal, and stopping automatic focusing; and shooting multiple frames of images at different focal lengths according to a preset interval, determining a clear area by using a gradient magnitude algorithm, and performing image registration and fusion to form a composite image. And if the whole area is not covered, shooting and splicing are carried out again. And through composite image splicing, distortion cutting, noise reduction and normalization processing, a final image is output for determining the identity of a person. According to the invention, through multi-frame shooting and accurate image processing, a high-quality image is obtained, and information limitation and quality defects of a single image are effectively avoided. Various algorithms are fused to ensure that the image is clear, complete and standardized, the accuracy and reliability of personnel identity recognition are remarkably improved, an accurate image basis is provided for sports equipment management, the normalization and safety of equipment use are guaranteed, meanwhile, the image collecting and processing efficiency is improved, and the management cost is reduced.

The present invention proposes a single-image de-raining method and system based on a multi-stage network, which obtains an image to be de-rained; constructs a first image de-raining network and a second image de-raining network based on a channel attention mechanism; constructs a third image de-raining network based on a channel attention mechanism and a spatial attention mechanism; inputs the image to be de-rained into the first image de-raining network, and the first image de-raining network outputs a first de-rained feature map; inputs the image to be de-rained and the first de-rained feature map into the second image de-raining network, and the second image de-raining network outputs a second de-rained feature map; inputs the image to be de-rained and the second de-rained feature map into the third image de-raining network, and the third image de-raining network outputs a de-rained image. The present invention improves the accuracy of de-raining while enhancing the generalization ability of the network and the ability to protect and restore the background details of the image.

The invention relates to the technical field of medicine image recognition, and discloses a medicine image recognition method based on artificial intelligence, which comprises the following steps: simultaneously projecting grid patterns through structured light sources in three orthogonal directions, and forming three orthogonal projection shadows of a medicine on a single image sensor; reconstructing a visual shell of the medicine from the three orthogonal projection shadows by using a projection contour inversion algorithm; the reconstructed visual shell is converted into simplified point cloud representation, and key geometric and texture features are reserved through point cloud sampling; performing feature extraction on the simplified point cloud by applying a compressed visual converter to generate feature vectors of the medicine; the knowledge distillation technology is combined, and through matching with a pre-established medicine three-dimensional feature library, rapid and accurate identification of the medicine is realized; according to the method, high-precision, high-speed and low-resource-consumption medicine identification is realized, and the method is suitable for high-speed production line and edge equipment deployment.