766 results about "Spatial image" patented technology

The invention discloses an intelligent fault diagnosis method fusing state monitoring and a multi-modal large model, and the method specifically comprises the steps: synchronously collecting time sequence data and a space image through a heterogeneous sensor group and monitoring equipment disposed in power grid equipment, and forming original data; based on the original data, a physical constraint feature vector is generated in combination with an equipment thermodynamic equation and a material deformation rule; performing health index prediction through the lightweight LSTM network based on the physical constraint feature vector; when detecting that the health indexes continuously decrease, clustering an HI time sequence curve by adopting a Gaussian mixture model, judging a degradation stage according to a clustering center distance, and obtaining a stage recognition result; and based on finite element simulation parameters, introducing a reinforcement learning model, optimizing the simulation parameters by taking maintenance cost minimization as a target, and outputting a predictive maintenance work order. According to the invention, intelligent fault diagnosis and accurate maintenance of the power grid equipment are realized, the fault processing efficiency and accuracy are improved, and the power failure loss is reduced.

The invention relates to the technical field of environmental data monitoring, and discloses a crop disease and insect pest monitoring and early warning system based on satellite remote sensing and an AI algorithm, and the system comprises the following steps: regularly collecting a farmland multispectral image covering a farmland area through a satellite remote sensing technology, and carrying out the preprocessing of the farmland multispectral image, and obtaining a farmland three-dimensional space image; performing pest and disease analysis on the farmland three-dimensional space image to extract a pest and disease image and spatial distribution thereof, analyzing historical pest and disease data based on an AI algorithm analysis model, obtaining pest-inducing influence factors corresponding to different target types of pests, and obtaining a pest and disease development trend corresponding to the target types of pests based on the pest-inducing influence factors; and compiling an intelligent contract for the disease and pest influence degree corresponding to the current disease and pest image, recording the timestamp of the disease and pest influence degree in the intelligent contract, and establishing a disease and pest early warning strategy based on evaluation reports of different time periods.

The invention provides a supporting cable production monitoring method and system for 3D visual detection, and relates to the technical field of image processing and intelligent detection, and the method carries out multi-source fusion processing based on collected cable three-dimensional space image data, and comprises coordinate alignment, point cloud reconstruction and multi-scale feature extraction, and construction of a unified space geometric model. On the basis, the center line of the cable is extracted, coordinates of starting and ending points are calculated, and then geometric evaluation parameters such as the axial deviation rate and the straightness are obtained. The method has the advantages of high precision, high robustness and high universality, and is suitable for real-time detection and quality control of slender components such as cables and hoses in the industrial production process. According to the invention, the technical problems of inaccurate three-dimensional structure extraction, insufficient defect identification, difficult defect cause tracing and untimely early warning response in the prior art can be solved.

The invention relates to a defect image generation method and device, equipment and a storage medium, and is applied to the field of artificial intelligence, and the method comprises the steps: obtaining a pure noise image, a defect object image, an original defect image, a defect text description and a template mask image; utilizing a ControlNet model and a diffusion model to generate a defect latent space image expressed by a latent space based on the pure noise image according to the original defect image, the defect text description and the template mask image; according to the defect submerged space image and the defect real object image, defect target features and background features represented by the submerged space are obtained by using the template mask image, and the defect target features and the background features are fused to generate submerged space fusion features represented by the submerged space; and processing the submerged space fusion features by adopting a diffusion model, and decoding to generate a final defect image. The method has the technical effects that the problem of long tail distribution of the industrial defect image is solved, and the quality of the defect image is improved.

The invention provides an image generation method, system and device for a public space and a medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining an image generation demand of a user; the image generation requirement comprises scene information and description content; based on the scene information, matching a corresponding target image generation model and a cue word combination strategy; combining the description content and the cue word combination strategy to construct a cue word text; and inputting the cue word text into the target image generation model to obtain a target space image corresponding to the image generation demand, so as to improve the integrating degree of the generated image and the user demand and the image generation efficiency.

The invention discloses a snapshot type hyperspectral imaging industrial online sorting system which comprises a support assembly, an optical imaging assembly and a motion control assembly. The bracket assembly consists of a bottom plate, and an adjustable bracket and a fixed bracket which are fixed on the bottom plate; the optical imaging assembly comprises a lighting unit, an imaging objective lens and a snapshot type hyperspectral camera, and can synchronously obtain space image information of a measured object and spectral information of a continuous wave band in a single exposure process; the motion control assembly realizes automatic transmission of the measured object through the transmission platform; the data processing module is integrated with an image preprocessing unit, a spectral analysis unit and a spectral database, and is used for end-to-end closed-loop control from original hyperspectral data generation to sorting decision making; the system adopts a metasurface light splitting structure to realize real-time decoding, performs material or category identification by using a pre-trained classification model, and finally automatically generates a sorting decision signal according to an identification result to drive a motion control assembly to complete sorting operation.

The invention discloses a tunnel lining crack identification method and system based on panoramic space image splicing, and relates to the technical field of tunnel visual identification. The method comprises the following steps: acquiring a tunnel lining image array, and performing local splicing on tunnel lining images by using a splicing model based on cylindrical surface projection to obtain a local spliced image; fusing and splicing the local spliced images by using the global image space model to obtain a panoramic spliced data graph; and abnormal region detection is carried out on the panoramic splicing data graph according to the spatial local difference, and a tunnel lining crack identification result is obtained. According to the method, the lining panoramic space model based on image two-stage fusion is used for splicing, so that the visual coherence and naturalness of a global spliced image are improved.

The invention discloses a cross-dataset magnetic resonance multi-mode super-resolution image synthesis method based on deep learning, and relates to the field of magnetic resonance imaging. An unsupervised method in deep learning is utilized, and the architecture of a generative adversarial network is adopted, so that the synthesis of a magnetic resonance multi-mode super-resolution image is realized. Carrying out data preprocessing on the image, wherein the data preprocessing comprises registration, slicing and normalization; constructing a high-frequency template, performing FFT (Fast Fourier Transform) on the data to obtain a K space, multiplying a K space image by the high-frequency template to obtain high-frequency information of the K space, and performing IFFT to obtain high-frequency information of an image domain; the prepared image and the high-frequency information thereof are fed into the network as inputs. And through network training, synthesis of multi-modal data is realized. And constructing an image degradation algorithm of the super-resolution reconstruction network. And constructing a network architecture for super-resolution image reconstruction, and inputting the degraded image and the original real image into a network for training to obtain a high-resolution multi-modal magnetic resonance image.

The invention provides a siRNA gene inhibition efficiency screening model construction and training method, a siRNA gene inhibition efficiency screening device and a siRNA gene inhibition efficiency screening method. The construction and training method comprises the following steps: acquiring first sequence information of target mRNA and second sequence information of siRNA; obtaining first space image information of the target mRNA and second space image information of the siRNA; constructing a siRNA gene inhibition efficiency screening model, and training a first neural network model by using the first sequence information and the second sequence information to extract sequence features; training a second neural network model by using the first spatial image information and the second spatial image information to extract spatial structure features; and fusing the sequence features and the spatial structure features, and training a third neural network model by using the fused features. Therefore, the accuracy of screening the siRNA with the inhibition efficiency on the target gene can be improved.

The invention belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, a self-adaptive filtering module and a density defect detection module, obtaining the maximum peak point of the centralized complex frequency spectrum; constructing a band elimination filter according to the maximum peak point, filtering the centralized complex frequency spectrum through the band elimination filter to obtain a filtered complex frequency spectrum, executing inverse Fourier transform to obtain a filtered space image, and binarizing the filtered space image through a threshold segmentation algorithm to obtain a pore mask; the porosity is obtained according to the ratio of the non-pore pixel points to the total number of the pixel points; and judging the weaving defect of the shielding layer according to the porosity. According to the method, the strong periodic signals of the weaving textures are automatically identified and filtered in the frequency domain, so that non-periodic defects such as pores are effectively highlighted, the technical problem that periodic textures cover defect signals is solved, and the accuracy of defect detection is improved.

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

The invention discloses a space image generation method and system based on visible light and thermal imaging, and relates to the technical field of image generation, and the method comprises the steps: obtaining thermal imaging image data, visible light gray data, depth map data and event camera data as collection data, and carrying out the coordinate system unification and space alignment; patch segmentation is carried out on the collected data, and position codes are added; according to the method, the global attention distribution is calculated by adopting ViT, so that each Patch can pay attention to all other Patches, it is ensured that the characteristics of cross-modal data are not only influenced by local neighborhoods, but the characteristics of the cross-modal data are combined with the information of the whole image, and a comprehensive characteristic matrix is calculated, so that the cross-modal data are obtained. The use of linear transformation can ensure that the final fusion feature comprises global information of ViT calculation, and the spatial attention weight obtained through calculation directly acts on the channel enhancement feature matrix, so that the expression of the spatial feature is further optimized.

The invention discloses a large space positioning fast conversion method based on image recognition, and relates to the technical field of large space positioning, and the method comprises the following steps: large space image collection and deployment, on-site measurement of the size of a large space and the position of an obstacle, introduction of software to construct a basic three-dimensional model, simulation of an initial site of a camera, and calculation of the initial site of the camera. Determining a final layout through evaluation and screening, and performing installation and debugging; according to the invention, through comprehensive utilization of an image identification technology and a multi-camera cooperative positioning mechanism, precision and reliability of target positioning in a large space are substantially improved; through combination of a triangulation principle and a Kalman filtering technology, the position of a target in a three-dimensional space can be effectively deduced reversely, a positioning result is corrected in real time, and positioning jitter caused by image noise and transient shielding of the target is smoothed; the positioning continuity is improved, and the stability and accuracy in a complex environment are enhanced.

The invention provides an underwater multi-color space image enhancement method based on depth information guidance, which is characterized in that a model of the method is based on a training model framework capable of utilizing physical priori knowledge and multi-color information, and comprises a depth information generation module, a multi-color space feature fusion module and a depth enhancement module, the relation between a depth image and an underwater scene is enhanced through attention estimation, scene reconstruction training and fusion of multi-color information, so that insensitivity of a single RGB color space to brightness and saturation image attributes is made up; the feature representation capability is improved, and the scene adaptability of the model is enhanced.

The invention provides a method for real-time analysis of a low-temperature plasma cracking thermodynamic process, which comprises the following steps of: acquiring emission spectrum information in a cracking cavity in the low-temperature plasma cracking process of a carbon fiber precursor by utilizing hyperspectral imaging equipment to obtain a cracking cavity space image containing the emission spectrum information; the spectral characteristics are used for representing the spectral characteristics in the carbon fiber molecular chain breakage process; the method comprises the following steps: monitoring a gray level image of a local region of vortex circulation, calculating curvature characteristics of a vortex form, identifying a geometrical shape and a change rule of a vortex region, determining a region of which the curvature characteristic similarity is higher than a similarity threshold as a vortex region with a self-organization characteristic, and representing the rearrangement and graphitization degree of a carbon fiber molecular chain by the vortex region.

The invention provides a three-dimensional target marking method and device based on a point cloud data-assisted two-dimensional image, and the method comprises the steps: fusing precise three-dimensional point cloud information obtained by a laser radar and two-dimensional image data collected by a camera based on a point cloud data and image data cooperation three-dimensional marking method; and a dynamic high-precision three-dimensional target marking system is constructed. Specifically, a space-image relationship is established through three-dimensional point cloud information and a two-dimensional image plane, and three-dimensional information of a target is mapped into a two-dimensional image, so that geometric and semantic information labeling is carried out on the target in the two-dimensional image, information loss of two-dimensional visual data in dimensions such as depth, attitude and space structure is made up, and the accuracy of the three-dimensional information labeling is improved. And meanwhile, when the three-dimensional point cloud is unmeasurable, a three-dimensional target labeling mode is dynamically adjusted in combination with two-dimensional image labeling information. According to the method, through dynamic high-precision three-dimensional target labeling, the obtained image-label can be used for training and reasoning of a three-dimensional target detection network, and the method has a wide application prospect.

The invention relates to the technical field of counterfeited face detection methods, in particular to a diffusion counterfeited face detection and positioning method based on multi-domain visual language reconstruction. The method comprises the following steps: firstly, carrying out spatial information learning on a diffusion counterfeit face image to respectively extract local and global spatial image features, carrying out prompt learning on a fine-grained text corresponding to the image to extract global language representation, and then reconstructing the image by utilizing the local spatial features to enhance spatial representation and obtain a residual image; the method comprises the following steps of: firstly, acquiring a residual image, learning the residual image to extract a residual feature, then combining the image feature with the residual feature to obtain a visual manipulation feature, reconstructing global language representation to reinforce language representation, and finally combining the language feature with the visual feature to learn multi-modal representation of a diffusion counterfeit face. And utilizing the model to learn visual manipulation features of the diffusion counterfeit face to realize detection and positioning of the diffusion counterfeit face. According to the method, the counterfeit detection and positioning efficiency and the cross-modal detection capability can be improved.

The invention relates to the related field of image processing and computer vision, in particular to a space target image intelligent identification method, which optimizes a series of training strategies, including learning rate dynamic adjustment (such as attenuation and preheating), adaptive optimization algorithm and regularization technology (such as Dropout and data enhancement). And the accuracy, generalization ability and convergence speed of the model are obviously improved. In addition, through a model compression and reasoning optimization technology, efficient reasoning in a resource-constrained environment is realized, and the real-time performance of space image recognition is ensured. The flexible deployment strategy of the model enables the model to adapt to various platforms, including a cloud, a local server and edge equipment, and has good expansibility and practicability. The visualized recognition result is presented, so that the intuition of the recognition result is further improved, and powerful support is provided for subsequent analysis and decision making. The method has wide application prospect and practical value in space image recognition and other related fields.

The application provides a camera module and an electronic device. The camera module comprises a lens, an SMA motor, a substrate and an image sensor. The SMA motor comprises a carrier, a seat and an SMA driving assembly. The lens is fixed to the carrier. The SMA driving assembly connects the carrier and the seat. The SMA driving assembly is used to drive the carrier to move relative to the seat. The shell of the SMA motor comprises a top plate and a bottom plate arranged oppositely. The bottom plate comprises a first part and four second parts. The four second parts are arranged at the outer circumferential side of the first part at intervals. The distance between the second part and the top plate is greater than the distance between the first part and the top plate. The four corners of the substrate are provided with recessed avoiding spaces. The first part is fixed to the substrate. The second part is at least partially located in the avoiding space. The image sensor is fixed to the middle part of the substrate and electrically connected to the substrate. The image sensor is arranged to face the lens. The above-mentioned camera module can have a smaller module shoulder height while ensuring the shooting quality.

[Problem] To provide a show window that has an effect of providing a video seemingly popping out directly from a wall and with which the ability to pull in customers can be expected to be enhanced.　The show window comprises: a window in a wall surface; and an aerial video display device provided to the inside of the window. The aerial video display device projects an aerial video on the outside of the window. The aerial video display device, which is formed from a to-be-projected body that constitutes a basis for the aerial video and a retro-transmissive optical plate that is interposed between the to-be-projected body and the window at a certain angle, is capable of projecting the aerial video through a transparent plate fitted into the window. In one embodiment, a darkening sheet for maximally suppressing reflection and scattering of light is provided to a space on the inside of the window at which the aerial video display device is provided.

A metrology system having an optical measurement system serves to simulate illumination and imaging properties of an optical production system when an object is illuminated and imaged. The optical measurement system has an illumination optical unit serving to illuminate the object and having a pupil stop in the region of an illumination pupil in a pupil plane, and an imaging optical unit for imaging the object in an image plane. At least one pupil stop for specifying a plurality of measurement illumination settings created by displacing the pupil stop in the pupil plane is provided within the scope of the simulation method. Measurement aerial images are recorded in the image plane for various displacement positions of the object perpendicular to the object plane with the various measurement illumination settings. The various measurement illumination settings are specified by displacing the pupil stop. A complex mask transfer function is reconstructed from the recorded measurement aerial images. A 3-D aerial image of the optical production system is determined from the reconstructed mask transfer function and a given illumination setting of the optical production system as the result of the simulation method. The reconstruction includes the fact that profiles of stop edges of the at least one pupil stop which effectively act to specify the respective measurement illumination setting are changed in a manner going beyond a pure displacement of the stop edge when the respective measurement illumination setting is specified on the basis of the displacement position of the pupil stop. This results in an improvement of the simulation method.

The invention discloses a device heating detection method and system based on HSV color space image fusion, and relates to the field of automobile manufacturing, and the method comprises the steps: converting the infrared image and visible light image data of a to-be-heated device into an HSV color space, and carrying out the channel separation; performing weighted fusion on the brightness channels of the infrared and visible light images, converting the fused HSV color space into an RGB image, and performing bilateral filtering on the obtained RGB image to obtain a fused image; and calculating the fused image based on a temperature identification algorithm of the target detection model to obtain real-time temperature information of the device to be heated. According to the invention, the detection efficiency is effectively improved, the image temperature can be accurately identified, and the temperature of the rearview mirror and the rear windshield of the automobile can be accurately sensed.

The invention discloses an information fusion state sensing system for a high-voltage wire inlet cavity of an underground coal mining machine. The information fusion state sensing system comprises an intelligent equipment sensing layer, an edge equipment and model layer, a model and calculation layer and an intelligent service layer. A reserved port of a coal mining machine network communication switch installed in a wall body adjacent to a wire inlet cavity is fully utilized, an anti-electromagnetic interference video and infrared image monitor is installed in the wire inlet cavity, three kinds of partial discharge signals of ultrahigh frequency, ultrasonic waves and transient ground potential are collected at the same time, and the partial discharge signals are acquired through space image and infrared temperature field mode recognition. Through information fusion processing of partial discharge signals and fault hidden danger early warning, state sensing and fault early warning in the harsh anti-explosion environment are achieved for the first time, and a foundation is laid for intelligent sensing and life cycle management of the coal mining machine.

The invention discloses a multi-mode feedback control method and system for a femtosecond laser stone crusher, and relates to the technical field of medical instruments. The method comprises the following steps: constructing spectral component analysis, collecting spectral signals, and executing target chemical component identification; ultrasonic position detection: acquiring an ultrasonic echo signal through a transducer, and calculating a spatial distance; performing vision-assisted positioning, acquiring image data through an endoscope, and generating space image information; according to the spectrum identification result, the distance parameter and the space image information, multi-source data synchronization processing is completed; generating a laser parameter control instruction according to the fusion result; and executing a real-time feedback regulation and control process, obtaining feedback information, and completing control parameter updating and closed-loop response. Through fusion of multi-modal information such as spectrum identification, ultrasonic distance measurement and visual navigation, real-time identification of stone types, intelligent matching of laser parameters and accurate control of spatial positioning are realized, and the accuracy, safety and intuition of lithotripsy operation are remarkably improved.

The invention provides a document image correction method based on deep learning, and belongs to the field of computer vision, and the method comprises the steps: carrying out texture rendering and offset mapping processing based on an original texture grid image in a training set, and generating a spatial image to be rendered; performing data enhancement on the spatial to-be-rendered image through a flat image corresponding to the original texture grid image, and performing random disturbance based on a transformation space to obtain a corresponding sample training pair; based on the sample training pairs in the training set, training the initial detection correction model, optimizing control parameters, and obtaining a detection correction model; detecting a four-point detection model and a document correction model in the correction model, and processing the document image to be corrected in sequence to generate a corrected output image; according to the method, model training is carried out based on the self-constructed sample training pair, the document image correction effect of the model is effectively improved, meanwhile, the method can adapt to a complex real scene, and subsequent detection and recognition of the document image are facilitated.

The invention relates to a washing control method and device, dish washing machine equipment, computer equipment and a medium, and the method comprises the steps that space images of at least two to-be-cleaned areas are obtained, and different to-be-cleaned areas are located on different placement layers; identifying whether the space image comprises a target object or not; when the space image comprises a target object and the smudginess degree of the target object is greater than or equal to a preset degree threshold value, determining a to-be-cleaned area corresponding to the space image as a cleaning area; and a cleaning assembly is controlled to clean the cleaning area. The images of the placement layers of the dish-washing machine equipment are collected, the conditions of the placement objects on the placement layers are automatically recognized, the placement layers needing to be cleaned are automatically divided, the cleaning assembly is controlled to clean the areas needing to be cleaned, layered cleaning control over the dish-washing machine equipment can be automatically achieved, cleaning is efficiently completed, and meanwhile the cleaning efficiency is improved. And hydroelectric resources can be effectively saved.

The invention provides a system and a method for detecting geometric parameters of an optical fiber with a longitudinal periodic structure. The system comprises an optical fiber clamp used for fixing a test part of the optical fiber to be detected in optical fiber refractive index matching oil in a space image imaging device; the light source is used for emitting test light to the collecting lens through the optical fiber bundle, and the test light is focused into a focused light spot through the collecting lens and then is sequentially emitted to the space image imaging device and the detection device; the space image imaging device is used for moving along the axis direction of the to-be-tested optical fiber based on a preset moving speed after the test light passes through a test part in the optical fiber refractive index matching oil so as to form an optical imaging signal corresponding to the test part; and the detection device is used for acquiring geometric parameters of the periodic structure in the to-be-detected optical fiber according to the optical imaging signal. According to the invention, the detection device can accurately obtain the periodic structure geometric parameters of the to-be-detected optical fiber, and the detection precision and reliability are improved.

The invention provides a tunnel convergence deformation data acquisition system and method, and relates to the technical field of optical metrology, laser point cloud data of reflection mark points on the inner wall of a tunnel is acquired, local modeling is performed on the laser point cloud data to obtain a local point cloud subset, and then nonlinear fitting is performed on the local point cloud subset to obtain a local deformation descriptor; determining a registration error according to the local deformation descriptor and the space image information of the inner wall of the tunnel, and performing iterative elimination reconstruction on the laser point cloud data according to the registration error to obtain a local deformation matching model; determining a deformation characteristic reference of the inner wall of the tunnel through local reflection data obtained by performing local laser scanning on the reflection mark points of the inner wall of the tunnel; the local deformation matching model determines a deviation abnormal value of the tunnel inner wall based on the deformation feature reference, convergence deformation data of the tunnel inner wall are obtained through screening of the deviation abnormal value, nonlinear local tiny deformation detection can be achieved, and the sensing capacity of the irregular tunnel inner wall is improved.

Systems and methods for fast reconstruction of motion-resolved magnetic resonance images using space-time-coil convolutional networks are disclosed. The system can receive a plurality of k-space data sets. The system can detect a motion signal therefrom. The system can classify the k-space data sets according to states of the motion signals. The system can resolve the k-space data set to Euclidean space images. The system can resolve the Euclidean space images to a combined Euclidian space image. For example, the system can use a convolutional network that exploits spatial, temporal and coil correlations without k-space data consistency to minimize computation time.

Method, processing unit, and non-transitory computer-readable medium for controlling view of user in immersive environment. Input, in form of an aperture, is received from presenter to control the view. A real-time scene is analyzed to identify viewable objects and spatial image layers are created based on the aperture. Further, by sequentially combining the plurality of spatial image layers, a view-controlled scene is generated for the at least one attendee. The view of the at least one attendee is controlled by replacing the real-time scene with the view-controlled scene in the immersive environment.