38results about How to "Resolve blur" patented technology

The invention relates to the technical field of infrared image recognition, and discloses a hot target extraction method and system based on infrared image recognition, and the system comprises a multi-sensor collection module, a data dimension reduction module, a data verification module, a segmentation optimization module, and a dynamic feedback module. Multi-modal data dimension reduction processing is achieved by collecting infrared images, visible light images and environment dynamic parameters and combining Laplacian feature mapping, and a target area is extracted through geometric verification and topological analysis. An energy optimization model and a distribution alignment method are adopted to complete accurate segmentation of a target area, and meanwhile, segmentation model parameters are adjusted in real time based on environment dynamic parameters, so that the system stability is ensured. According to the method, high-precision and robust extraction of the thermal target is realized in a complex dynamic environment, the problems of fuzzy target boundary, noise interference and insufficient dynamic environment adaptability in the prior art are effectively solved, and the method is suitable for the fields of military reconnaissance, fireproof monitoring, electric power inspection and the like.

The invention provides an underwater multi-view three-dimensional reconstruction method. The underwater multi-view three-dimensional reconstruction method mainly comprises the following steps: S1, acquiring a high-quality multi-view underwater image through a physical perception image enhancement technology PUIE; s2, initializing a neural symbol distance function SDF, and implicitly representing the three-dimensional shape of an object by a neural network; s3, generating a two-dimensional foreground mask by using few-sample multi-view target segmentation, and obtaining three-dimensional geometric information in combination with monocular depth estimation and normal vector prediction to form mixed geometric prior; and S4, in combination with an image enhancement result and mixed geometric priori, through a joint loss function, training a neural network optimization SDF, and completing accurate three-dimensional reconstruction. According to the underwater three-dimensional reconstruction method, a more efficient and more accurate underwater three-dimensional reconstruction solution is provided by introducing mixed geometric prior and optimizing the reconstruction process of the neural symbol distance function SDF.

The invention discloses an earth surface exposed geologic body reality sense three-dimensional reconstruction method based on a neural radiation field. The method comprises the steps that an earth surface geologic body image data set is acquired; performing feature matching on the image data set by using a feature matching strategy based on a nearest neighbor algorithm and a LightGlue neural network to generate feature point matching information and a feature description file; sparse three-dimensional point cloud data and camera pose information are obtained based on incremental SfM; and obtaining depth information of an image data set based on sparse three-dimensional point cloud data and camera pose information, and establishing a geologic body realistic three-dimensional reconstruction model by using a neural radiation field optimization method fusing the depth information and Gaussian position coding. According to the method, the feature matching precision and the camera pose calculation accuracy are improved, the texture detail expression ability of a geological structure can be improved, the reconstruction quality of a surface geologic body model is enhanced, and geological information support can be provided for the fields of geological research and engineering application.

The invention discloses a low-field magnetic resonance image super-resolution network, and relates to the field of magnetic resonance imaging. In order to solve the problem of insufficient resolution of low-field magnetic resonance imaging (MRI) equipment, a multi-level attention mechanism and stable wavelet transform are introduced to carry out image super-resolution. According to a low-field magnetic resonance image, firstly, shallow features are extracted through a shallow feature extraction network, then, deep features of local details are extracted through a U-shaped feature extraction network, feature integration is carried out on the features through a network containing an attention mechanism, up-sampling is carried out through pixel rearrangement, meanwhile, an original image structure is learned through residual error design, and a low-field magnetic resonance image is obtained. And finally, high-resolution output is obtained. According to the method, the definition of the low-field magnetic resonance image is effectively improved, and the problem that a traditional method is fuzzy in a high-frequency area is solved; the method is excellent in image quality indexes such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and efficient image reconstruction can be realized under the condition of relatively low calculation cost.

The invention discloses a voice interaction teaching system integrated with a visual tactile sensor and a braille recognition algorithm, which comprises the steps that a braille image is acquired through a tactile sensing module, an input instruction is acquired through a voice module to carry out mode adjustment on a braille translation module, and the adjustment modes comprise a teaching mode and a translation mode; in the teaching mode, the braille image is preprocessed through the braille translation module to obtain dot information, the dot information is recognized through the braille recognition algorithm to obtain character information, and the character information is broadcasted through the voice module. Whether the braille recognition algorithm is used for detection or not is judged through a frame difference algorithm and an inter-frame minimum circle center distance algorithm when the braille recognition algorithm is used; in the translation mode, the character information is detected through the four columns of threshold lines according to the dot information, the detected character information is obtained, and the detected character information is broadcasted through the voice module.

The invention provides a vehicle driving comfort determination method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the operation information of a user driving a vehicle, and the operation information at least comprises the state value of each state item in a plurality of state items related to the vehicle operation state; for each preset index item, determining at least one target state item corresponding to the index item from the plurality of state items, and determining an index value of the index item according to the state value of each target state item; determining a comfort level corresponding to each index item according to the index value of each index item and a preset index standard of each index item; wherein the index standard of each index item is determined by using historical operation information of the vehicle; and determining the current comfort level of the user driving the vehicle according to the comfort level corresponding to each index item. The method can solve the problems that an existing comfort level evaluation method is low in efficiency, is easily influenced by subjective factors of an evaluator, and is fuzzy in evaluation standard.

The present invention relates to the technical field of power grid fault diagnosis, and specifically to a power grid fault diagnosis method, system, device and storage medium. After preprocessing and synchronous compression wavelet transform processing are performed on collected original fault recording data and fault recording simulation data, fault features are obtained. The fault features are successively subjected to full convolution mask processing and decoding processing and input into a power grid fault diagnosis model for model training to obtain a trained power grid fault diagnosis model. Shallow feature extraction is performed on newly input fault recording data, and then multiple convolution processing is performed to obtain deep features. Then, non-parameter attention processing is performed, and weighted deep features are obtained after fusion processing with the deep features. After downsampling processing, the final weighted deep features obtained are fully connected, and the corresponding fault type labels are output, which can improve the accuracy and efficiency of power grid fault diagnosis.

The invention provides a clothes defect detection method and system based on multi-spectrum characteristic harmonic complex, and relates to the technical field of image detection. The method comprises the following steps: establishing a multi-spectrum characteristic harmonic aggregation complex defect detection model and carrying out pre-training, wherein the multi-spectrum characteristic harmonic aggregation complex defect detection model comprises a spectral domain frequency division network, a spectrum harmonic aggregation network, a boundary spectrum complex network and a target detection output layer; inputting a to-be-detected image in the original clothing image data into the multi-spectrum feature harmonic congelation complex defect detection model, and processing the to-be-detected image by using a spectral domain frequency division network, a spectrum harmonic congelation network and a boundary spectrum complex network in sequence to obtain feature representation with enhanced edge perception capability, and performing target detection and classification on the feature representation through a target detection layer, and outputting a defect detection result of the to-be-detected image. The problems that in the actual production process, the garment flaws are difficult to recognize, and the manual detection efficiency is low are solved, and efficient and accurate detection of the garment flaws under the actual engineering application scene is achieved.

The application relates to the technical field of intelligent transportation and carbon governance, in particular to a passenger car carbon emission monitoring and regulation system and method based on multi-source spatio-temporal data. The application solves the problem of scattered and inconsistent data by preprocessing and uniformly mapping multi-source spatio-temporal data. A three-dimensional carbon emission calculation model of vehicle-space unit-time period is constructed to realize accurate calculation of single-trip carbon emission of a single vehicle and positioning of high-carbon emission hotspots. A clustering algorithm is used to complete differentiated division of user groups. A dynamic early warning threshold is generated based on a warning threshold prediction model, regional carbon emission targets and historical data to reduce false positives and false negatives. A warning type-policy tool-adaptation object mapping rule library is established, and a regulation strategy is generated by combining early warning signals and real-time data to realize automatic and accurate generation of regulation strategies from problem identification. The model parameter, early warning threshold and mapping rule are optimized by using policy implementation effect data to form a monitoring, early warning, regulation, optimization closed-loop management and realize accurate regulation.

The invention discloses a wake detection model construction method based on frequency domain multi-scale perception, which relates to the technical field of ocean observation, is used for capturing weak wake features to carry out sea surface internal wave wake detection, and comprises the steps of constructing a wake image data set, constructing a wake detection model by taking an RT-DETR model as a baseline model, and constructing a multi-scale texture information selection network. A backbone network of the baseline model is replaced, a frequency domain multi-scale attention perception module is constructed, an AIFI module of the baseline model is replaced, a golden cudgel convolution down-sampling module is adopted to replace a standard convolution down-sampling module of the baseline model, training and optimization are performed on the wake detection model based on the wake image data set, and a target detection result is obtained. According to the method, the wake detection model AUVRT-DETR is constructed, the detection precision is improved while the advantage of light weight is kept, the optimal balance is achieved between the detection precision and the calculation efficiency, and the model parameter quantity and the calculation overhead are remarkably reduced while the highest detection precision is achieved.

The invention relates to a method and system for reconstructing a hyperspectral image based on an RGB image, and the method comprises the steps: S1, selecting an RGB image in a space; s2, processing the RGB image to obtain end member information of the RGB image; s3, generating end member spectral representation based on the end member information of the RGB image; s4, encoding, decoding and attention modeling are carried out on the RGB image, and abundance information of the RGB image is obtained; and S5, constructing a rough hyperspectral image based on the end member information of the RGB image and the abundance information of the RGB image, performing feature enhancement on the rough hyperspectral image and the RGB image, and performing fusion to obtain a final hyperspectral image. According to the invention, the hyperspectral image can be effectively constructed based on the RGB image.

The invention discloses an endoscope image processing method, system and device and a medium, which are used for holmium laser lithotripsy, and the method comprises the following steps: when an endoscope works in a holmium laser mode and a holmium laser probe stretches out, controlling the endoscope to carry out gain adjustment; and obtaining a target endoscope image based on the endoscope after gain adjustment. According to the invention, the endoscope gain can be automatically adjusted in the holmium laser mode, the problem of image blurring caused by overlong extension of the holmium laser probe in the holmium laser mode is effectively improved, the image quality in the holmium laser mode is effectively improved, and a doctor can be better assisted in holmium laser lithotripsy.

The invention discloses a motor quality inspection method and computer equipment, and relates to the field of industrial automation and precision manufacturing, and the method comprises the steps: determining a plurality of part actual shapes and a plurality of surface actual coatings of a target motor according to image data; comparing the actual shapes of the plurality of parts with standard shapes of the parts corresponding to the plurality of target motor parts to obtain first quality inspection results of the actual shapes of the plurality of parts; component color features and component glossiness features are determined according to the multiple surface actual coatings; comparing the component color feature and the component glossiness feature with a component standard color and a component standard glossiness in the surface standard coating respectively to obtain a plurality of second quality inspection results of the surface actual coating; and when the first quality inspection result and the second quality inspection result meet the preset target motor quality standard, returning a quality inspection qualification result. By implementing the method, personalized quality inspection of different motor types is realized through image analysis, and the accuracy and reliability of quality inspection are improved.

The embodiment discloses an automatic potential inspection system for a subsea pipeline, which is characterized in that a magnetic detector and a cesium optical pump are arranged on an underwater load platform, position signals between the subsea pipeline and the underwater load platform are respectively acquired, and an automatic inspection path signal of the underwater load platform is acquired based on the signals acquired by the magnetic detector and the cesium optical pump; the problem that in the prior art, image information is fuzzy when automatic routing inspection path signals are obtained depending on images is solved, the problems that the pipeline detection capacity and precision are limited by independently adopting magnetic detection equipment are solved, the obtained automatic routing inspection path signals are high in precision, and meanwhile the potential collection precision is not affected.

The invention relates to the technical field of fuel cell production, in particular to a fuel cell bipolar plate adhesive tape segmentation method, system, medium and equipment based on gradient attention and a multi-scale void residual structure, a MobileNetV3-U-Net fusion architecture is adopted, lightweight MobileNetV3 is used as an encoder, high-level semantic features of an image are extracted step by step through a multi-layer inverse residual block, and the high-level semantic features of the image are extracted through a multi-scale void residual block. Meanwhile, a DG Attention module is embedded in the bottommost layer of the encoding stage, the DG Attention module guides attention weight calculation by extracting gradient features and long-strip-shaped features and strengthens feature capture of the edge and the slender structure of the adhesive tape, a decoder reserves multi-layer jump connection of U-Net, meanwhile, a traditional convolution operation is replaced with a Res2DilatedConv module, and therefore the decoding efficiency is improved. Multi-scale feature extraction is realized through channel grouping, multi-voidage convolution and cross-group feature fusion, the structural difference of the adhesive tape under different widths and bending degrees is adapted, and efficient and accurate segmentation of the adhesive tape region in the fuel cell bipolar plate is realized.

This invention provides an intelligent inspection method, system, equipment, and medium for railway freight inspection, belonging to the field of railway freight inspection technology. The method includes: acquiring images of the freight cars using a line-scan camera array on both sides and the top of the track; performing spatiotemporal synchronization and adaptive parameter adjustment based on an encoder and a precise clock protocol; performing geometric correction, flat-field correction, motion compensation, and stitching on the images to generate a two-dimensional unfolded image of the freight car; using a deep learning-based freight car segmentation model to segment and number freight car instances, and extracting images of individual freight cars; finally, using a damage detection model integrating severity assessment to identify damage, outputting a structured result including damage category, location, confidence level, and severity score. This invention achieves non-contact, full-coverage, and high-precision intelligent inspection of freight cars, effectively improving inspection efficiency and automation levels.

The embodiment of the invention discloses a signal processing method and device and electronic equipment, and the method comprises the steps: carrying out the optical signal collection and conversion processing of a first position corresponding to a pixel group through a first pixel unit in a pixel array in a first exposure period, and obtaining M first signals; wherein the pixel array comprises the pixel groups in i rows and j columns, and each pixel group comprises one first pixel unit and one second pixel unit; m, i and j are respectively integers greater than 1; determining a second signal corresponding to the first position through a second pixel unit in the pixel group; and carrying out fusion processing on the M first signals and the second signals to obtain M image signals corresponding to the positions.

The invention discloses an image processing method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: controlling a merging mode of four Bayer array image sensors based on an image amplification instruction of a user, so as to generate an original output image, carrying out the preset operation of the original output image, obtaining initial Bayer grating data corresponding to the original output image; performing a first reconstruction operation on the initial Bayer grid data to obtain first intermediate grid data, and performing a second reconstruction operation on the initial Bayer grid data to obtain second intermediate grid data; and integrating the first intermediate raster data and the second intermediate raster data into target raster data, and generating a target output image based on the target raster data. According to the technical scheme provided by the invention, the problem of image blurring in the digital zooming process can be solved.

The invention relates to the field of polymerized rosin, in particular to zinc-free polymerized rosin and a preparation method thereof. Zinc-free polymerized rosin comprises the following raw materials: masson pine, inorganic acid, zinc chloride, a solvent, oxaloacetic acid and phosphotungstic acid. The inorganic acid accounts for 0.6-1% of the mass of the masson pine, the zinc chloride accounts for 2.5-4.5% of the mass of the masson pine, the solvent accounts for 75-85% of the mass of the masson pine, the oxaloacetic acid accounts for 0.04-0.08% of the mass of the masson pine, and the phosphotungstic acid accounts for 0.04-0.08% of the mass of the masson pine. According to the zinc-free polymerized rosin provided by the invention, the high yield of a product can be kept, zinc residues can be thoroughly removed, the overseas technical monopoly can be broken, the overall competitiveness of the domestic ink industry is improved, and a strength is contributed to the healthy development of national economy.

This invention discloses a method for constructing a disease knowledge graph of the musk deer based on the GPLinker model, belonging to the field of natural language processing technology. The method includes: inputting the labeled disease corpus into the GPLinker joint extraction model; an encoding module extracting multi-scale semantic features from the disease corpus; a boundary awareness module obtaining the probability distribution of the boundary label corresponding to each word segment based on the multi-scale semantic features, obtaining the sum of the probabilities of the first label and the second label based on the probability distribution, and obtaining a gating weight signal; fusing the gating weight signal and the multi-scale semantic features to obtain enhanced features; a GPLinker module obtaining the target entity and the target relationship between the target entities based on the enhanced features, obtaining triples based on the target entities and entity relationships, and obtaining the musk deer disease knowledge graph based on the triples. This solves the problem that existing musk deer disease knowledge graph construction methods cannot accurately identify entity boundaries, resulting in low knowledge extraction quality.

The application discloses a dynamic power quality disturbance signal detection method, device and medium, and the method steps comprise the following steps: S01, acquiring a dynamic power quality disturbance signal to be detected; S02, dividing a signal frequency band, and performing time-frequency analysis on the dynamic power quality disturbance signal by using an improved variable resolution S transform method; the improved variable resolution S transform method is an S transform based on a Gaussian window, and a Gaussian window scale factor is used to optimize the Gaussian window function; S03, performing a synchronous squeezing rearrangement operation on the improved variable resolution S transform result, so that energy components scattered in adjacent frequency intervals are aggregated to real frequency axis positions, and a time-frequency matrix after synchronous squeezing rearrangement is obtained; and S04, detecting signal components of the time-frequency matrix after synchronous squeezing rearrangement. The application can reduce energy diffusion and realize high-precision detection of non-stationary multi-scale complex dynamic power quality disturbance signals.

The invention relates to the technical field of image enhancement, in particular to an image enhancement generation method based on face key point self-adaptive deformation. Comprising the steps of receiving a popular science museum face image data stream, performing topology reconstruction through a three-dimensional parameterized model, outputting a source face grid, mapping the source face grid to a UV parameter space, and decoupling and outputting an albedo texture tensor and a geometric texture tensor; calculating vertex displacement through a preset topology corresponding relation, constructing a differential homeomorphic deformation field, and constructing a continuous manifold coordinate field through nonlinear space adaptation; constructing an implicit neural texture representation network, performing overfitting training, inputting coordinate points of the continuous manifold coordinate field into the implicit neural texture representation network point by point, performing frequency modulation, calculating texture color values, and generating an enhanced facial image. According to the method, the continuous manifold coordinate field, the implicit neural texture representation network and the frequency modulation mechanism are deeply coupled, so that reconstruction from a low-resolution feature to a high-resolution visual signal is realized.

This invention belongs to the field of laser micromachining and relates to a coaxial monitoring system for ultrafast laser micromachining. The system includes an ultrafast laser, a beam-splitting plate, a laser focusing lens, an intermediate lens group, a filter, a fixed-magnification telecentric lens, and a high dynamic range camera. The ultrafast laser emits ultrafast laser light. The beam-splitting plate and the laser focusing lens are arranged sequentially from front to back along the optical path of the ultrafast laser. The laser focusing lens focuses the ultrafast laser light onto the workpiece, generating visible monitoring light on the workpiece. The visible monitoring light passes through the laser focusing lens and is reflected by the beam-splitting plate to form reflected visible monitoring light. The intermediate lens group, filter, fixed-magnification telecentric lens, and high dynamic range camera are arranged sequentially from front to back along the optical path of the reflected visible monitoring light. This invention provides a coaxial monitoring system for ultrafast laser micromachining that improves imaging clarity and resolution.

The utility model belongs to the technical field of cable insulation wire core pair twisting, and specifically discloses a novel high-speed pair twister light-weight wire passing guide wheel comprising a weight reduction guide wheel, a miniature bearing, an alloy support, a twisting bow and a rivet, the weight reduction guide wheel is arranged at the inner side of the alloy support, the miniature bearing is inserted in the alloy support and the weight reduction guide wheel, and the rivet is arranged at the inner side of the alloy support. The miniature bearing and the weight reduction guide wheel are concentrically arranged, the miniature bearing and the weight reduction guide wheel are rotationally connected, a wire passing groove is formed in the outer side wall of the weight reduction guide wheel, and the cross section of the wire passing groove is of a U-shaped structure; the stranding bow is arranged above the alloy support, the rivet is located above the stranding bow, penetrates through the stranding bow and is inserted into the alloy support to fix the stranding bow, the rivet is in threaded connection with the alloy support, a groove is formed in the side, close to the weight reduction guide wheel, of the stranding bow, and the groove design adopted by the stranding bow and the U-shaped wire passing groove design of the weight reduction guide wheel form an approximately-circular space. And the insulation wire core is ensured to be smooth in a high-speed pair twisting process.

The present invention relates to the technical field of printed circuit board manufacturing, and specifically to a multiple averaging prediction alignment method and system of an exposure machine, comprising the following steps: S1: acquiring multiple image data under different exposure conditions through an exposure machine; S2: performing image correction on the identified blurred area through an inverse motion compensation algorithm; S3: calculating the superposition error between each exposure image, and making a preliminary correction to the error; S4: dynamically adjusting the optical parameters of the exposure machine using an adaptive optical operator; S5: iteratively fusing the corrected image, and performing multiple iterative averaging processes; S6: transmitting the final alignment image data obtained in S5 to the control process of the exposure machine. The present invention significantly improves the image alignment accuracy and clarity during the PCB exposure process by integrating motion data analysis, inverse motion compensation, multiple averaging prediction alignment algorithms, and optical parameter adaptive adjustment technology, thereby ensuring high-quality PCB manufacturing.

The invention discloses a sonar image super-resolution method and system based on a diffusion generation model, and belongs to the technical field of underwater image processing. The method comprises the following steps: generating an initial diffusion state based on an input image and Gaussian noise obeying standard normal distribution; inputting the current diffusion state and the current iteration time step into a diffusion generation model to obtain total noise contained in the image in the current diffusion state; combining the current diffusion state, separating the total noise into structural noise and random noise, and fusing the structural noise and the random noise to obtain optimized noise; calculating a preliminary estimation image corresponding to the current iteration time step based on the optimized noise and the current diffusion state; sampling noise obeying standard normal distribution is generated, and the diffusion state of the next iteration time step is generated based on the preliminary estimation image, the optimization noise and the sampling noise; and obtaining a super-resolution image until the inverse diffusion iteration process is finished. According to the invention, a high-resolution image which is high in fidelity, globally consistent in signal-to-noise ratio and adaptively deployed can be obtained.

The present invention provides a front end structure, an insertion part and an endoscope of an endoscope, and relates to the technical field of medical devices. The front end structure includes a front end shell, whose distal end surface is provided with a mounting hole; a camera module, which is installed in the mounting hole, and a receiving gap is formed between the camera module installed in place and the distal opening of the mounting hole; a transparent optical disc, which is movably arranged in the receiving gap, and the transparent optical disc has a rotating part, and the rotating part is evenly distributed circumferentially on the proximal surface of the transparent optical disc; a water spraying part, including a spraying part and a circulation part, the spraying part is arranged in the front end shell, and the spraying part has a spraying port facing the distal end surface of the transparent optical disc; the circulation part is arranged in the front end shell and communicated with the spraying part, and is used for the flushing liquid to enter from the proximal end of the endoscope and flow into the spraying part, so that the flushing liquid is sprayed to the distal end surface of the transparent optical disc through the spraying port. Compared with the prior art, the present invention can solve the problem of blurred imaging of the camera module.

The long-distance synchronous acquisition method and system of multiple normal and dynamic visual sensors provided by the embodiments of the present application comprise multiple normal visual sensors, multiple dynamic visual sensors and a control processor. The method is applied to the control processor and comprises the following steps: determining a target normal visual sensor and determining a target dynamic visual sensor; acquiring a first synchronization signal of the target normal visual sensor and sending the first synchronization signal to the remaining normal visual sensors after first delay compensation processing; sending the first synchronization signal to each dynamic visual sensor after second delay compensation processing; and sending a second synchronization signal emitted by the target dynamic visual sensor to the remaining dynamic visual sensors after third delay compensation processing, so as to perform data synchronous acquisition and improve the accuracy and synchronism of image data acquisition of multiple visual sensors.

The embodiment of the present application provides an image denoising method, apparatus, device and storage medium, wherein the image to be processed is input into an image denoising model for denoising, and an initial denoised image corresponding to the image to be processed output by the image denoising model is obtained; the image to be processed is input into an annotation model for pixel annotation, and pixel information corresponding to the image to be processed output by the annotation model is obtained; based on the pixel information and the initial denoised image, a target denoised image corresponding to the image to be processed is obtained. Image denoising is achieved by combining an image denoising model and a pixel-level annotation model, which can effectively solve the problem of local image blur while ensuring the overall image denoising effect, and provides the possibility for denoising image edge information and restoring image details.